Looking at Oracle’s latest iteration of its SPARC processor technology, the new M7 CPU, it is at first blush an excellent implementation of SPARC, with 32 cores with 8 threads each implemented in an aggressive 20 nm process and promising a well-deserved performance bump for legacy SPARC/Solaris users. But the impact of the M7 goes beyond simple comparisons to previous generations of SPARC and competing products such as Intel’s Xeon E7 and IBM POWER 8. The M7 is Oracle’s first tangible delivery of its “Software on Silicon” promise, with significant acceleration of key software operations enabled in the M7 hardware.[i]
Oracle took aim at selected performance bottlenecks and security exposures, some specific to Oracle software, and some generic in nature but of great importance. Among the major enhancements in the M7 are:[ii]
Cryptography – While many CPUs now include some form of acceleration for cryptography, Oracle claims the M7 includes a wider variety and deeper support, resulting in almost indistinguishable performance across a range of benchmarks with SSL and other cryptographic protocols enabled. Oracle claims that the M7 is the first CPU architecture that does not present users with the choice of secure or fast, but allows both simultaneously.
The acquisition of EMC by Dell has is generating an immense amount of hype and prose, much of it looking forward at how the merged entity will try and compete in cloud, integrate and rationalize its new product line, and how Dell will pay for it (see Forrester report “Quick Take: Dell Buys EMC, Creating a New Legacy Vendor”). Interestingly not a lot has been written about the changes in the fundamental competitive faceoff between Dell and HP, both newly transformed by divestiture and by acquisition.
Yesterday the competition was straightforward and relatively easy to characterize. HP is the dominant enterprise server vendor, Dell a strong challenger, both with PCs and both with some storage IP that was good but in no sense dominant. Both have competent data center practices and embryonic cloud strategies which were still works in process. Post transformation we have a totally different picture with two very transformed companies:
A slimmer HP. HP is smaller (although $50B is not in any sense a small company), and bereft of its historical profit engine, the margins on its printer supplies. Free to focus on its core mandate of enterprise systems, software and services, HP Enterprise is positioning itself as a giant startup, focused and agile. Color me slightly skeptical but willing to believe that it can’t be any less agile than its precursor at twice the size. Certainly along with the margin contribution they lose the option to fight about budget allocations between enterprise and print/PC priorities.
In the world of CMOS semiconductor process, the fundamental heartbeat that drives the continuing evolution of all the devices and computers we use and governs at a fundamantal level hte services we can layer on top of them is the continual shrinkage of the transistors we build upon, and we are used to the regular cadence of miniaturization, generally led by Intel, as we progress from one generation to the next. 32nm logic is so old-fashioned, 22nm parts are in volume production across the entire CPU spectrum, 14 nm parts have started to appear, and the rumor mill is active with reports of initial shipments of 10 nm parts in mid-2016. But there is a collective nervousness about the transition to 7 nm, the next step in the industry process roadmap, with industry leader Intel commenting at the recent 2015 International Solid State Circuit conference that it may have to move away from conventional silicon materials for the transition to 7 nm parts, and that there were many obstacles to mass production beyond the 10 nm threshold.
But there are other players in the game, and some of them are anxious to demonstrate that Intel may not have the commanding lead that many observers assume they have. In a surprise move that hints at the future of some of its own products and that will certainly galvanize both partners and competitors, IBM, discounted by many as a spent force in the semiconductor world with its recent divestiture of its manufacturing business, has just made a real jaw-dropper of an announcement – the existence of working 7nm semiconductors.
Today, IBM and Box announced a partnership and integration strategyto “transform work in the cloud." This is an interesting move that further validates Forrester’s view that the ECM market is transforming — largely due to new, often customer-activated, use cases. We also see that the current horizontal collaboration market is shifting to better target specific work output, as opposed to more general-purpose knowledge-dissemination use cases.
What does this partnership mean for IBM, Box, and their partners and customers?
For Box, the company gets important access to the extensive IBM ecosystem: Global Services, developer communities via IBM’s Bluemix platform, and the IBM-Apple MobileFirst relationship, as well as engineering acceleration to fill gaps in its content collaboration offering in areas such as capture, case management, governance, and analytics, including Watson.
Unfortunately, visa issues prevented me from attending the OpenStack summit in Vancouver last week — despite submitting my application to the Canadian embassy in Beijing 40 days in advance! However after following extensive online discussions of the event and discussing it with vendors and peers, I would say that OpenStack is moving to a new phase, for two reasons:
The rise of containers is laying the foundation for the next level of enterprise readiness. Docker’s container technology has become a major factor in the evolution of OpenStack components. Docker drivers have been implemented for the key components of Nova and Heat for extended computing and orchestration capabilities, respectively. The Magnum project aiming at container services allows OpenStack to create clusters with Kubernetes (k8s) by Google and Swarm by Docker.com. The Murano project contributed by Mirantis aiming at application catalog services is also integrated with k8s.
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.