We have been repeatedly reminded that the requirements of hyper-scale cloud properties are different from those of the mainstream enterprise, but I am now beginning to suspect that the top strata of the traditional enterprise may be leaning in the same direction. This suspicion has been triggered by the combination of a recent day in NY visiting I&O groups in a handful of very large companies and a number of unrelated client interactions.
The pattern that I see developing is one of “haves” versus “have nots” in terms of their ability to execute on their technology vision with internal resources. The “haves” are the traditional large sophisticated corporations, with a high concentration in financial services. They have sophisticated IT groups, are capable fo writing extremely complex systems management and operations software, and typically own and manage 10,000 servers or more. The have nots are the ones with more modest skills and abilities, who may own 1000s of servers, but tend to be less advanced than the core FSI companies in terms of their ability to integrate and optimize their infrastructure.
The divergence in requirements comes from what they expect and want from their primary system vendors. The have nots are companies who understand their limitations and are looking for help form their vendors in the form of converged infrastructures, new virtualization management tools, and deeper integration of management software to automate operational tasks, These are people who buy HP c-Class, Cisco UCS, for example, and then add vendor-supplied and ISV management and automation tools on top of them in an attempt to control complexity and costs. They are willing to accept deeper vendor lock-in in exchange for the benefits of the advanced capabilities.
A project I’m working on for an approximately half-billion dollar company in the health care industry has forced me to revisit Hyper-V versus VMware after a long period of inattention on my part, and it has become apparent that Hyper-V has made significant progress as a viable platform for at least medium enterprises. My key takeaways include:
Hyper-V has come a long way and is now a viable competitor in Microsoft environments up through mid-size enterprise as long as their DR/HA requirements are not too stringent and as long as they are willing to use Microsoft’s Systems Center, Server Management Suite and Performance Resource Optimization as well as other vendor specific pieces of software as part of their management environment.
Hyper-V still has limitations in VM memory size, total physical system memory size and number of cores per VM compared to VMware, and VMware boasts more flexible memory management and I/O options, but these differences are less significant that they were two years ago.
For large enterprises and for complete integrated management, particularly storage, HA, DR and automated workload migration, and for what appears to be close to 100% coverage of workload sizes, VMware is still king of the barnyard. VMware also boasts an incredibly rich partner ecosystem.
For cloud, Microsoft has a plausible story but it is completely wrapped around Azure.
While I have not had the time (or the inclination, if I was being totally honest) to develop a very granular comparison, VMware’s recent changes to its legacy licensing structure (and subsequent changes to the new pricing structure) does look like license cost remains an attraction for Microsoft Hyper-V, especially if the enterprise is using Windows Server Enterprise Edition.
An important prerequisite for a full cloud broker model is the technical capability of cloud bursting:
Cloud bursting is the dynamic relocation of workloads from private environments to cloud providers and vice versa. A workload can represent IT infrastructure or end-to-end business processes.
The initial meaning of cloud bursting was relatively simple. Consider this scenario: An enterprise with traditional, non-cloud infrastructure is running out of infrastructure and temporarily gets additional compute power from a cloud service provider. Many enterprises have now established private clouds, and cloud bursting fits even better here, with dynamic workload relocation between private clouds, public clouds, and the more private provider models in the middle; Forrester calls these virtual private clouds. The private cloud is literally bursting into the next cloud level at peak times.
An essential step before leveraging cloud bursting is properly classifying workloads. This involves describing the most public cloud level possible, based on technical restrictions and data privacy needs (including compliance concerns). A conservative enterprise could structure their workloads into three classes of cloud:
Productive workloads of back-office data and processes, such as financial applications or customer-related transactions:These need to remain on-premises. An example is the trading system of an investment bank.
NVIDIA recently shared a case study involving risk calculations at a JP Morgan Chase that I think is significant for the extreme levels of acceleration gained by integrating GPUs with conventional CPUs, and also as an illustration of a mainstream financial application of GPU technology.
JP Morgan Chase’s Equity Derivatives Group began evaluating GPUs as computational accelerators in 2009, and now runs over half of their risk calculations on hybrid systems containing x86 CPUs and NVIDIA Tesla GPUs, and claims a 40x improvement in calculation times combined with a 75% cost savings. The cost savings appear to be derived from a combination of lower capital costs to deliver an equivalent throughput of calculations along with improved energy efficiency per calculation.
Implicit in the speedup of 40x, from multiple hours to several minutes, is the implication that these calculations can become part of a near real-time business-critical analysis process instead of an overnight or daily batch process. Given the intensely competitive nature of derivatives trading, it is highly likely that JPMC will enhance their use of GPUs as traders demand an ever increasing number of these calculations. And of course, their competition has been using the same technology as well, based on numerous conversations I have had with Wall Street infrastructure architects over the past year.
My net take on this is that we will see a succession of similar announcements as GPUs become a fully mainstream acceleration technology as opposed to an experimental fringe. If you are an I&O professional whose users are demanding extreme computational performance on a constrained space, power and capital budget, you owe it to yourself and your company to evaluate the newest accelerator technology. Your competitors are almost certainly doing so.