Web3, IoT/Edge, AI, HPC, Blockchain, Cryptocurrencies, GPUs and Quantum, Cyber Risk, 5G, and BioTech point to opportunities and threats. Why are there so many big technology trends right now? Doug and Shahin discuss a framework to help make sense of why these trends point to important changes, how these trends are related, and what they mean individually and together.
The OrionX editorial team manages the content on this website.
Here is my perspective after several discussions and some side research:
Needless to say, battling digital mistrust and deepfakes is already a very significant problem. As I like to say, trust is expensive, cherish it if you have it!
Shahin is a technology analyst and an active CxO, board member, and advisor. He serves on the board of directors of Wizmo (SaaS) and Massively Parallel Technologies (code modernization) and is an advisor to CollabWorks (future of work). He is co-host of the popular RadioFreeHPC (“Car Talk” of supercomputing), OrionX Download, and Afero IoT podcasts.
No, not asking if you own any Bitcoin. Or the IP address.
This blog is prompted by the Nicholas Weaver article “Risks of Cryptocurrencies” in the June 2018 Communications of the ACM.
He writes, rather misleadingly in our opinion:
“This was not because our Bitcoin was stolen from a honeypot, rather the graduate student who created the wallet maintained a copy and his account was compromised. If security experts can’t safely keep cryptocurrencies on an Internet-connected computer, nobody can. If Bitcoin is the ‘Internet of money’, what does it say that it cannot be safely stored on an Internet connected computer?”
Would you leave a gold coin lying around in the open? Lock that thing up in a safe or safety deposit box.
Bitcoin is not really the ‘Internet of Money’ so much as ‘Money in the Internet’. And the cryptocurrency was not on an Internet-connected computer. Those were the keys.
Your wallet holds one or more private keys, not cryptocurrency itself.
Key distinction (pun intended). The money doesn’t move off the distributed ledger. When it moves from one wallet to another what happens is the send process (that you initiate) changes which private key can access it. Namely the designated receiver’s key becomes the only one that works.
The graduate student’s indiscretion was in making a copy of the key that allowed the safe or safety deposit box to be opened by an unauthorized person. And then not properly securing it.
Where is the Bitcoin stored? Why in the distributed ledger, the blockchain, that is simultaneously existing in many places, but has a single verified history from the Nakamoto consensus protocol that committed it into the blockchain.
That is effectively the bank where all the safety deposit boxes are.
How do you get to your coin? With a key stored in a wallet, the private key. Visit your bank.
That key must be stored in a safe place. It can be in a hardware wallet (USB device typically) which is stored in a home safe. And then it has the same level of security as the gold coins in your safe.
Better, since you can keep another copy in another secure location (safety deposit box, for example).
The next best alternative is a pass phrase on a piece of paper again stored in a safe or safety deposit box. Or a paper wallet that can use a QR code.
There is no need for your private key to be sitting on the Internet.
If you use an exchange you can use their vault, or cold storage, option for most of your holdings. Then you are relying on their assurances that they are storing in offline devices.
When you do visit your Money in the Internet bank, do so from the privacy of your home, not from some insecure wifi in a cafe.
You go to the bank and take some gold coins out from your box and they are already less secure, but that is why they have guards at banks. And when you go out to your car with a couple of the coins, they and you are even less secure.
But we are used to doing that. We understand the procedures.
It’s just that there are new procedures that we have to get used to, with digital gold like Bitcoin. It’s rare to be physically mugged for Bitcoin.
Keep only moderate amounts of cryptocurrencies in exchanges with established security reputations, and modest amounts in mobile wallets.
Stephen Perrenod has lived and worked in Asia, the US, and Europe and possesses business experience across all major geographies in the Asia-Pacific region. He specializes in corporate strategy for market expansion, and cryptocurrency/blockchain on a deep foundation of high performance computing (HPC), cloud computing and big data. He is a prolific blogger and author of a book on cosmology.
National Security considerations are often intimately intertwined with the adoption of new technologies.
Last year Tokyo hosted a meeting of the International Standards Organization, including a session on blockchain technology to examine ideas around standards for blockchain and distributed ledgers.
A member of the Russian delegation, who is part of their intelligence apparatus at the FSB, apparently said “the Internet belonged to the US, the Blockchain will belong to Russia.” In fact three of the four Russian delegates were FSB agents! By contrast, Chinese attendees were from the Finance Ministry, and American attendees were representing major technology companies, reportedly IBM and Microsoft among others.
Let’s unpack this a bit. The Internet grew out of a US military funded program, Arpanet, and the US has been the dominant player in Internet technology due to the strength of its research community and its technology companies in particular.
As we wrote in our most recent blog (https://orionx.net/2018/05/is-blockchain-the-key-to-web-3-0/), blockchain has the potential to significantly impact the Internet’s development, as a key Web 3.0 technology.
Blockchain and the first cryptocurrency, Bitcoin, were developed by an unknown person or persons, with pen name Satoshi Nakamoto. Based on email timestamps, the location may have been New York or London, so American or British citizenship for Bitcoin’s inventor seem likely, but that is speculation.
More to the point, the US is the center of blockchain funding and development activity, while China in particular has been playing a major role in mining and cryptocurrency development.
There are many Russian and Eastern European developers and ICO promoters in the community as well. The Baltic nations bordering Russia and the Russian diaspora community have been particularly active.
The second most valuable cryptocurrency after Bitcoin is Ethereum, which was invented by a Russian-Canadian, Vitalik Buterin. Buterin famously met with Russian President Vladimir Butin in 2017. Putin is himself of course a former intelligence agent.
The Russians reportedly want to influence the cryptographic standards around blockchain. This immediately raises fears of a backdoor accessible to Russian intelligence. Russia is also considering the idea of a cryptocurrency as a way to get around sanctions imposed by the American and European governments.
The Russian government has a number of blockchain projects. The government-run Sberbank had initial implementation of a document storage blockchain late last year. There is draft regulation around cryptocurrency working its way through the Russian parliament. President Putin has said that Russia cannot afford to fall behind in blockchain technology.
Given the broad array of applications being developed for cryptocurrencies, including money transfer, asset registration, identity, voting, data security, and supply chain management among others, national governments have critical interests in the technology.
China has been cracking down on ICOs and mining, but it is clear they think blockchain is important and they want to be in control. Most of their government concerns and interest appear to be centered around the potential in finance, such as examining the possibility of a national cryptocurrency (cryptoYuan).
China would like to wriggle free from the dollar standard that dominates trade and their currency reserves. They have joined the SDR (foreign reserve assets of the IMF) and have been building their stocks of gold as two alternatives to the dollar.
China’s biggest international initiative is around a new ‘Silk Road’, the One Belt, One Road initiative for infrastructure development across EurAsia and into the Middle East and Africa. One could imagine a trading currency in conjunction with this, a “SilkRoadCoin”. In fact, the government-run Belt and Development Center has just announced an agreement with Matrix AI as blockchain partner. Matrix AI is developing a blockchain that will support AI-based consensus mechanisms and intelligent contracts.
China’s One Belt One Road Initiative actually has six land corridors and a maritime corridor.
(Image credit: CC 4.0, author: Lommes)
The American military is taking interest in blockchain technology. DARPA believes that blockchain may be useful as a cybersecurity shield. The US Navy has a manufacturing related application around the concept of Digital Thread for secure registration of data across the supply chain.
In fact the latest National Defense Authorization Act requires the Pentagon to assess the potential of blockchain for military deployment and to report to Congress their findings, beginning this month for an initial report.
What is clear, is that blockchain and distributed ledger technology have the potential to be of major significance in national security and development for the world’s leading nations.
A range of efforts are underway by government, industry, and academia to understand blockchain technology and cryptocurrencies, to enhance the technology, and plan for the future. In that context, we see potential in the technology to impact many facets of society and global dynamics. That potential is now sufficiently developed for us to advocate a more visible presence by government agencies in helping shape the policy and academic research.
We encourage the US government to increase engagement with blockchain and distributed ledger technology. This can include funding research in universities, pilot projects with industry across various government agencies including the military and intelligence communities, the Federal Reserve, and the Department of Energy, NOAA and NASA, in particular.
Also the federal government should pursue standards development under the auspices of the NIST and together with ISO. Individual state governments are also promising laboratories for projects around identity, voting, and title registration.
Information has always been key to warfare. But there is little doubt that warfare is increasingly moving toward a battlefield within the information sphere itself. These are wars directed against the civilian population; these are wars for peoples’ minds. Blockchain technologies could play a significant role in these present and future battles, both defensively and offensively.
America was founded and grew rapidly largely in the context of the Industrial Revolution. The Information Age provides a similar opportunity and responsibility to set the course for the next century and beyond. As before, getting it right will not only assure the country’s continued success and leadership. It also arms the nation to solve problems that affect all of humanity.
References :
DARPA https://www.google.co.th/amp/s/cointelegraph.com/news/pentagon-thinks-blockchain-technology-can-be-used-as-cybersecurity-shield/amp
US Navy http://www.secnav.navy.mil/innovation/Pages/2017/06/BlockChain.aspx
2018 National Defense Authorization Act https://www.realcleardefense.com/articles/2018/05/03/could_americas_cyber_competitors_use_blockchain_for_their_defense_113400.html
NIST https://csrc.nist.gov/publications/detail/nistir/8202/draft
http://www.scmp.com/business/china-business/article/2136188/beijing-signals-it-wants-become-front-runner-blockchain
https://www.cbinsights.com/research/future-of-information-warfare/
Stephen Perrenod has lived and worked in Asia, the US, and Europe and possesses business experience across all major geographies in the Asia-Pacific region. He specializes in corporate strategy for market expansion, and cryptocurrency/blockchain on a deep foundation of high performance computing (HPC), cloud computing and big data. He is a prolific blogger and author of a book on cosmology.
Why are cryptocurrencies like Bitcoin and Ethereum Money 3.0? And what are Money 1.0 and Money 2.0?
Recently, Jamie Dimon called Bitcoin a ‘fraud’. This coming from the CEO of JP Morgan, the bank that has been fined more than any other, save one, for financial crimes since the Great Recession of 2008. His statement reeks of hypocrisy since JPM is a member of the Enterprise Ethereum Alliance, his traders have been trading Bitcoin related ETN securities, and his firm has applied for patents using blockchain technology.
By the way the Enterprise Ethereum Alliance has well over 100 members including Microsoft and Mastercard. Serious players understand that cryptocurrencies are a big deal. The market cap of all cryptocurrencies is currently in the neighborhood of $150 billion, around 2/3 the market cap of Visa. And this has all happened in only eight years’ time.
It is amazing how few people can give a definition, other than pulling out a bill from their wallet, or referring to the numbers in their checking account statement. And how does money get created in our modern economy? Very few actually understand the process. Most people say government creates it. Governments can, and do, but most money is not created directly by the government. What the government does is validate money, they define a single type of money for their nation. They print currency, but most money today is digital, residing in bank balance sheets, and most money creation occurs as banks issue new loans.
Throughout history there have been many forms of money, but two forms have dominated. The first form, Money 1.0, was the dominant form for millennia. It was coins made of precious metals, in particular gold and silver, and ‘base’ metals such as copper and bronze. According to the St. Louis Federal Reserve, money must have six properties: durability, portability, divisibility, uniformity, limited supply, and acceptability.
They sound a bit like goldbugs when they write it that way. These are all useful attributes of the thing that is used for money, be it gold or paper. But it doesn’t quite get to the most essential three properties of money. It must serve as:
Money is whatever can be used as a socially agreed upon unit of account and medium of exchange. It also should retain its value, not depreciate quickly, so that it can be used next month and next year as well. Notice I say socially. Societies agree upon what is used as money, and nation states in recent centuries have taken the lead in that definition. In order to be conveniently exchanged, then the six properties above come into play. Durability and limited supply allow the retention of value. Portability and divisibility make it easier to exchange. Uniformity makes it a useful unit of account, as does acceptability.
We all have to more or less agree on what the accounting unit is. That is actually the starting point for money, agreeing on the standard measure. The government can decree the accounting unit, and can demand taxes be paid in that unit. That is government fiat, and can apply for either coined money of precious or base metals (Money 1.0) or paper money (Money 2.0).
Image: Roman gold Solidus coin. York Museums Trust. CC-BY-SA 4.0
The US dollar was originally defined to contain a certain weight of silver, and aligned to the Spanish dollar (originally Austrian thaler) or ‘pieces of eight’ that was widely used in New World trade. The US dollar has also been defined against gold, with an official act in 1900 following nearly 3 decades of defacto gold standard following the Civil War. Of course the gold standard is now entirely gone after being discarded in two phases, under Roosevelt in 1933 and Nixon in 1971. The remnants of the bimetallic standard of the late 19th century remain in present-day dimes and quarters that used to contain silver even until 1965, retain the color, but have been entirely debased.
No nation remains on a Money 1.0 standard of precious metals, all have moved to Money 2.0, fiat paper money. If they did they would lose their gold, and they prefer to melt it into bars and store it in central bank vaults as a reserve. So as Warren Buffet says, we dig it up in mines, melt it down into bars, and bury it again in vaults.
With paper money, there must be fiat, as nobody wants pieces of paper that have no value. The days of gold certificates and silver certificates as circulating currency are long gone, although I remember silver certificate dollar bills from my youth. The value comes from the legal tender requirements that the paper be accepted by businesses, be used for taxes, and from the government’s printing process to make counterfeiting difficult plus the government’s overall management of the money supply (usually through interest rate policies) to limit loss of value due to inflation.
The technology of high quality paper engraving, augmented with serial numbers, threads and holograms, and the technology of central banks, allow fiat money to work. The vast majority of nations have central banks to lend to the commercial banks in times of crisis and to manage the banking system and money supply indirectly.
So those are Money 1.0 and Money 2.0. In summary:
Money 1.0 – Public or private, asset-based, intrinsic value, coins or bars of precious metal
Image of $2 Federal Reserve Note, Bureau of Engraving and Printing, U.S. Dept. of Treasury
Money 2.0 – Public and sovereign, debt-based, no intrinsic value, paper and digital.
Most Money 2.0 is digital, with the circulating currency representing a small percentage. Money mostly comes into circulation not through the printing press, but when banks make new loans. If a bank creates an auto loan, it credits the checking account of its customer digitally. Banks are allowed to make new loans within the limits of their central bank authority determined reserves and equity capital requirements.
Note as an aside that Money 1.0 and Money 2.0 can coexist. We mostly have Money 2.0 in the United States, but there was a small amount of silver coinage money circulating alongside up until the 1960s. This is an important principle, since we are beginning to see the coexistence of Money 2.0 and Money 3.0.
Cryptocurrencies are purely digital, whereas Money 2.0, fiat and debt-based money, is mostly digital.
Why Money 3.0? Technologists and advocates of non-fiat money were concerned about the risks of centralized monetary systems dominated by central banks and by money center banks engaged in fraudulent activities around mortgages and other lending with derivatives including CDOs, CDSs and more. The corrupt system lead to the Great Recession of 2008. Everyone in the society suffered, but the banks were bailed out by enormous government loans.
There were more than 50 attempts at creating a digital crypotcurrency prior to the year 2000. None succeeded. One was gold-based and known as e-gold. It was shut down in 2009 by the US government, because it ran afoul of stricter money laundering regulations. It was also subject to repeated thefts of accounts from Russian and other criminal hackers.
A successful non-fiat cryptocurrency must provide a single secure ledger of entries to protect against counterfeiting and double spending. It must have a method of commiting a single instance of a transaction to this secure ledger that is publicly shared, and is known as the blockchain. It must have a built-in automated “central banking” function that determines the money supply.
Satoshi Nakamoto’s brilliance was to combine a number of existing ideas around public/private key cryptography, distributed ledgers, and a mining algorithm with “proof of work” that rewarded miners for solving a difficult cryptographic hash problem. Transactions are signed with private keys. All bitcoins reside in the distributed ledger. The owner has a wallet with the key that allows them to transfer bitcoin in arbitrary amounts to someone else and thus confers ownership.
The supply is limited with a maximum at 21 million bitcoins that will not be reached until well into the 22nd century. New bitcoin comes into existence in conjunction with the mining of blocks of transactions. The successful miner is rewarded with an allocation of new coins, presently 12.5 coins per block of approximately 2000 transactions. So here we have the central banking function and a digital minting or mining process for the ‘coins’ which are really just ledger entries.
We describe this Nakamoto consensus algorithm and the mining process in more detail at orionx.net/podcast.
Now we have not just Bitcoin, but Ethereum, Bitcoin Cash (which is a recent fork of Bitcoin with large block size), Ripple, Litecoin and hundreds more cryptocurrencies. We have new coins being created rapidly in conjunction with new applications and ICOs – initial coin offerings.
The largest of these, those with market caps in the billions of dollars, meet the three requirements for money. Unit of account. Medium of Exchange. Store of value. Their limitations at present relate to the latter two attributes. They are accepted as medium of exchange in some environments, but relatively few compared to existing fiat currencies. And as a result of that their value is less stable and determined more by investment and speculative demand. Their ultimate value will be determined by the cryptocurrency economy as uses cases, applications, and acceptance grow.
They are child currencies, developing and growing, but far from the maturity of an existing national fiat currency. The value should continue to grow for the long term, however since transaction volumes are increasing very rapidly.
So now we have in the world:
Money 3.0 – Private and globally distributed, asset-based, digital only.
Money 3.0 holds much promise. It can remove a lot of cost and friction from the financial system. Trying sending a check or ACH transfer to your sister and having the transaction complete on the weekend. Send her some bitcoin? She will get it even on Sunday at 3 am around an hour or so after you send it. Bitcoin is 24 by 7 by 365. And with very limited fees within the Bitcoin economy. Most of the cost is in moving Bitcoin to fiat or vice versa.
It is not based on debt, so does not have the instability of debt or counterparty risk. The only real risk is security, which holds as well for your banking balances. The other risk is to the value as governments and politicians feel threatened. But at the end of the day, they can only regulate it, but not eliminate it. The technology is too widely available to anyone.
Money 3.0 is not poised to replace Money 2.0 anytime soon, although in a number of ways it is superior. They will coexist. At some point a small country will convert their currency to Money 3.0, by building a blockchain-based peso or some such. A number of central banks, large and small, are already studying this issue.
Many have talked about global currencies in the past. The US dollar has global impact for trade and the price of key commodities, but you have to exchange it when you cross borders. The Euro has been a boon for commerce, trade, and travel in many countries within Europe. Gold historically had a global role but was difficult to move and verify as to weight and purity.
Bitcoin has no weight and purity issues. It transcends borders. It, Ether, and the other cryptocurrencies are indeed the first global currencies.
Stephen Perrenod has lived and worked in Asia, the US, and Europe and possesses business experience across all major geographies in the Asia-Pacific region. He specializes in corporate strategy for market expansion, and cryptocurrency/blockchain on a deep foundation of high performance computing (HPC), cloud computing and big data. He is a prolific blogger and author of a book on cosmology.
There are more Things than anything else! And they’re going online in droves, adding capability but also vulnerability, and making the Secure Internet of Things (IoT), the super set of the technology trends. Secure IoT is the place with the most interdisciplinary, and therefore, the most difficult challenges. If you believe, as you should, that no-compromise cybersecurity is table stakes, then IoT means “secure IoT” and IoT by itself becomes the ultimate hashtag soup. Here is why.
IoT and security require an end-to-end mindset. Today’s solutions are fragmented. Integrating them is hard and requires blending of many technologies and algorithms. It requires a team with a very broad set of expertise who have managed to understand each other and employ each other’s strengths.
Things come in many shapes and sizes. We have to make a distinction between Small-t things, sensors and micro devices, Big-T Things, which often form meta things or Systems, or All-caps THINGS, meta systems and the so-called Distributed Autonomous Organizations (DAOs). Managing them in a coherent manner requires an expertise rubber band that has to stretch across from the microscopic to the astronomic:
Security is not just a technology and practice but a non-negotiable requirement that cuts across all aspect of tech. Small-t things require a whole a new approach to device and data security all the way up the supply chain, a difficult technological and operational task an area of intense innovation, for example by blending AI, IoT, and Cybersecurity disciplines or pursuing entirely new approaches. Big-T Things remain hard to secure but have more built-in resources and processing capability, making them eligible to be handled like existing IT systems.
Things, scattered around, are exposed to physical tampering so it is important to physically secure them and detect any unauthorized physical access or near-field or far-field monitoring. As is often said, “security is security” so physical security and cybersecurity must be viewed and implemented as one thing: all under the umbrella of risk and threat management and #sustainability.
Many Things move, either by themselves (like #drones or robots) or carried by other things (pick a form of transportation) or by people (personal device or #wearables). Mobility issues from initial on-boarding to authenticated secure transmission to data streaming and data semantics to over-the-air (OTA) updates all become critical in IoT.
Distributed Things that generate lots of data, require secure authenticated communications, are possibly subject to compliance regulations, may need digital rights management (#DRM), or need verifiable delivery… are the kind of use case that Blockchain technology can address very well. IoT applications are often natural candidates. (see also the OrionX Download podcast series on Blockchain.)
Connected devices generate data. Making sense of that data requires Big Data practices and technologies: data lakes, data science, storage, search, apps, etc.
When you have so much data, invariably you will find an opportunity for AI (see the OrionX/ai page for a repository of reports), either via #MachineLearning which uses the data, or #ExpertSystems for policy management which decides what to do about the insight that the data generates. AI framewroks, especially the #DeepLearning variety, are #HPC oriented and require knowledge of HPC systems and algorithms.
There will come a time when another emerging trend, #QuantumComputing, will be useful for AI and cybersecurity.
If Things are smart enough and can move, they become robots. Distributed Autonomous Objects (DAOs) use and generate data and require a host of security, processing, control, policy, etc.
All of this processing happens in the cloud somewhere, so all cloud technologies come into play. The required expertise will cover the gamut: app development, streaming data, dev-ops, elasticity, service level assurance, API management, microservices, data storage, multi-cloud reliability, etc.
IoT provides new kinds of information extraction, manipulation, and control. Just the AI and Robotics components are enough to pose a challenge for existing legal systems. Ultimately, it requires an ethics framework that can be used to create a proper legal system all the while ensuring widespread communication so organizational structures and culture can keep up with it.
Not every IoT deployment will need all of the above, but as you try to stitch together a strategy for your IoT project, it is imperative to do so with a big picture in mind. IoT is the essence of #DigitalTransformation and touches all aspects of your organization. It’s quite a challenge to make it seamless, and you’ll need specialists, generalists, and not just analysts but also “synthesists”.
In the OrionX 2016 Technology Issues and Predictions blog, we said “If you missed the boat on cloud, you can’t miss it on IoT too”, and “IoT is where Big Data Analytics, Cognitive Computing, and Machine Learning come together for entirely new ways of managing business processes.” Later, in February of 2016, my colleague Stephen Perrenod’s blog IoT: The Ultimate Convergence, further set the scene. Today, we see those predictions have become reality while IoT is poised for even more convergence.
We will cover these topics in future episodes of The OrionX Download podcast, identifying the salient points. As usual, we will go one or two levels below the surface to understand not just what these technologies do, but how they do it and how they came about. That will in turn, help put new developments in perspective and explain why they may or may not address a pain point.
Shahin is a technology analyst and an active CxO, board member, and advisor. He serves on the board of directors of Wizmo (SaaS) and Massively Parallel Technologies (code modernization) and is an advisor to CollabWorks (future of work). He is co-host of the popular RadioFreeHPC (“Car Talk” of supercomputing), OrionX Download, and Afero IoT podcasts.
We go through a transaction’s journey, explaining hash functions and public key cryptography along the way.
OrionX is a team of industry analysts, marketing executives, and demand generation experts. With a stellar reputation in Silicon Valley, OrionX is known for its trusted counsel, command of market forces, technical depth, and original content.
Stephen Perrenod joins Shahin and Dan to make sense of Cryptocurrency/Blockchain/Bitcoin. In this first installment, they start at the beginning: the original Satoshi Nakamoto paper that started it all.
OrionX is a team of industry analysts, marketing executives, and demand generation experts. With a stellar reputation in Silicon Valley, OrionX is known for its trusted counsel, command of market forces, technical depth, and original content.
David Simmons of Oracle and Peter ffoulkes of OrionX are Dan’s guests as they discuss Oracle’s strategy for Cloud in Enterprise IT. Dan Olds and Peter ffoulkes discuss the OrionX blog that instigated this episode.
OrionX is a team of industry analysts, marketing executives, and demand generation experts. With a stellar reputation in Silicon Valley, OrionX is known for its trusted counsel, command of market forces, technical depth, and original content.
There is a loudly expressed opinion that ‘the cloud’ is the future of computing and that companies such as Amazon, Google, and Microsoft will dominate the market, replacing established hardware and software vendors such as IBM, Dell, HPE, and Oracle. In some ways, this is true especially for startups, small companies, and perhaps even some midsize companies, but the situation for enterprise level deployments is very different.
For small companies, the use of cloud services minimizes the need for capital expenditure on infrastructure and the recruitment of IT specialists, accelerating their time to production, possibly at the expense of some customization.
By contrast, enterprise level organizations typically have a substantial reliance on established business critical applications. Many of these applications cannot easily be rewritten or re-deployed on public cloud architectures. Sometimes the systems they run on are not based on the x86 architecture, further complicating matters.
Existing enterprise applications, however, can and often need to be integrated with new cloud services to deliver a modern infrastructure as a cloud service while ensuring business continuity. This hybrid approach is an optimum combination of private and public cloud services. It leverages existing data center and infrastructure resources and combines elastic cloud services from external providers when needed. For businesses where regulation and compliance may affect where and how data may be located and accessed, or if large memory scale up data systems may be UNIX-based and are not well-suited or compatible with scale out Intel-based architectures, a hybrid cloud is particularly advantageous.
Enterprise customers are much more concerned with a business solution to their cloud problem than just a technical solution. Minimizing costly changes is valuable to them as long as the underlying technology has a reasonable and real roadmap. For them, a mixture of private and public cloud can address multiple requirements:
This approach focuses on business optimization, integrates and consolidates disparate systems, offers a flexible choice of on-premises, managed on-premises, or off-premises cloud.
For enterprise customers, this is a critical transformation. The service provider model is generally accepted as the way forward, but successfully navigating the journey requires the integration of existing business critical systems with new and agile applications. This requires differentiation at a business service level, it is not technology driven but must incorporate both the old and the new while optimizing capital costs and operational margins.
Cloud computing means fewer but larger buyers of infrastructure technologies. Cloud service providers such as Amazon and Google buy standardized systems in large volumes. Other CPU and infrastructure vendors have doubled down on the capabilities of their architectures to support both legacy and modern systems.
Among established technologies, x86 continues to dominate at the CPU level but is unable to reach a large body of enterprise applications that were written for IBM, HP, and Oracle/Sun. HP has effectively moved to x86 and is not offering its own public cloud service. IBM has formed the OpenPower consortium as it attempts to build an ecosystem and is building its Softlayer cloud service, but relies on application software partners to complete the stack. This leaves Oracle in the unique position of offering chips-to-apps-to-cloud in one place supporting x86 as well as its own SPARC chip (with the SPARC Dedicated Compute Service). As the only UNIX public cloud service available from a major systems or cloud vendor, Oracle has a robust roadmap with everything available on-premises, managed-on-premises, and off-premises.
For most enterprises, a successful transition to cloud service-based infrastructures is more important than a rapid transition. There are many market research reports that discuss the nature and pace of cloud adoption in ‘the Enterprise’, but one thing they have in common is the limited pace and scope of public cloud adoption. At the end of the day, it is business and workload considerations that drive decisions, not technology.
While the graph (same link as the image) is slightly dated (2015), the trends have not changed and the presented results are still indicative. The common trends demonstrate that dependence upon isolated on-premises systems – even if virtualized – is declining. Private cloud, whether on-premises or hosted is and will be a substantial part of the market, but the dominant and growing sector that seems destined to dominate is hybrid cloud, a combination of on-premises, hosted and public cloud services.
For many enterprises, the challenge is to find a vendor that can offer all of those things with a viable business model. We live in very interesting times when the only global technology vendor that appears to be able to meet all the criteria is Oracle, with a viable alternative to Intel Architecture in the form of SPARC.
Peter is an expert in Cloud Computing, Big Data, and HPC markets, technology trends, and customer requirements which he blends to craft growth strategies and assess opportunities.
Here at OrionX.net, our research agenda is driven by the latest developments in technology. We are also fortunate to work with many tech leaders in nearly every part of the “stack”, from chips to apps, who are driving the development of such technologies.
In recent years, we have had projects in Cryptocurrencies, IoT, AI, Cybersecurity, HPC, Cloud, Data Center, … and often a combination of them. Doing this for several years has given us a unique perspective. Our clients see value in our work since it helps them connect the dots better, impacts their investment decisions and the options they consider, and assists them in communicating their vision and strategies more effectively.
We wanted to bring that unique perspective and insight directly to you. “Simplifying the big ideas in technology” is how we’d like to think of it.
Thus was born our first podcast/slidecast series: The OrionX Download™
The OrionX Download is both a video slidecast (visuals but no talking heads) and an audio podcast in case you’d like to listen to it.
Every two weeks, co-hosts Dan Olds and Shahin Khan, and other OrionX analysts, discuss some of the latest and most important advances in technology. If our research has a specially interesting finding, we’ll invite guests to probe the subject and add their take.
Please give it a try and let us know what we can do better or if you have a specific question or topic that you’d like us to cover.
Shahin is a technology analyst and an active CxO, board member, and advisor. He serves on the board of directors of Wizmo (SaaS) and Massively Parallel Technologies (code modernization) and is an advisor to CollabWorks (future of work). He is co-host of the popular RadioFreeHPC (“Car Talk” of supercomputing), OrionX Download, and Afero IoT podcasts.
A variation of this article was originally published in The Register, “Wow, what an incredible 12 months: 2017’s data center year in review”, Feb 6, 2017.
The data center market is hot, especially now that we are getting a raft of new stuff, from promising non-Intel chips and system architectures to power and cooling optimizations to new applications in Analytics, IoT, and Artificial Intelligence.
Here is my Top-10 Data Center Predictions for 2017:
Data centers are information factories with lots of components and moving parts. There was a time when companies started becoming much more complex, which fueled the massive enterprise resource planning market. Managing everything in the data center is in a similar place. To automate, monitor, troubleshoot, plan, optimize, cost-contain, report, etc, is a giant task, and it is good to see new apps in this area.
Data center infrastructure management provides visibility into and helps control IT. Once it is deployed, you’ll wonder how you did without it. Some day, it will be one cohesive thing, but for now, because it’s such a big task, there will be several companies addressing different parts of it.
Cloud is the big wave, of course, and almost anything that touches it is on the right side of history. So, private and hybrid clouds will grow nicely and they will even temper the growth of public clouds. But the growth of public clouds will continue to impress, despite increasing recognition that they are not the cheapest option, especially for the mid-size users.
AWS will lead again, capturing most new apps. However, Azure will grow faster, on the strength of both landing new apps and also bringing along existing apps where Microsoft maintains a significant footprint.
Moving Exchange, Office, and other apps to the cloud, in addition to operating lots of regional data centers and having lots of local feet on the ground will help.
Some of the same dynamics will help Oracle show a strong hand and get close to Google and IBM. And large telcos will stay very much in the game. Smaller players will persevere and even grow, but they will also start to realize that public clouds are their supplier or partner, not their competition! It will be cheaper for them to OEM services from bigger players, or offer joint services, than to build and maintain their own public cloud.
Just as the hottest thing in enterprise becomes Big Data, we find that the most expensive part of computing is moving all that data around. Naturally, we start seeing what OrionX calls “In-Situ Processing” (see page 4 of the report in the link): instead of data going to compute, compute would go to data, processing it locally wherever data happens to be.
But as the gap between CPU speed and storage speed separates apps and data, memory becomes the bottleneck. In comes storage class memory (mostly flash, with a nod to other promising technologies), getting larger, faster and cheaper. So, we will see examples of apps using a Great Wall of persistent memory, built by hardware and software solutions that bridge the size/speed/cost gap between traditional storage and DRAM. Eventually, we expect programming languages to naturally support byte-addressable persistent memory.
Vendors already configure and sell racks, but they often populate them with servers that are designed as if they’d be used stand-alone. Vendors with rack-level thinking have been doing better because designing the rack vs the single node lets them add value to the rack while removing unneeded value from server nodes.
So server vendors will start thinking of a rack, not a single-node, as the system they design and sell. Intel’s Rack Scale Architecture has been on the right track, a real competitive advantage, and an indication of how traditional server vendors must adapt. The server rack will become the next level of integration and what a “typical system” will look like. Going forward, multi-rack systems are where server vendors have a shot at adding real value. HPC vendors have long been there.
Traditional enterprise apps – the bulk of what runs on servers – will show that they have access to enough compute capacity already. Most of that work is transactional, so their growth is correlated with the growth in GDP, minus efficiencies in processing.
New apps, on the other hand, are hungry, but they are much more distributed, more focused on mobile clients, and more amenable to what OrionX calls “High-Density Processing”: algorithms that have a high ops/bytes ratio running on hardware that provides similarly high ops/byte capability – ie, compute accelerators like GPUs, FPGAs, vector processors, manycore CPUs, ASICs, and new chips on the horizon.
On top of that, there will be more In-Situ Processing: processing the data wherever it happens to be, say locally on the client vs sending it around to the backend. This will be made easier by the significant rise in client-side computing power and more capable data center switches and storage nodes that can do a lot of local processing.
We will also continue to see cloud computing and virtualization eliminate idle servers and increase the utilization rates of existing systems.
Finally, commoditization of servers and racks, driven by fewer-but-larger buyers and standardization efforts like the Open Compute Project, put pressure on server costs and limit the areas in which server vendors can add value. The old adage in servers: “I know how to build it so it costs $1m, but don’t know how to build it so it’s worth $1m” will be true more than ever.
These will all combine to keep server revenues in check. We will see 5G’s wow-speeds but modest roll-out, and though it can drive a jump in video and some server-heavy apps, we’ll have to wait a bit longer.
The real battle in server architecture will be between Intel’s in-house coalition and what my colleague Dan Olds has called the Rebel Alliance: IBM’s OpenPower industry coalition. Intel brings its all-star team: Xeon Phi, Altera, Omni-Path (plus Nervana/Movidius) and Lustre, while OpenPower counters with a dream team if its own: POWER, Nvidia, Xilinx, and Mellanox (plus TrueNorth) and GPFS (Spectrum Scale). Then there is Watson which will become more visible as a differentiator, and a series of acquisitions by both camps as they fill various gaps.
The all-in-house model promises seamless integration and consistent design, while the extended team offers a best-of-breed approach. Both have merits. Both camps are pretty formidable. And there is real differentiation in strategy, design, and implementation. Competition is good.
One day in the not too distant future, your fancy car will break down on the highway for no apparent reason. It will turn out that the auto entertainment system launched a denial of service attack on the rest of the car, in a bold attempt to gain control. It even convinced the nav system to throw in with it! This is a joke, of course, but could become all too real if/when human and AI hackers get involved.
IoT is coming, and with it will come all sorts of security issues. Do you know where your connected devices are? Can you really trust them?
For the first time in decades, there is a real market opening for new chips. What drove this includes:
This will be a year when many new chips became available and tested, and there is a pretty long list of them, showing just how big the opportunity is, how eager investors must have been to not miss out, and how many different angles there are.
In addition to AI, there are a few important general-purpose chips being built. The coolest one is by startup Rex Computing, which is working on a chip for exascale, focused on very high compute-power/electric-power ratios. Qualcomm and Caviuum have manycore ARM processors, Oracle is advancing SPARC quite well, IBM’s POWER continues to look very interesting, and Intel and AMD push the X86 envelope nicely.
With AI chips, Intel already has acquired Nervana and Movidius. Google has its TPU, and IBM its neuromorphic chip, TrueNorth. Other AI chip efforts include Mobileye (the company is being bought by Intel since this article was written), Graphcore, BrainChip, TeraDeep, KnuEdge, Wave Computing, and Horizon Robotics. In addition, there are several well-publicized and respected projects like NeuRAM3, P-Neuro, SpiNNaker, Eyeriss, and krtkl going after different parts of the market. That’s a lot of chips, but most of these bets, of course, won’t pay off.
Speaking of chips, ARM servers will remain important but elusive. They will continue to make a lot of noise and point to significant wins and systems, but fail to move the needle when it comes to revenue market share in 2017.
As a long-term play, ARM is an important phenomenon in the server market – more so now with the backing of SoftBank, a much larger company apparently intent on investing and building, and various supercomputing and cloud projects that are great proving grounds.
But at the end, you need differentiation and ARM has the same problem against X86 as Intel’s Atom had against ARM. Atom did not differentiate vs ARM any more than ARM is differentiating vs Xeon.
Most systems end up being really good at something, however, and there are new apps and an open-source stack to support existing apps, which will help find specific workloads where ARM implementations could shine. That will help the differentiation become more than “it’s not X86”.
What is going on with big new apps? They keep getting more modular (microservices), more elastic (scale out), and more real-time (streaming). They’ve become their own large graph, in the computer science sense, and even more so with IoT (sensors everywhere plus In-Situ Processing).
As apps became services, they began resembling a network of interacting modules, depending on each other and evolving together. When the number of interacting pieces keeps increasing, you’ve got yourself a fabric. But as an app, it’s the kind of fabric that evolves and has an overall purpose (semantic web).
Among engineering disciplines, software engineering already doesn’t have a great reputation for predictability and maintainability. More modularity is not going to help with that.
But efforts to manage interdependence and application evolution have already created standards for structured modularity like the OSGi Alliance for Java. Smart organizations have had ways to reduce future problems (technical debt) from the get-go. So, it will be nice to see that type of methodology get better recognized and better generalized.
OK, we stop here now, leaving several interesting topics for later.
What do you think?
Shahin is a technology analyst and an active CxO, board member, and advisor. He serves on the board of directors of Wizmo (SaaS) and Massively Parallel Technologies (code modernization) and is an advisor to CollabWorks (future of work). He is co-host of the popular RadioFreeHPC (“Car Talk” of supercomputing), OrionX Download, and Afero IoT podcasts.
Today, we are announcing the OrionX Constellation™ research framework as part of our strategy services.
Paraphrasing what I wrote in my last blog when we launched 8 packaged solutions:
One way to see OrionX is as a partner that can help you with content, context, and dissemination. OrionX Strategy can create the right content because it conducts deep analysis and provides a solid perspective in growth segments like Cloud, HPC, IoT, Artifical Intelligence, etc. OrionX Marketing can put that content in the right context by aligning it with your business objectives and customer needs. And OrionX PR can build on that with new content and packaging to pursue the right dissemination, whether by doing the PR work itself or by working with your existing PR resources.
Even if you only use one of the services, the results are more effective because strategy is aware of market positioning and communication requirements; marketing benefits from deep industry knowledge and technical depth; and PR can set a reliable trajectory armed with a coherent content strategy.
The Constellation research is all about high quality content. That requires the ability to go deep on technologies, market trends, and customer sentiment. Over the past two years, we have built such a capability.
Given the stellar connotations of the name Orion (not to mention our partner Stephen Perrenod’s book and blog about quantum and astro- physics), it is natural that we would see the players in each market segment as stars in a constellation. Constellations interact and evolve, and stars are of varying size and brightness, their respective positions dependent on cosmic (market) forces and your (customers’) point of view. Capturing that reality in a relatively simple framework has been an exciting achievement that we are proud to unveil.
The OrionX strategy process collects and organizes data in three categories: market, customer, and product. The Constellation process further divides that into two parameters in each category:
This data is then analyzed to identify salient points which are in turn synthesized into the coherent insights that are necessary for a clear understanding of the dynamics at play and effective market conversations and change management.
The resulting content leads to five types of reports that capture the OrionX perspective on a segment. We call these the five Es:
Visualizing six distinct parameters, each with its own axis, in three categories cannot be done with the traditional 2×2 or 3×3 diagrams. Indeed, existing models in the industry do quite a good job of providing simple diagrams that rank vendors in a segment.
To simplify this visualization, we have developed the OrionX Constellation Decision Tool. This diagram effectively visualizes the six parameters that capture the core of technology customers’ selection process. (A future blog will drill down into the diagram.)
While OrionX is not a typical industry analyst firm, we will offer samples of our reports from time to time. We hope they are useful to you and provide a glimpse of our work. They will complement our blog.
As always, thank you for your support and friendship. Please track our progress on social media or contact us for a free consultation or with any comments. We’d love to hear from you.
Shahin is a technology analyst and an active CxO, board member, and advisor. He serves on the board of directors of Wizmo (SaaS) and Massively Parallel Technologies (code modernization) and is an advisor to CollabWorks (future of work). He is co-host of the popular RadioFreeHPC (“Car Talk” of supercomputing), OrionX Download, and Afero IoT podcasts.
The world of quantum computing frequently seems as bizarre as the alternate realities created in Lewis Carroll’s masterpieces “Alice’s Adventures in Wonderland” and “Through the Looking-Glass”. Carroll (Charles Lutwidge Dodgson) was a well-respected mathematician and logician in addition to being a photographer and enigmatic author.
Has quantum computing’s time actually come or are we just chasing rabbits?
That is probably a twenty million dollar question by the time a D-Wave 2X™ System has been installed and is in use by a team of researchers. Publicly disclosed installations currently include Lockheed Martin, NASA’s Ames Research Center and Los Alamos National Laboratory.
Hosted at NASA’s Ames Research Center in California, the Quantum Artificial Intelligence Laboratory (QuAIL) supports a collaborative effort among NASA, Google and the Universities Space Research Association (USRA) to explore the potential for quantum computers to tackle optimization problems that are difficult or impossible for traditional supercomputers to handle. Researchers on NASA’s QuAIL team are using the system to investigate areas where quantum algorithms might someday dramatically improve the agency’s ability to solve difficult optimization problems in aeronautics, Earth and space sciences, and space exploration. For Google the goal is to study how quantum computing might advance machine learning. The USRA manages access for researchers from around the world to share time on the system.
Using quantum annealing to solve optimization problems
D-Wave’s quantum annealing technology addresses optimization and probabilistic sampling problems by framing them as energy minimization problems and exploiting the properties of quantum physics to identify the most likely outcomes or as a probabilistic map of the solution landscape.
Quantum annealer dynamics are dominated by paths through the mean field energy landscape that have the highest transition probabilities. Figure 1 shows a path that connects local minimum A to local minimum D.
Figure 2 shows the effect of quantum tunneling (in blue) to reduce the thermal activation energy needed to overcome the barriers between the local minima with the greatest advantage observed from A to B and B to C, and a negligible gain from C to D. The principle and benefits are explained in detail in the paper “What is the Computational Value of Finite Range Tunneling?”
The D-Wave 2X: Interstellar Overdrive – How cool is that?
As a research area, quantum computing is highly competitive, but if you want to buy a quantum computer then D-Wave Systems , founded in 1999, is the only game in town. Quantum computing is as promising as it is unproven. Quantum computing goes beyond Moore’s law since every quantum bit (qubit) doubles the computational power, similar to the famous wheat and chessboard problem. So the payoff is huge, even though it is expensive, unproven, and difficult to program.
The advantage of quantum annealing machines is they are much simpler to build than gate-model quantum computing machines. The latest D-Wave machine (the D-Wave 2X), installed at NASA Ames, is approximately twice as powerful (in a quantum, exponential sense) as the previous model at over 1,000 qubits (1,097). This compares with roughly 10 qubits for current gate-model quantum systems, so two orders of magnitude. It’s a question of scale, no simple task, and a unique achievement. Although quantum researchers initially questioned whether the D-Wave system even qualified as a quantum computer, albeit a subset of quantum computing architectures, that argument seems mostly settled and it is now generally accepted that quantum characteristics have been adequately demonstrated.
In a D-Wave system, a coupled pair of qubits (quantum bits) demonstrate quantum entanglement (they influence each other), so that the entangled pair can be in any one of four states resulting from how the coupling and energy biases are programmed. By representing the problem to be addressed as an energy map the most likely outcomes can be derived by identifying the lowest energy states.
A lattice of approximately 1,000 tiny superconducting circuits (the qubits) is chilled close to absolute zero to deliver quantum effects. A user models a problem into a search for the lowest point in a vast energy landscape. The processor considers all possibilities simultaneously to determine the lowest energy required to form those relationships. Multiple solutions are returned to the user, scaled to show optimal answers, in an execution time of around 20 microseconds, practically instantaneously for all intents and purposes.
The D-wave system cabinet – “The Fridge”– is a closed cycle dilution refrigerator. The superconducting processor itself generates no heat, but to operate reliably must be cooled to about 180 times colder than interstellar space, approximately 0.015° Kelvin.
Environmental considerations: Green is the color
To function reliably, quantum computing systems require environments that are not only shielded from the Earth’s natural environment, but would be considered inhospitable to any known life form. A high vacuum is required, a pressure 10 billion times lower than atmospheric pressure, and shielded to 50,000 times less than Earth’s magnetic field. Not exactly a normal office, datacenter, or HPC facility environment.
On the other hand, the self-contained “Fridge” and servers consume just 25kW of power (approximately the power draw of a single heavily populated standard rack) and about three orders of magnitude (1000 times) less power than the current number one system on the TOP500, including its cooling system. Perhaps a more significant consideration is that power demand is not anticipated to increase significantly as it scales to several thousands of qubits and beyond.
In addition to doubling the number of qubits compared with the prior D-Wave system, the D-Wave 2X delivers lower noise in qubits and couples, delivering greater confidence in achieved results.
So much for the pictures, what about the conversations?
Now that we have largely moved beyond the debate of whether a D-Wave system is actually a quantum machine or not, then the question “OK, so what now?” could bring us back to chasing rabbits, although this time inspired by the classic Jefferson Airplane song, “White Rabbit”:
“One algorithm makes you larger
And another makes you small
But do the ones a D-Wave processes
Do anything at all?”
That of course, is where the conversations begin. It may depend upon the definition of “useful” and also a comparison between “conventional” systems and quantum computing approaches. Even the fastest supercomputer we can build using the most advanced traditional technologies can still only perform analysis by examining each possible solution serially, one solution at a time. This makes optimizing complex problems with a large number of variables and large data sets a very time consuming business. By comparison, once a problem has been suitably constructed for a quantum computer it can explore all the possible solutions at once and instantly identify the most likely outcomes.
If we consider relative performance then we begin to have a simplistic basis for comparison, at least for execution times. The QuAIL system was benchmarked for the time required to find the optimal solution with 99% probability for different problem sizes up to 945 variables. Simulated Annealing (SA), Quantum Monte Carlo (QMC) and the D-Wave 2X were compared. Full details are available in the paper referenced previously. Shown in the chart are the 50th, 75th and 85th percentiles over a set of 100 instances. The error bars represent 95% confidence intervals from bootstrapping.
This experiment occupied millions of processor cores for several days to tune and run the classical algorithms for these benchmarks. The runtimes for the higher quantiles for the larger problem sizes for QMC were not computed because the computational cost was too high.
The results demonstrate a performance advantage to the quantum annealing approach by a factor of 100 million compared with simulated annealing running on a single state of the art processor core. By comparison the current leading system on the TOP500 has fewer than 6 million cores of any kind, implying a clear performance advantage for quantum annealing based on execution time.
The challenge and the next step is to explore the mapping of real world problems to quantum machines and to improve the programming environments, which will no doubt take a significant amount of work and many conversations. New players will become more visible, early use cases and gaps will become better defined, new use cases will be identified, and a short stack will emerge to ease programming. This is reminiscent of the early days of computing or space flight.
A quantum of solace for the TOP500: Size still matters.
Even though we don’t expect to see viable exascale systems this decade, and quite likely not before the middle of the next, we won’t be seeing a Quantum500 anytime soon either. NASA talks about putting humans on Mars sometime in the 2030s and it isn’t unrealistic to think about practical quantum computing as being on a similar trajectory. Recent research at the University of New South Wales (UNSW) in Sidney, Australia demonstrated that it may be possible to create quantum computer chips that could store thousands, even millions of qubits on a single silicon processor chip leveraging conventional computer technology.
Although the current D-Wave 2X system is a singular achievement it is still regarded as being relatively small to handle real world problems, and would benefit from getting beyond pairwise connectivity, but that isn’t really the point. It plays a significant role in research into areas such as vision systems, artificial intelligence and machine learning alongside its optimization capabilities.
In the near term, we’ve got enough information and evidence to get the picture. It will be the conversations that become paramount with both conventional and quantum computing systems working together to develop better algorithms and expand the boundaries of knowledge and achievement.
References and attributions:
Paper on What is the Computational Value of Finite Range Tunneling? http://arxiv.org/pdf/1512.02206v4.pdf
Paper on Benchmarking a quantum annealing processor with the time-to-target metric: http://www.dwavesys.com/sites/default/files/ttt_arxiv.pdf
White rabbit from Sir John Tenniel’s illustrations of “Alice’s Adventures in Wonderland” by Lewis Carroll
“White Rabbit” Lyrics copyright Jefferson Airplane/ Grace Slick
Peter ffoulkes – Partner at OrionX.net
Peter is an expert in Cloud Computing, Big Data, and HPC markets, technology trends, and customer requirements which he blends to craft growth strategies and assess opportunities.
The Internet of Things (IoT) marketplace is expected to run into the trillions of dollars during the next 5 years. Gartner estimates 6.4 billion devices will be part of the Internet of Things this year. And they project $3 trillion of endpoint spending by the year 2020. [updated link] Gartner estimates 5.8 billion IoT endpoints will be in use in 2020 with revenue from endpoint electronics totaling $389 billion globally.
In our 2016 predictions blog, we said “If you missed the boat on cloud, you can’t miss it on IoT too”, and “IoT is where Big Data Analytics, Cognitive Computing, and Machine Learning come together for entirely new ways of managing business processes.”
IoT represents the ultimate convergence theme in the marketplace today. IoT includes fixed and mobile edge devices (fog computing), cloud computing, big data, analytics and machine learning, and in many instances can include social and mobile computing as well.
Because of the very large number of devices being incorporated in IoT solutions and because of the high data rates that must be supported at the very edge of a network, IoT also requires embedded computing with low-power processors for preliminary data ingestion and filtering. Data processing at the edge accomplishes two things: firstly, it allows elimination of data that does not need to be transmitted to the central cloud-based data lake. Secondly, it supports preliminary real-time processing of acquired data that can be used for device monitoring and control with immediate feedback and very low latency. Additionally, computing at the edge should be a prerequisite for enabling security of devices and the data at the time of acquisition.
IOT and cloud computing fit together like a hand in a glove. This is only natural with the highly distributed and very dynamic nature of IoT devices and IoT data. Cloud infrastructure provides the most versatile, flexible and adaptable platform for an IoT repository and processing system. Cloud vendors including Amazon (AWS), Microsoft (Azure), IBM (SoftLayer) and Alphabet (Google Compute Engine) are racing to implement IoT capabilities, APIs, and advanced analytics and machine learning solutions in their public cloud environments. For reasons of control and to meet privacy and regulatory requirements, many companies will choose to implement private cloud services as well. Hybrid cloud services will play an important role in IoT.
When it comes to infrastructure within IoT clouds and at the edge, one can imagine every type of processor, network and protocol, servers and storage playing a role – both in distributed and centralized fashions – for IoT environments. Shared APIs and protocols are critical to promote interoperability. End-to-end security is as well an imperative; security is needed within every device and at each and every layer and sub-layer of the solution.
If anything deserves the term Big Data it is IoT. Billions and billions of devices are becoming Internet-enabled and the number of devices engaged in IoT applications will soon exceed the number of people on Earth. Because there are now many Big Data solutions available in public clouds this reinforces the cloud as a natural repository for IoT-generated Big Data lakes.
Analytics and machine learning will be heavily used to extract maximum value from the large amount of data acquired. A very wide range of business intelligence, analytics and machine learning techniques will be required. We foresee that IoT will be a big driver for machine learning advances. Analytics and machine learning will support everything from better operations of the connected devices to better information on how devices are utilized and new value-added services enabled by the device manufacturers.
There is a wide array of potential IoT applications covering every industry: Transportation, Retail, Manufacturing, Energy, Finance, Smart Cities, Healthcare, Agriculture, Government and other areas. Consumer apps are found in areas such as wearables, fitness, smart homes, electronics, and gaming.
Benefits from IoT applications will include improved operational efficiency and reliability, insight into usage patterns, and opportunities for increased revenue and better product design for manufacturers. Just as today we cannot imagine life without the Internet, we will not be able to imagine life without IoT by the beginning of the next decade.
It’s difficult to think of an area that requires a more holistic and converged view than IoT and that is not already a natural subset or example of IoT. Robotics? Drones? 3D printing? …. Every device of value should benefit from being plugged into the Internet, if only for maintenance and monitoring purposes, and these IoT-enabled devices will become available to participate in other IoT apps. At the other end of the data flow, big value comes from analytics services that are applied to a pool of devices and the streaming data they provide.
IT and IoT will become inseparable. IoT requires, incorporates, and benefits from all of the other major themes in IT today and thus we see it as representing the Ultimate Convergence at present.
What is your IoT strategy? OrionX can help.
Stephen Perrenod has lived and worked in Asia, the US, and Europe and possesses business experience across all major geographies in the Asia-Pacific region. He specializes in corporate strategy for market expansion, and cryptocurrency/blockchain on a deep foundation of high performance computing (HPC), cloud computing and big data. He is a prolific blogger and author of a book on cosmology.
Here at OrionX.net, we are fortunate to work with tech leaders across several industries and geographies, serving markets in Mobile, Social, Cloud, and Big Data (including Analytics, Cognitive Computing, IoT, Machine Learning, Semantic Web, etc.), and focused on pretty much every part of the “stack”, from chips to apps and everything in between. Doing this for several years has given us a privileged perspective. We spent some time to discuss what we are seeing and to capture some of the trends in this blog: our 2016 technology issues and predictions. We cut it at 17 but we hope it’s a quick read that you find worthwhile. Let us know if you’d like to discuss any of the items or the companies that are driving them.
Energy is arguably the most important industry on the planet. Advances in energy efficiency and sustainable energy sources, combined with the debate and observations of climate change, and new ways of managing capacity risk are coming together to have a profound impact on the social and political structure of the world, as indicated by the Paris Agreement and the recent collapse in energy prices. These trends will deepen into 2016.
Money was the original virtualization technology! It decoupled value from goods, simplified commerce, and enabled the service economy. Free from the limitations of physical money, cryptocurrencies can take a fresh approach to simplifying how value (and ultimately trust, in a financial sense) is represented, modified, transferred, and guaranteed in a self-regulated manner. While none of the existing implementations accomplish that, they are getting better understood and the ecosystem built around them will point the way toward a true digital currency.
Whether flying, driving, walking, sailing, or swimming, drones and robots of all kinds are increasingly common. Full autonomy will remain a fantasy except for very well defined and constrained use cases. Commercial success favors technologies that aim to augment a human operator. The technology complexity is not in getting one of them to do an acceptable job, but in managing fleets of them as a single network. But everything will be subordinate to an evolving and complex legal framework.
A whole new approach to computing (as in, not binary any more), quantum computing is as promising as it is unproven. Quantum computing goes beyond Moore’s law since every quantum bit (qubit) doubles the computational power, similar to the famous wheat and chessboard problem. So the payoff is huge, even though it is, for now, expensive, unproven, and difficult to use. But new players will become more visible, early use cases and gaps will become better defined, new use cases will be identified, and a short stack will emerge to ease programming. This is reminiscent of the early days of computing so a visit to the Computer History Museum would be a good recalibrating experience.
The changing nature of work and traditional jobs received substantial coverage in 2015. The prospect of artificial intelligence that could actually work is causing fears of wholesale elimination of jobs and management layers. On the other hand, employers routinely have difficulty finding talent, and employees routinely have difficulty staying engaged. There is a structural problem here. The “sharing economy” is one approach, albeit legally challenged in the short term. But the freelance and outsourcing approach is alive and well and thriving. In this model, everything is either an activity-sliced project, or a time-sliced process, to be performed by the most suitable internal or external resources. Already, in Silicon Valley, it is common to see people carrying 2-3 business cards as they match their skills and passions to their work and livelihood in a more flexible way than the elusive “permanent” full-time job.
With the tectonic shifts in technology, demographic, and globalization, companies must transform or else. Design thinking is a good way to bring customer-centricity further into a company and ignite employees’ creativity, going beyond traditional “data driven needs analysis.” What is different this time is the intimate integration of arts and sciences. What remains the same is the sheer difficulty of translating complex user needs to products that are simple but not simplistic, and beautiful yet functional.
Old guard IT vendors will have the upper hand over new Cloud leaders as they all rush to claim IoT leadership. IoT is where Big Data Analytics, Cognitive Computing, and Machine Learning come together for entirely new ways of managing business processes. In its current (emerging) condition, IoT requires a lot more vertical specialization, professional services, and “solution-selling” than cloud computing did when it was in its relative infancy. This gives traditional big (and even small) IT vendors a chance to drive and define the terms of competition, possibly controlling the choice of cloud/software-stack.
Cybercrime is big business and any organization with digital assets is vulnerable to attack. As Cloud and Mobile weaken IT’s control and IoT adds many more points of vulnerability, new strategies are needed. Cloud-native security technologies will include those that redirect traffic through SaaS-based filters, Micro-Zones to permeate security throughout an app, and brand new approaches to data security.
In any value chain, a vendor must decide what value it offers and to whom. With cloud computing, the IT value chain has been disrupted. What used to be a system is now a piece of some cloud somewhere. As the real growth moves to “as-a-service” procurements, there will be fewer but bigger buyers of raw technology who drive hardware design towards scale and commoditization.
The computing industry was down the path of hardware partitioning when virtualization took over, and dynamic reconfiguration of hardware resources took a backseat to manipulating software containers. Infrastructure-as-code, composable infrastructure, converged infrastructure, and rack-optimized designs expand that concept. But container reconfiguration is insufficient at scale, and what is needed is hardware reconfiguration across the data center. That is the next frontier and the technologies to enable it are coming.
Mobile devices are now sufficiently capable that new features may or may not be needed by all users and new OS revs often slow down the device. Even with free upgrades and pre-downloaded OS revs, it is hard to make customers upgrade, while power users jailbreak and get the new features on an old OS. Over time, new capabilities will be provided via more modular dynamically loaded OS services, essentially a new class of apps that are deeply integrated into the OS, to be downloaded on demand.
Nowhere are the demands for scale, flexibility and effectiveness for analytics greater than in social media. This is far beyond Web Analytics. The seven largest “populations” in the world are Google, China, India, Facebook, WhatsApp, WeChat and Baidu, in approximately that order, not to mention Amazon, Apple, Samsung, and several others, plus many important commercial and government applications that rely on social media datasets. Searching through such large datasets with very large numbers of images, social commentary, and complex network relationships stresses the analytical algorithms far beyond anything ever seen before. The pace of algorithmic development for data analytics and for machine intelligence will accelerate, increasingly shaped by social media requirements.
Legacy modernization will get more attention as micro-services, data-flow, and scale-out elasticity become established. But long-term, software engineering is in dire need of the predictability and maintainability that is associated with other engineering disciplines. That need is not going away and may very well require a wholesale new methodology for programming. In the meantime, technologies that help automate software modernization, or enable modular maintainability, will gain traction.
The technical and operational burden on developers has been growing. It is not sustainable. NoSQL databases removed the time-delay and complexity of a data schema at the expense of more complex codes, pulling developers closer to data management and persistence issues. DevOps, on the other hand, has pulled developers closer to the actual deployment and operation of apps with the associated networking, resource allocation, and quality-of-service (QoS) issues. This is another “rubber band” that cannot stretch much more. As cloud adoption continues, development, deployment, and operations will become more synchronized enabling more automation.
The idea of a “memory-only architecture” dates back several decades. New super-large memory systems are finally making it possible to hold entire data sets in memory. Combine this with Flash (and other emerging storage-class memory technologies) and you have the recipe for entirely new ways of achieving near-real-time/straight-through processing.
Small and mid-size public cloud providers will form coalitions around a large market leader to offer enterprise customers the flexibility of cloud without the lock-in and the risk of having a single supplier for a given app. This opens the door for transparently running a single app across multiple public clouds at the same time.
It’s been years since most app developers needed to know what CPU their app runs on, since they work on the higher levels of a tall software stack. Porting code sill requires time and effort but for elastic/stateless cloud apps, the work is to make sure the software stack is there and works as expected. But the emergence of large cloud providers is changing the dynamics. They have the wherewithal to port any system software to any CPU thus assuring a rich software infrastructure. And they need to differentiate and cut costs. We are already seeing GPUs in cloud offerings and FPGAs embedded in CPUs. We will also see the first examples of special cloud regions based on one or more of ARM, OpenPower, MIPS, and SPARC. Large providers can now offer a usable cloud infrastructure using any hardware that is differentiated and economically viable, free from the requirement of binary compatibility.
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