Cloud SLAs: A contrarian view

CIO - You've probably seen a hundred -- or even a thousand -- articles criticizing cloud computing service-level agreements (SLA). A common example in those articles is the putatively low Amazon Web Services SLA. Typically, authors of these kind of articles go on to cite recent outages by cloud providers, implying (or stating directly) that cloud computing falls woefully short of the true SLA requirements of enterprises, often described as "five nines," i.e., 99.999% availability.

Left unsaid in these articles is the assumption that enterprise data centers operate at far higher availability rates than cloud providers. Frankly, I'm unconvinced of this. It seems that nearly every time I contact a large company's support, the very nice call center representative apologizes for a delay caused by "the computer running slow this morning." Plenty of people I interact with fume because their e-mail is down, etc. So the comparison slighting cloud providers versus internal data centers may be inaccurate; however, it may be that the accuracy is in fact, unprovable, as many enterprises do not actually measure real-world SLA performance.

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Something else that should be noted is the absence of real-world "five nine" existence. If one looks at the Uptime Institute's definition of the various tiers of data center robustness, the scale goes from Tier 1, with single path of components and single points of failure, up to Tier 4, with multiple cooling equipment and power paths, along with redundant components. However, even Tier 4 achieves only 99.995% availability, below the "five nines" standard.

Moreover, there is an apples-to-oranges element of comparison here as well. The cloud providers not only deliver infrastructure (the domain of the Uptime Institute's definitions) but deliver software capability as well. In the case of Amazon, the software capability consists of hypervisors, storage management and cloud management software. Google and Microsoft layer additional platform functionality on top of what Amazon provides.

If one were to compare internal data center SLAs that incorporate the software layer as well as the infrastructure, one could hazard a guess that the cloud providers would start to look a lot better.

However, this mode of assessment is increasingly out of date, based as it is on a world of small amounts of expensive hardware running software designed to squeeze onto resource-constrained servers. That approach to system design is no longer leading edge. Why?

In Transaction Processing, written by Jim Gray and Andreas Reuter, the authors posit that hardware is far more reliable than software; consequently, the book spends no time on hardware strategy for figuring out how to raise uptime. Rather, the book discusses how to design software to be transaction-safe (hint: use a relational database that has transaction logging). Gray and Reuter assume a world of small amounts of hardware with applications spanning one or a few machines -- but that approach is being left behind in today's world of big data and massive applications.