Task Classification for Improving Scheduling and Resource Management in Cloud Computing

Abstract

In cloud computing users tasks come up with varied resource demands. But the resource planned is always higher than the actual requirement for the successful execution of a task. The majority of tasks may not consume the entire amount of resource allocated for its execution, thus leading to improper resource utilization and load imbalance thus experiencing high cloud maintenance costs. One way to address this issue is by having prior knowledge of resource requirements and characterizing the incoming tasks based on the resource requirement for efficient use of resources. Hence, the task classification model is proposed, which analyses the incoming tasks and categorizes them into different clusters based on workload using fuzzy clustering algorithm. Furthermore depending on the tasks’ CPU and memory requirement the clustered tasks are buffered as light, heavy, compute-intensive, and memory-intensive which benefits during the scheduling and allocation process. The result of the clustering is used in task scheduling and estimation of the actual resource required for successful task execution. The experimental results are compared with existing clustering algorithms and the proposed method proves to achieve increased resource savings.