I’ve written and commented in the past about the inevitability of a new class of infrastructure called “composable”, i.e. integrated server, storage and network infrastructure that allowed its users to “compose”, that is to say configure, a physical server out of a collection of pooled server nodes, storage devices and shared network connections.[i]
The early exemplars of this class were pioneering efforts from Egenera and blade systems from Cisco, HP, IBM and others, which allowed some level of abstraction (a necessary precursor to composablity) of server UIDs including network addresses and storage bindings, and introduced the notion of templates for server configuration. More recently the Dell FX and the Cisco UCS M-Series servers introduced the notion of composing of servers from pools of resources within the bounds of a single chassis.[ii] While innovative, they were early efforts, and lacked a number of software and hardware features that were required for deployment against a wide spectrum of enterprise workloads.
This morning, HPE put a major marker down in the realm of composable infrastructure with the announcement of Synergy, its new composable infrastructure system. HPE Synergy represents a major step-function in capabilities for core enterprise infrastructure as it delivers cloud-like semantics to core physical infrastructure. Among its key capabilities:
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.
Another week, another divestiture in the content management and collaboration market. A new - or more accurately, a re-newed - player enters the Enterprise Content Management market this week as iManage and HP make an apparently amicable split. Executives with longstanding roots in the iManage and Interwoven businesses, including Neil Araujo and Dan Carmel, have executed a management buyout to spin a revitalized iManage business out of HP’s Software division. iManage's press
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.
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.
Intel has made no secret of its development of the Xeon D, an SOC product designed to take Xeon processing close to power levels and product niches currently occupied by its lower-power and lower performance Atom line, and where emerging competition from ARM is more viable.
The new Xeon D-1500 is clear evidence that Intel “gets it” as far as platforms for hyperscale computing and other throughput per Watt and density-sensitive workloads, both in the enterprise and in the cloud are concerned. The D1500 breaks new ground in several areas:
It is the first Xeon SOC, combining 4 or 8 Xeon cores with embedded I/O including SATA, PCIe and multiple 10 nd 1 Gb Ethernet ports.
It is the first of Intel’s 14 nm server chips expected to be introduced this year. This expected process shrink will also deliver a further performance and performance per Watt across the entire line of entry through mid-range server parts this year.
Why is this significant?
With the D-1500, Intel effectively draws a very deep line in the sand for emerging ARM technology as well as for AMD. The D1500, with 20W – 45W power, delivers the lower end of Xeon performance at power and density levels previously associated with Atom, and close enough to what is expected from the newer generation of higher performance ARM chips to once again call into question the viability of ARM on a pure performance and efficiency basis. While ARM implementations with embedded accelerators such as DSPs may still be attractive in selected workloads, the availability of a mainstream x86 option at these power levels may blunt the pace of ARM design wins both for general-purpose servers as well as embedded designs, notably for storage systems.
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.