Distributed Energy-Efficient Scheduling for Data-Intensive Applications with Deadline Constraints on Data Grids

• Published in: 2008 IEEE International Performance, Computing and Communications Conference pp. 26 - 33
• Main Authors: Cong Liu, Xiao Qin, Kulkarni, S., Chengjun Wang, Shuang Li, Manzanares, A., Baskiyar, S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2008
• Subjects: Degradation; Dispatching; Energy consumption; Energy dissipation; Energy efficiency; Energy storage; Large-scale systems; Peer to peer computing; Processor scheduling; Throughput
• ISBN: 1424433681; 9781424433681
• ISSN: 1097-2641
• DOI: 10.1109/PCCC.2008.4745123

Although data duplications may be able to improve the performance of data-intensive applications on data grids, a large number of data replicas inevitably increase energy dissipation in storage resources on the data grids. In order to implement a data grid with high energy efficiency, we address in this study the issue of energy-efficient scheduling for data grids supporting real-time and data-intensive applications. Taking into account both data locations and application properties, we design a novel Distributed Energy-Efficient Scheduler (or DEES for short) that aims to seamlessly integrate the process of scheduling tasks with data placement strategies to provide energy savings. DEES is distributed in the essence - it can successfully schedule tasks and save energy without knowledge of a complete grid state. DEES encompasses three main components: energy-aware ranking, performance-aware scheduling, and energy-aware dispatching. By reducing the amount of data replications and task transfers, DEES effectively saves energy. Simulation results based on a real-world trace demonstrate that with respect to energy consumption, DEES conserves over 35% more energy than previous approaches without degrading the performance.