The Cloud Foundry Foundation held its 2015 Summit recently in Santa Clara, attracting 1,500 application developers, operation experts, technical and business managers, service providers, and community contributors. After listening to the presentations and discussions, I believe that Cloud Foundry —one of the major platform-as-a-service (PaaS) offerings —is making a strategic shift from its traditional focus on application staging and execution to a new emphasis on micro-service composition. This is a key factor that will help companies gain the agility they need for both technology management and business transformation. Here’s what I learned:
Containers are critical for micro-service-based agility. Container based micro-services are getting momentum: IBM presented their latest Bluemix UI micro-services architecture; while SAP introduced their latest practice on Docker. Containers can encapsulate fine-grained business logic as micro-services for dynamic composition, which will greatly simplify development and deployment of applications, helping firms achieve continuous delivery to meet dynamic business requirements. This is why Forrester believes that the combination of containers and micro-services will prove irresistible for developers.
A few months ago, I blogged about testing quality@speed in the same way that F1 racing teams do to win races and fans. Last week, I published my F(TA)1 Forrester Wave! It examines the capabilities of nine vendors to evaluate how they support Agile development and continuous delivery teams when it comes to continuous testing: Borland, CA Technologies, HP, IBM, Microsoft, Parasoft, SmartBear, TestPlant, and Tricentis. However, only Forrester clients can attend “the race” to see the leaders.
The market overview section of our evaluation complements the analysis in the underlying model by looking at other providers that either augment FTA capabilities, play in a different market segment, or did not meet one of the criteria for inclusion in the Forrester Wave. These include: 1) open source tools like Selenium and Sahi, 2) test case design and automation tools like Grid-Tools Agile Designer, and 3) other tools, such as Original Software, which mostly focuses on graphical user interface (GUI) and packaged apps testing, and Qualitia and Applitools, which focus on GUI and visualization testing.
We deliberately weighted the Forrester Wave criteria more heavily towards “beyond GUI” and API testing approaches. Why? Because:
Recently we’ve had a chance to look again at two very conflicting views from HP and Facebook on how to do web-scale and cloud computing, both announced at the recent OCP annual event in California.
From HP come its new CloudLine systems, the public face of their joint venture with Foxcon. Early details released by HP show a line of cost-optimized servers descended from a conventional engineering lineage and incorporating selected bits of OCP technology to reduce costs. These are minimalist rack servers designed, after stripping away all the announcement verbiage, to compete with white-box vendors such as Quanta, SuperMicro and a host of others. Available in five models ranging from the minimally-featured CL1100 up through larger nodes designed for high I/O, big data and compute-intensive workloads, these systems will allow large installations to install capacity at costs ranging from 10 – 25% less than the equivalent capacity in their standard ProLiant product line. While the strategic implications of HP having to share IP and market presence with Foxcon are still unclear, it is a measure of HP’s adaptability that they were willing to execute on this arrangement to protect against inroads from emerging competition in the most rapidly growing segment of the server market, and one where they have probably been under immense margin pressure.
We have been watching many variants on efficient packaging of servers for highly scalable workloads for years, including blades, modular servers, and dense HPC rack offerings from multiple vendors, most of the highly effective, and all highly proprietary. With the advent of Facebook’s Open Compute Project, the table was set for a wave of standardized rack servers and the prospect of very cost-effective rack-scale deployments of very standardized servers. But the IP for intelligently shared and managed power and cooling at a rack level needed a serious R&D effort that the OCP community, by and large, was unwilling to make. Into this opportunity stepped Intel, which has been quietly working on its internal Rack Scale Architecture (RSA) program for the last couple of years, and whose first product wave was officially outed recently as part of an announcement by Intel and Ericsson.
While not officially announcing Intel’s product nomenclature, Ericsson announced their “HDS 8000” based on Intel’s RSA, and Intel representatives then went on to explain the fundamental of RSA, including a view of the enhancements coming this year.
RSA is a combination of very standardized x86 servers, a specialized rack enclosure with shared Ethernet switching and power/cooling, and layers of firmware to accomplish a set of tasks common to managing a rack of servers, including:
· Asset discovery
· Switch setup and management
· Power and cooling management across the servers with the rack
Last year I published a reasonably well-received research document on Hadoop infrastructure, “Building the Foundations for Customer Insight: Hadoop Infrastructure Architecture”. Now, less than a year later it’s looking obsolete, not so much because it was wrong for traditional (and yes, it does seem funny to use a word like “traditional” to describe a technology that itself is still rapidly evolving and only in mainstream use for a handful of years) Hadoop, but because the universe of analytics technology and tools has been evolving at light-speed.
If your analytics are anchored by Hadoop and its underlying map reduce processing, then the mainstream architecture described in the document, that of clusters of servers each with their own compute and storage, may still be appropriate. On the other hand, if, like many enterprises, you are adding additional analysis tools such as NoSQL databases, SQL on Hadoop (Impala, Stinger, Vertica) and particularly Spark, an in-memory-based analytics technology that is well suited for real-time and streaming data, it may be necessary to begin reassessing the supporting infrastructure in order to build something that can continue to support Hadoop as well as cater to the differing access patterns of other tools sets. This need to rethink the underlying analytics plumbing was brought home by a recent demonstration by HP of a reference architecture for analytics, publicly referred to as the HP Big Data Reference Architecture.
On one level, IBM’s new z13, announced last Wednesday in New York, is exactly what the mainframe world has been expecting for the last two and a half years – more capacity (a big boost this time around – triple the main memory, more and faster cores, more I/O ports, etc.), a modest boost in price performance, and a very sexy cabinet design (I know it’s not really a major evaluation factor, but I think IBM’s industrial design for its system enclosures for Flex System, Power and the z System is absolutely gorgeous, should be in the MOMA*). IBM indeed delivered against these expectations, plus more. In this case a lot more.
In addition to the required upgrades to fuel the normal mainframe upgrade cycle and its reasonably predictable revenue, IBM has made a bold but rational repositioning of the mainframe as a core platform for the workloads generated by mobile transactions, the most rapidly growing workload across all sectors of the global economy. What makes this positioning rational as opposed to a pipe-dream for IBM is an underlying pattern common to many of these transactions – at some point they access data generated by and stored on a mainframe. By enhancing the economics of the increasingly Linux-centric processing chain that occurs before the call for the mainframe data, IBM hopes to foster the migration of these workloads to the mainframe where its access to the resident data will be more efficient, benefitting from inherently lower latency for data access as well as from access to embedded high-value functions such as accelerators for inline analytics. In essence, IBM hopes to shift the center of gravity for mobile processing toward the mainframe and away from distributed x86 Linux systems that they no longer manufacture.
I’ve been getting a steady trickle of inquires this year about the future of the mainframe from our enterprise clients. Most of them are more or less in the form of “I have a lot of stuff running on mainframes. Is this a viable platform for the next decade or is IBM going to abandon them.” I think the answer is that the platform is secure, and in the majority of cases the large business-critical workloads that are currently on the mainframe probably should remain on the mainframes. In the interests of transparency I’ve tried to lay out my reasoning below so that you can see if it applies to your own situation.
How Big is the Mainframe LOB?
It's hard to get exact figures for the mainframe contributions to IBM's STG (System & Technology Group) total revenues, but the data they have shared shows that their mainframe revenues seem to have recovered from the declines of previous quarters and at worst flattened. Because the business is inherently somewhat cyclical, I would expect that the next cycle of mainframes, rumored to be arriving next year, should give them a boost similar to the last major cycle, allowing them to show positive revenues next year.
I’ve been talking to a number of users and providers of bare-metal cloud services, and am finding the common threads among the high-profile use cases both interesting individually and starting to connect some dots in terms of common use cases for these service providers who provide the ability to provision and use dedicated physical servers with very similar semantics to the common VM IaaS cloud – servers that can be instantiated at will in the cloud, provisioned with a variety of OS images, be connected to storage and run applications. The differentiation for the customers is in behavior of the resulting images:
Deterministic performance – Your workload is running on a dedicated resource, so there is no question of any “noisy neighbor” problem, or even of sharing resources with otherwise well-behaved neighbors.
Extreme low latency – Like it or not, VMs, even lightweight ones, impose some level of additional latency compared to bare-metal OS images. Where this latency is a factor, bare-metal clouds offer a differentiated alternative.
Raw performance – Under the right conditions, a single bare-metal server can process more work than a collection of VMs, even when their nominal aggregate performance is similar. Benchmarking is always tricky, but several of the bare metal cloud vendors can show some impressive comparative benchmarks to prospective customers.
There is always a tendency to regard the major players in large markets as being a static background against which the froth of smaller companies and the rapid dance of customer innovation plays out. But if we turn our lens toward the major server vendors (who are now also storage and networking as well as software vendors), we see that the relatively flat industry revenues hide almost continuous churn. Turn back the clock slightly more than five years ago, and the market was dominated by three vendors, HP, Dell and IBM. In slightly more than five years, IBM has divested itself of highest velocity portion of its server business, Dell is no longer a public company, Lenovo is now a major player in servers, Cisco has come out of nowhere to mount a serious challenge in the x86 server segment, and HP has announced that it intends to split itself into two companies.
And it hasn’t stopped. Two recent events, the fracturing of the VCE consortium and the formerly unthinkable hook-up of IBM and Cisco illustrate the urgency with which existing players are seeking differential advantage, and reinforce our contention that the whole segment of converged and integrated infrastructure remains one of the active and profitable segments of the industry.
EMC’s recent acquisition of Cisco’s interest in VCE effectively acknowledged what most customers have been telling us for a long time – that VCE had become essentially an EMC-driven sales vehicle to sell storage, supported by VMware (owned by EMC) and Cisco as a systems platform. EMC’s purchase of Cisco’s interest also tacitly acknowledges two underlying tensions in the converged infrastructure space:
An inquiry call from a digital strategy agency advising a client of theirs on data commercialization generated a lively discussion on strategies for taking data to market. With few best practices out there, the emerging opportunity just might feel like space exploration – going boldly where no man has gone before. The question is increasingly common. "We know we have data that would be of use to others but how do we know? And, which use cases should we pursue?" In It's Time To Take Your Data To Market published earlier this fall, my colleagues and I provided some guideance on identifying and commercializing that "Picasso in the attic." But the ideas around how to go-to-market continue to evolve.
In answer to the inquiry questions asked the other day, my advice was pretty simple: Don’t try to anticipate all possible uses of the data. Get started by making selected data sets available for people to play with, see what it can do, and talk about it to spread the word. However, there are some specific use cases that can kick-start the process.
Look to your existing customers.
The grass is not always greener, and your existing clients might just provide some fertile ground. A couple thoughts on ways your existing customers could use new data sources: