Deadline Aware Energy-Efficient Task Scheduling Model for a Virtualized Server

Abstract

The demand of cloud-based services is increasing day by day. To fulfill the seamless computing demand of consumers, a large number of servers are installed in the datacenters. These servers consume high energy. Reducing the energy consumption of servers with the same performance of service is a vital challenge. Energy-efficient task scheduling is one of the method to reduce energy consumption while preserving the task constraints. This paper presents a deadline and energy-aware scheduling (DEAS) model for a virtualized server to achieve energy efficiency while executing deadline conscious tasks. These tasks are independent in nature and arrives dynamically. The presented DEAS model follows the heuristic approach where the first instance guarantee ratio (GR) is maximized to raise the energy efficiency of the server per unit of work performed. Task slack time is utilized in an innovative way to improve the GR of the server. In this way, the average energy consumption (energy consumption per task) of the server per unit of work done is minimized. The energy efficiency of the server in its idle state is further achieved by applying core-level granularity of dynamic voltage and frequency scaling (DVFS) technology. The DEAS model is evaluated through extensive simulation experiments using the CloudSim simulator. Results show that DEAS model performs better than existing models on the account of considered performance metrics, i.e., GR, total energy consumption, energy consumption per task, and resource utilization.