Task Clustering-Based Energy-Aware Workflow Scheduling in Cloud Environment

Abstract

In the recent years, the efficient management of Cloud Data Center (CDC) resources (computation, storage, and network) is a complex task due to over provisioning and overloading. Furthermore, to form an energy-aware data center with negligible impact on the workload performance is also a critical issue because over provisioned and overload system consumes a considerable amount of electric power. The compute-intensive scientific applications generate complex workload structure (workflow) whose successful execution in terms of energy-performance trade-off is still at its beginning. For this, the simulators are used to analyze and understand the behavior of diverse workload in a distributed environment. WorkflowSim is a popular simulator for workflow processing because of it presents scientific workflows in Directed Cyclic Graphs (DAG) format and offers task clustering (horizontal clustering, vertical clustering, and balanced clustering) for load balancing. In this work, task clustering is used for load balancing and we proposed a Power-aware MAX-MIN VM placement algorithm. Dynamic Voltage Frequency Scaling (DVFS) technique with On-Demand governor and Conservative governor are integrated into Power-aware MAX-MIN VM placement algorithm for reducing power consumption. The implementation results show that energy consumption is reduced considerably in balance, On-Demand, and Conservative approaches with respect to Simple approach.