Abstract

Rapid growth in data, maximum functionality and changing behavior tends the workload to be more complex. Organizations have complex type of workloads that is very difficult to manage by the humans and even in some cases, this management becomes impossible. Human experts take much time to get sufficient experience so that they can manage workload efficiently. The versatility in workload due to huge data size and requests (workload) lead us towards new challenges. One of the challenges is the identification of the problems queries and the decision about these, that is, whether to continue their execution or stop. The other challenge is how to characterize the workload, as good configuration, prediction and adoption is fully dependent on characterization of the workload. Correct and timely characterization leads to managing the workload in an efficient manner and vice versa. In this scenario, our objective is to produce such workload management strategy or framework that is fully autonomic. This paper provides some basis and achievements about the tools that exhibit autonomic computing (AC) in workload management with respect to self-characteristics. We have categorized the workload tools to these self-characteristics and identified their limitations. Finally the paper presents the research done in workload management tools with respect to workload type and autonomic computing.

Key words: Autonomic computing, workload, optimization, configuration, prediction, organization, adoption.