Controlling energy without compromising system performance in mobile grid environments

The challenges confronting in mobile grid systems are: limited CPU power, limited memory, small screen, short battery life, and intermittent disconnection. Considering all these limitations, this paper is targeted to control energy consumption without compromising system's performance in mobile grid. In this paper, we focus on using the mobile devices on the mobile grid environment. Mobile devices can serve two important functions in mobile grid environment either as service consumer or as valuable service providers. The proposed approach is not only to reduce energy consumption, but also to improve system performance in mobile grid environment. Utility functions are used to express grid users' requirements, resource providers' benefit function and system's objectives. Dynamic programming is used to optimize the total utility function of mobile grid. A distributed controlling energy algorithm in mobile grid environment is proposed which decomposes mobile grid system optimization problem into sub-problems. In order to verify the efficiency of the proposed algorithm, in the experiment, the performance evaluation of controlling energy algorithm is conducted.

[1]  Richard Wolski,et al.  Analyzing Market-Based Resource Allocation Strategies for the Computational Grid , 2001, Int. J. High Perform. Comput. Appl..

[2]  Jen-Hung Huang,et al.  Price-Based Resource Allocation Strategies for Wireless Ad Hoc Networks with Transmission Rate and Energy Constraints , 2007, 2007 16th International Conference on Computer Communications and Networks.

[3]  Li Chunlin,et al.  Agent framework to support the computational grid , 2004 .

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

[5]  Li Shang,et al.  An Economics-based Power-aware Protocol for Computation Distribution in Mobile Ad-Hoc Networks , 2002, IASTED PDCS.

[6]  Ramin Yahyapour,et al.  Economic Scheduling in Grid Computing , 2002, JSSPP.

[7]  Krishnendu Chakrabarty,et al.  Real-time task scheduling for energy-aware embedded systems , 2001, J. Frankl. Inst..

[8]  Lin Chen,et al.  A Game Theoretic Framework of Distributed Power and Rate Control in IEEE 802.11 WLANs , 2008, 2007 IEEE International Conference on Network Protocols.

[9]  Karsten Schwan,et al.  Energy-Aware Mobile Service Overlays: Cooperative Dynamic Power Management in Distributed Mobile Systems , 2007, Fourth International Conference on Autonomic Computing (ICAC'07).

[10]  Hong Shen,et al.  Energy Balanced Data Gathering in WSNs with Grid Topologies , 2008, 2008 Seventh International Conference on Grid and Cooperative Computing.

[11]  Hossam S. Hassanein,et al.  Energy-aware task allocation over MANETs , 2005, WiMob'2005), IEEE International Conference on Wireless And Mobile Computing, Networking And Communications, 2005..

[12]  Ishfaq Ahmad,et al.  A Cooperative Game Theoretical Technique for Joint Optimization of Energy Consumption and Response Time in Computational Grids , 2009, IEEE Transactions on Parallel and Distributed Systems.

[13]  Li Chunlin,et al.  Multi economic agent interaction for optimizing the aggregate utility of grid users in computational grid , 2006, Applied Intelligence.

[14]  Li Chunlin,et al.  Joint QoS optimization for layered computational grid , 2007 .

[15]  Marty Humphrey,et al.  Mobile OGSI.NET: grid computing on mobile devices , 2004, Fifth IEEE/ACM International Workshop on Grid Computing.

[16]  G.C. Polyzos,et al.  Evaluation of scheduling policies in a Mobile Grid architecture , 2008, 2008 International Symposium on Performance Evaluation of Computer and Telecommunication Systems.

[17]  Nalini Venkatasubramanian,et al.  An energy-efficient middleware for supporting multimedia services in mobile grid environments , 2005, International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II.

[18]  Li Chunlin,et al.  A distributed utility-based two level market solution for optimal resource scheduling in computational grid , 2005 .

[19]  Layuan Li,et al.  Utility-based QoS optimisation strategy for multi-criteria scheduling on the grid , 2007, J. Parallel Distributed Comput..

[20]  Heonshik Shin,et al.  Selective Grid Access for Energy-Aware Mobile Computing , 2007, UIC.

[21]  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).