The autonomic puzzle

WHEN IBM REBRANDED eLiza last month, she was given a cute send-off. "In a self-configuring transformation of historical proportions," the announcement read, "Project eLiza of IBM self-managing IT infrastructure fame, is now known as the IBM autonomic computing initiative."

In the flurry of white papers that swirled around this event, the four mantras of IBM's autonomic vision -- self-configuring, self-healing, self-optimizing, self-protecting -- were used everywhere, consistently. But it was hard to avoid concluding that "autonomic" for IBM has become what ".Net" is for Microsoft: an umbrella marketing term that encompasses everything and nothing in particular.

Is "autonomic" just a label for good ideas and best practices that have been floating around for a long time in both IBM and non-IBM products? Are IBM DB2's self-tuning features notably different from those in other enterprise databases? We put these questions to Alan Ganek, IBM's vice president for autonomic computing.

According to Ganek, IBM has defined a set of architectural principles for autonomic computing. "We define the attributes of autonomic managers for resource elements," he says, "and we lay out the notion of sensors and effectors for each of those elements, and the reference model for monitoring, analyzing, planning, and executing change." The lingo is deliberately biological: sensors, effectors, and feedback loops are the tools used by the autonomic nervous system to maintain homeostasis, or dynamic equilibrium. Using these same tools to keep computers and networks healthy is a great idea. And like many great ideas, it's been around for a while.

"This is a journey," admits Michael Zisman, general manager of IBM's storage software unit, "and it didn't start last year when we began using the word autonomic." The current marketing push signals a concentrated effort to change the slope of the curve. He cites the Linux-based virtualization engine in the forthcoming StorageTank product, which isolates the system administrator from storage, as an enabler of self-configuring, self-healing capabilities. Ganek cites DB2's new Configuration Advisor, which analyzes its environment, and, he says, delivers recommendations that rival IBM's best human experts, and can double the throughput of some customer DBAs.

Other IBM brands are singing in the same choir. The Tivoli Risk Manager uses a DB2-like expert-system model to correlate intrusions and recommend how to handle them. WebSphere 5.0 is expected to announce self-configuring and self-tuning features today.

It's a journey, not a destination, we agree. But along the way, we wonder if IBM is creating more than a methodology of sensors, effectors, and control loops. Was the Intelligent Resource Director for IBM's zSeries mainframes, for example, based on a reusable expert-systems engine that's also driving the autonomic features of DB2, Tivoli, WebSphere, and StorageTank?

Apparently not. Some of the correlation engines may have broader applicability, Ganek says, but for now, they're specialized and domain-specific. "Look, 20 percent of this is about advanced cognitive kinds of things," he says, "and 80 percent is the infrastructure. If you don't have good instrumentation, if you don't know what the system is doing, it's hard to ask some analytic engine to tell you what to do about it."