This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Fair Scheduling Algorithms in Grids
November 2007 (vol. 18 no. 11)
pp. 1630-1648
In this paper, we propose a new algorithm for fair scheduling, and we compare it to other scheduling schemes such as the Earliest Deadline First and the First Come First Serve schemes. Our algorithm uses a max-min fair sharing approach for providing fair access to users. When there is no shortage of resources, the algorithm assigns to each task enough computational power for it to finish within its deadline. When there is congestion, the main idea is to fairly reduce the CPU rates assigned to the tasks, so that the share of resources that each user gets is proportional to the user’s weight. The weight of a user may be defined as the user’s contribution to the infrastructure or the price he is willing to pay for services or any other socioeconomic consideration. In our algorithms, all tasks whose requirements are lower than their fair share CPU rate are served at their demanded CPU rates. However, the CPU rates of tasks whose requirements are larger than their fair share CPU rate are reduced to fit the total available computational capacity in a fair manner.Three different versions of fair scheduling are adopted in this paper; the Simple Fair Task Order (SFTO), which schedules the tasks according to their respective fair completion times, the Adjusted Fair Task Order (AFTO), that refines the SFTO policy by ordering the tasks using the adjusted fair completion times, and the Max-min Fair Share (MMFS) scheduling policy, which simultaneously addresses the problem of finding a fair task order and assigning a processor to each task based on a Max-Min fair sharing policy. Experimental results and comparisons with traditional scheduling schemes, such as the Earliest Deadline First (EDF) and the First Come First Served (FCFS) are presented using three different error criteria. Validation of the simulations using real experiments of tasks generated from 3D image rendering processes is also provided. The three proposed scheduling schemes can be integrated into existing Grid computing architectures

[1] I. Foster, C. Kesselman, and S. Tuecke, “The Anatomy of the Grid: Enabling Scalable Virtual Organizations,” Int'l J. Supercomputer Applications, vol. 15, no. 3, 2001.
[2] W. Leinberger and V. Kumar, “Information Power Grid: The New Frontier in Parallel Computing,” IEEE Concurrency, vol. 7, no. 4, pp. 75-84, Oct.-Dec. 1999.
[3] “Scheduling Working Group of the Grid Forum,” Document: 10.5, Sept. 2001.
[4] I. Foster and C. Kesselman, “Globus: A Metacomputing Infrastructure Toolkit,” Int'l J. Supercomputer Applications, vol. 11, no. 2, pp. 115-128, 1997.
[5] J. Basney, M. Livny, and T. Tannenbaum, “High Throughput Computing with Condor,” High Performance Computer Unit (HPCU) News, vol. 1, no. 2, June 1997.
[6] D. Thain, T. Tannenbaum, and M. Livny, “Condor and the Grid,” Grid Computing: Making the Global Infrastructure a Reality, F.Berman, A.J.G. Hey, and G. Fox, eds., John Wiley & Sons, 2003.
[7] J. Frey, T. Tannenbaum, I. Foster, M. Livny, and S. Tuecke, “Condor-G: A Computation Management Agent for Multi-Institutional Grids,” J. Cluster Computing, vol. 5, pp. 237-246, 2002.
[8] A.S. Grimshaw, M.A. Humphrey, and A. Natrajan, A Philosophical and Technical Comparison of Legion and Globus. Corp. Riverton, 2004.
[9] D. Abramson, J. Giddy, and L. Kotler, “High Performance Parametric Modeling with Nimrod/G: Killer Application for the Global Grid,” Proc. Int'l Parallel and Distributed Processing Symp. (IPDPS '00), 2000.
[10] D. Abramson, I. Foster, J. Giddy, A. Lewis, R. Sosic, R.R. Sutherst, and N. White, “Nimrod Computational Workbench: A Case Study in Desktop Metacomputing,” Proc. Australian Computer Science Conf. (ACSC '97), Feb. 1997.
[11] R. Buyya, D. Abramson, and J. Giddy, “Nimrod/G: An Architecture for a Resource Management and Scheduling System in a Global Computational Grid,” Proc. Fourth Int'l Conf. High Performance Computing in Asia-Pacific Region, 2000.
[12] F. Berman, A. Chien, K. Cooper, J. Dongarra, I. Foster, D. Gannon, L. Johnsson, K. Kennedy, C. Kesselman, J. Mellor-Crummey, D. Reed, L. Torczon, and R. Wolski, “The GrADS Project: Software Support for High-Level Grid Application Development,” Int'l J.High Performance Computing Applications, vol. 15, no. 4, pp. 327-344, Winter, 2001.
[13] H. Dail, H. Casanova, and F. Berman, “A Decoupled Scheduling Approach for the GrADS Environment,” Proc. Conf. Supercomputing (SC '02), Nov. 2002.
[14] R. Wolski, J.S. Plank, J. Brevik, and T. Bryan, “G-commerce: Market Formulations Controlling Resource Allocation on the Computational Grid,” Proc. Int'l Parallel and Distributed Processing Symp. (IPDPS '01), Apr. 2001.
[15] S.M. Jackson, “Allocation Management with QBank,” white paper, technical report in Pacific Northwest Nat'l Laboratories, 2000.
[16] T. Hacker and W. Thigpen, “Distributed Accounting on the Grid,” Grid Forum Working Draft, 2007.
[17] M.S. Fineberg and O. Serlin, “Multiprogramming for Hybrid Computation,” Proc. Int'l Federation for Information Processing Societies (IFIPS) Fall Joint Computer Conf., 1967.
[18] J.A. Stankovic, et al. “Implications of Classical Scheduling Results for Real Time Systems,” Computer, pp. 16-25, June 1995.
[19] M.L. Dertouzos and A.K.-L. Mok, “Multiprocessor On-Line Scheduling for Hard Real Time Tasks,” IEEE Trans. Software Eng., pp. 1497-1506, Dec. 1989.
[20] G. Manimaran, C.S.R. Murthy, M. Vijay, and K. Ramamritham, “New Algorithms for Resource Reclaiming from Precedence Constrained Tasks in Multiprocessor Real-Time Systems,” J.Parallel and Distributed Computing, vol. 44, no. 2, pp. 123-132, Aug. 1997.
[21] K. Ramamritham, J.A. Stankovic, and P.-F. Shiah, “Efficient Scheduling Algorithms for Real-Time Multiprocessor Systems,” IEEE Trans. Parallel and Distributed Systems, vol. 1, no. 2, pp. 184-194, Apr. 1990.
[22] W. Zhao, K. Ramamritham, and J.A. Stankovic, “Scheduling Tasks with Resource Requirements in Hard Real Time Systems,” IEEE Trans. Software Eng., vol. 12, no. 3, pp. 360-369, May 1990.
[23] J.Y-T. Leung and M.L. Merrill, “A Note on Preemptive, Scheduling of Periodic, Real-Time Tasks,” Information Processing Letters, pp. 115-118, Nov. 1980.
[24] X. Deng, N. Gu, T. Brecht, and K.-C. Lu, “Preemptive Scheduling of Parallel Jobs on Multiprocessors,” SIAM J. Computing, vol. 30, no. 1, pp. 145-160, 2000.
[25] G. Manimaran and C.S.R. Murthy, “An Efficient Dynamic Scheduling Algorithm for Multiprocessor Real-Time Systems,” IEEE Trans. Parallel and Distributed Systems, vol. 9, no. 3, pp. 312-319, Mar. 1998.
[26] L.E. Jackson and G.N. Rouskas, “Deterministic Preemptive Scheduling of Real Time Tasks,” Computer, vol. 35, no. 5, pp. 72-79, May 2002.
[27] W. Zhang, B. Fang, H. He, H. Zhang, and M. Hu, “Multisite Resource Selection and Scheduling Algorithm on Computational Grid,” Proc. 18th Parallel and Distributed Processing Symp., pp. 105-115, 2004.
[28] S. Zhuk, A. Chernykh, A. Avetisyan, S. Gaissaryan, D. Grushin, N. Kuzjurin, A. Pospelov, and A. Shokurov, “Comparison of Scheduling Heuristics for Grid Resource Broker,” Proc. IEEE Fifth Mexican Int'l Conf. Computer Science, pp. 388-392, 2004.
[29] D.P. Spooner, S.A. Jarvis, J. Cao, S. Saini, and G.R. Nudd, “Local Grid Scheduling Techniques Using Performance Prediction,” IEE Proc. Computers and Digital Techniques, vol. 150, no. 2, pp. 87-96, Mar. 2003.
[30] S. Kim and J.B. Weissman, “A Genetic Algorithm Based Approach for Scheduling Decomposable Data Grid Applications,” Proc. Int'l Conf. Parallel Processing (ICPP '04), pp. 406-413, 2004.
[31] R. Jain, A Survey of Scheduling Methods. Nokia Research Center, Sept. 1997.
[32] M. Hawa, “Stochastic Evaluation of Fair Scheduling with Applications to Quality-of-Service in Broadband Wireless Access Networks,” PhD dissertation, Univ. of Kansas, Aug. 2003.
[33] I. Ahmad, Y.-K. Kwok, M.-Y. Wu, and K. Li, “Experimental Performance Evaluation of Job Scheduling and Processor Allocation Algorithms for Grid Computing on Metacomputers,” Proc. IEEE 18th Int'l Parallel and Distributed Processing Symp. (IPDPS '04), pp. 170-177, 2004.
[34] A.K. Parekh and R.G. Gallager, “A Generalized Processor Sharing Approach to Flow Control in Integrated Services Networks: The Single-Node Case,” IEEE/ACM Trans. Networking, vol. 1, no. 3, pp.344-357, 1993.
[35] A. Demers, S. Keshav, and S. Shenker, “Design and Analysis of a Fair Queuing Algorithm,” Proc. ACM SIGCOMM '89, Sept. 1989.
[36] D. Bertsekas and R. Gallager, Data Networks, second ed., section starting on p. 524. Prentice Hall, 1992.
[37] N. Doulamis, A. Doulamis, A. Panagakis, K. Dolkas, T. Varvarigou, and E. Varvarigos, “A Combined Fuzzy-Neural Network Model for Non-Linear Prediction of 3D Rendering Workload in Grid Computing,” IEEE Trans. Systems Man and Cybernetics, Part-B, vol. 34, no. 2, pp. 1235-1247, Apr. 2004.
[38] J. Turner, “Terabit Burst Switching,” J. High Speed Networks, vol. 8, no. 1, pp. 3-16, 1999.
[39] Y. Xiong, M. Vandenhoute, and H.C. Cankaya, “Control Architecture in Optical Burst-Switched WDM Networks,” IEEE J.Selected Areas in Comm., vol. 18, pp. 1838-1851, 2000.
[40] S. Keshav, An Engineering Approach to Computer Networking. Addison-Wesley, 1997.
[41] D.S. Johnson, “Fast Algorithms for Bin Packing,” J. Computer and System Sciences, vol. 8, pp. 272-314, 1974.

Index Terms:
Grid Computing, Fair Grid Scheduling
Citation:
Nikolaos Doulamis, Emmanouel Varvarigos1, Theodora Varvarigou, "Fair Scheduling Algorithms in Grids," IEEE Transactions on Parallel and Distributed Systems, vol. 18, no. 11, pp. 1630-1648, Nov. 2007, doi:10.1109/TPDS.2007.1053
Usage of this product signifies your acceptance of the Terms of Use.