Autonomic Clouds

Cloud computing continues to increase in complexity due to a number of factors: (i) increasing availability of configuration options from public Cloud providers (Amazon, for instance, offers over 4000 different configuration options), and (ii) increasing variability and types of application instances that can be deployed on such platforms, such as tuning options in hyper visors that enable different virtual machine instances to be associated with physical machines, storage, compute and I/O preferences that offer different power and price, and operating system configurations that provide differing degrees of security. This complexity can also be seen in enterprise scale data centers that dominate computing infrastructures in industry, which are growing in size and complexity, leading to complex business applications and workflows that Clouds are enabling. Autonomic computing offers self-capabilities that enable self management of systems. The underlying concepts and mechanisms of autonomics can be applied to each component within a Cloud system (resource manager/scheduler, power manager, etc.), or could be applied within an application that makes use of such a Cloud system. Understanding where such capability can be most effectively used is a decision variable often hard to fully appreciate -- an aspect explored in this tutorial.