Energy-aware grid resource scheduling: model and algorithm

Energy efficiency for high-performance computing and communication systems has recently become an important concern, but most current grid environments do not implement energy-aware resource management. This paper proposes an energy-aware grid resource scheduling scheme. Energy-aware grid resource scheduling optimisation is formulated as utility optimisation. The goal of the paper is not only to reduce energy consumption, but also to improve the system utility in the grid environment, ensuring the battery lifetime and the deadlines of the grid applications. To reduce the computational complexity, we decompose the energy-aware grid resource scheduling optimisation problem into two sub-problems; the interaction between the two sub-problems is controlled through the use of the pricing variable. The paper proposes an energy-aware grid resource scheduling optimisation algorithm. The performance evaluation of the algorithm is conducted by comparing with other related algorithms.

[1]  Chaitali Chakrabarti,et al.  High-level power management of embedded systems with application-specific energy cost functions , 2006, 2006 43rd ACM/IEEE Design Automation Conference.

[2]  Xiao Qin,et al.  Energy-Efficient Scheduling for Parallel Applications Running on Heterogeneous Clusters , 2007, 2007 International Conference on Parallel Processing (ICPP 2007).

[3]  Rami G. Melhem,et al.  Energy Efficient Configuration for QoS in Reliable Parallel Servers , 2005, EDCC.

[4]  Hakan Aydin,et al.  Energy-constrained scheduling for weakly-hard real-time systems , 2005, 26th IEEE International Real-Time Systems Symposium (RTSS'05).

[5]  Venkata Durga Kiran Kasula.  Performance Analysis of Layered Architecture to integrate Mobile Devices and Grid Computing with a Resource Scheduling Algorithm , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[6]  Anthony A. Maciejewski,et al.  Dynamic mapping in energy constrained heterogeneous computing systems , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[7]  Theodora Varvarigou,et al.  MOBILE GRID COMPUTING: CHANGES AND CHALLENGES OF RESOURCE MANAGEMENT IN A ΜOBILE GRID ENVIRONMENT , 2003 .

[8]  Rajkumar Buyya,et al.  Power Aware Scheduling of Bag-of-Tasks Applications with Deadline Constraints on DVS-enabled Clusters , 2007, Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07).

[9]  Emmanouel A. Varvarigos,et al.  Adjusted fair scheduling and non-linear workload prediction for QoS guarantees in grid computing , 2007, Comput. Commun..

[10]  Xiao Qin,et al.  Solving Energy-Latency Dilemma: Task Allocation for Parallel Applications in Heterogeneous Embedded Systems , 2006, 2006 International Conference on Parallel Processing (ICPP'06).

[11]  Frank Kelly,et al.  Rate control for communication networks: shadow prices, proportional fairness and stability , 1998, J. Oper. Res. Soc..

[12]  Venkata Durga.  Performance Analysis of Layered Architecture to Integrate Mobile Devices and Grid computing with a resource scheduling algorithm , 2007 .

[13]  Dimitrios Skoutas,et al.  Efficient task replication and management for adaptive fault tolerance in Mobile Grid environments , 2007, Future Gener. Comput. Syst..

[14]  Claudio Scordino,et al.  Energy-Efficient Real-Time Heterogeneous Server Clusters , 2006, 12th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'06).