IBM announced a lot of goodies for z and Power users at Enterprise 2013 wrapping up in Orlando today. There were no blockbuster announcements, like a new z machine—we’re probably 12-18 months away from that and even then the first will likely focus on Power8—but it brought a slew of announcements nonetheless. For a full rundown on what was announced click here.
Cloud and analytics—not surprisingly—loom large. For example, Hadoop and a variety of other capabilities have been newly cobbled together, integrated, optimized, and presented as new big data offerings or as new cloud solutions. This was exemplified by a new Cognos offering for CFOs needing to create, analyze and manage sophisticated financial plans that can provide greater visibility into enterprise profitability or the lack thereof.
Another announcement featured a new IBM Entry Cloud Configuration for SAP on zEnterprise. This is a cloud-enablement offering combining high-performance technology and services to automate, standardize and accelerate day-to-day SAP operations for reduced operational costs and increased ROI. Services also were big at the conference.
Kicking off the event was a dive into data center economics by Steve Mills, Senior Vice President & Group Executive, IBM Software & Systems. Part of the challenge of optimizing IT economics, he noted, was that the IT environment is cumulative. Enterprises keep picking up more systems, hardware and software, as new needs arise but nothing goes away or gets rationalized in any meaningful way.
Between 2000 and 2010, Mills noted, servers had grown at a 6x rate while storage grew at a 69x rate. Virtual machines, meanwhile, were multiplying at the rate of 42% per year. Does anyone see a potential problem here?
Mills’ suggestion: virtualize and consolidate. Specifically, large servers are better for consolidation. His argument goes like this: Most workloads experience variance in demand. But when you consolidate workloads with variance on a virtualized server the variance of the sum is less due to statistical multiplexing (which fits workloads into the gaps created by the variances). Furthermore, the more workloads you can consolidate, the smaller the variance of the sum. His conclusion: bigger servers with capacity to run more workloads can be driven to higher average utilization levels without violating service level agreements, thereby reducing the cost per workload. Finally, the larger the shared processor pool is the more statistical benefit you get.
On the basis of statistical multiplexing, the zEnterprise and the Power 795 are ideal choices for this. Depending on your workloads, just load up the host server, a System z or a big Power box, with as many cores as you can afford and consolidate as many workloads as practical.
Mills’ other cost savings tips: use flash to avoid the cost and complexity of disk storage. Also, eliminate duplicate applications—the fewer you run, the lower the cost. In short, elimination is the clearest path to saving money in the data center.
To illustrate the point, Jim Tussing from Nationwide described how the company virtualized and consolidated 60% on their 10,500 servers on a few mainframes and saved $46 million over five years. It also allowed the company to delay the need for an additional data center for 4 years.
See, if DancingDinosaur was an actual data center manager it could have justified attendance at the entire conference based on the economic tips from just one of the opening keynotes and spent the rest of the conference playing golf. Of course, DancingDinosaur doesn’t play golf so it sat in numerous program sessions instead, which you will hear more about in coming weeks.
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