Workload Estimation for Improving Resource Management Decisions in the Cloud

In cloud computing, good resource management can benefit both cloud users as well as cloud providers. Workload prediction is a crucial step towards achieving good resource management. While it is possible to estimate the workloads of long-running tasks based on the periodicity in their historical workloads, it is difficult to do so for tasks which do not have such recurring workload patterns. In this paper, we present an innovative clustering based resource estimation approach which groups tasks that have similar characteristics into the same cluster. The historical workload data for tasks in a cluster are used to estimate the resources needed by new tasks based on the cluster(s) to which they belong. In particular, for a new task T, we measure T's initial workload and predict to which cluster(s) it may belong. Then, the workload information of the cluster(s) is used to estimate the workload of T. The approach is experimentally evaluated using Google dataset, including resource usage data of over half a million tasks. We develop a workload model based on the dataset which is then used to estimate the workload patterns of several randomly selected tasks from the trace log. The results confirm the effectiveness of this cluster-based method for estimating the resources required by each task.