Archive for May 11th, 2010

Intelligent Workload Management Myth or Mystery?

Virtualization Critical Evaluation – Chapter 15

Ever wonder why various firms have published only parts of their wondrous cloud designs?  The provisioning aspects, the infrastructure aspects, seem obvious no?  PXE, DHCP, TFTP, HTTPS, VLANs, PODs, switches, racks, chassis, blades, servers, remote lights out or control devices, out-of-band devices, etc., stateless operating systems, floating applications, node aware applications, etc. as services, grid application solutions as a primitive forms of cloud services?  The list goes on and on, but in reality everything discussed, is not new.  Not really a significant leap in design or concept.

What is not discussed is the work-load management aspect of the cloud.  In the last few years there have been or are a small number of patents on file that discuss concepts of and unique implications of resource dynamics, autonomous computing infrastructures.  Some describe types of intelligence applied to resource demand needs, availability needs, etc.  This would be the foundation of work-load-management for a cloud to be sure.  I leave it to the reader to do some basic resource on what is published from a patent perspective, but other than the hints of how work-load management might be implemented from a logical design perspective; there is very little information on how it is implemented at a function or tactical level.  Why work load management should be implemented is discussed at various points since the initial days of virtualization theory, or virtualization reality as we know it today.

Moreover, a number of firms have approached aspects of work-load management, usually from a control and reporting perspective that grows into a historical trending, capacity forecasting methodology.  A very few entities have tackled the predictive analysis aspects of work-load management.  No, VMware DRS is not predictive analysis, nor does VMware Capacity IQ have such.  This is not a stab at VMware, but an acknowledgement that even the 800 pound gorilla of virtualization, has trouble with the subject.  Predictive analysis is non-trivial, complex, and difficult to do, and to do it well, the use of a magic wand might be needed, and as far as I am concerned, strongly recommended.  Do disrespect to Wizards Guild intended.

If you want to give yourself a headache, Google workload management, and see what appears, then Google predictive analysis, and then, try to intersect the two topics. Some of the most interesting solutions that I have seen never appear in the joint Google index has returned.  It appears as though the two topics are mutually exclusive?  Ha!  No way.  Note, we are not discussing business analytics or business intelligence here, but cloud computing.  Business or application analytics are rules of logic that drive solution behavior at an application level, ignoring the infrastructure that the application runs on, with an expectation that said infrastructure is homogenous or quite uniform.  The grid technologies, even cluster technologies, and virtualized high-availability models of today are taken for granted by business application analytics, and are quite static compared to what a heterogeneous cloud should be able to do now or in the very near future, no?

So where does that leave us?  With more questions than answers?  Yet again?  Some might expect or decry that I have not rattled of 5 or 10 products, itemizing the good, the bad, or even the ugly aspects of each product?  Rating or even ranting about whatever.  Not this time.  This is a topic that any virtualization architecture, that has any integrity, should learn the old fashion way… do your own research.  That is the only way to appreciate the complexity of the subject, and difficulty of design, and stress to achieve implementation.  Intelligent workload management is a mystery still, but no longer a myth, the functional components exist, and can be leveraged.  However, getting square pegs into round holes requires some original thinking and unique effort.

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