The Community for Technology Leaders
RSS Icon
Issue No.10 - Oct. (2013 vol.62)
pp: 2108-2114
Menglan Hu , National University of Singapore, Singapore
Bharadwaj Veeravalli , National University of Singapore, Singapore
Grids have been extensively deployed to handle various scientific and engineering applications that can be structured as bag-of-tasks (BoT). The scheduling of BoT applications on Grids is an important issue for achieving high performance. Grid scheduling involves a number of challenging issues, mainly due to the dynamic nature of the Grid. To deal with this dynamic nature, in this paper, we propose an online scheduling algorithm called prudent algorithm with replication (PAR) for scheduling Grid applications. PAR is shown to prudently make scheduling decisions in such a way that it can tolerate inaccurate performance predictions. Another point to note is that PAR adopts task duplication as an attempt to reduce serious schedule increases. Moreover, since the applications to be performed may widely vary in terms of their required hardware and software, we also capture the loads' various processing requirements in our algorithms, a unique feature that is applicable for running proprietary applications only on certain eligible processing nodes. Thus, in our problem formulation each application can only be processed by certain processors as both the applications and processing nodes are heterogeneous. We then present a task selection policy, referred to as requirement-aware load selection (RALS) policy to handle the contention of multiple applications that have various processing requirements but share the same computing resources. Based on RALS and PAR, we develop two scheduling algorithms: requirement-aware prudent algorithm with replication (RAPAR), and requirement-aware knowledge-free algorithm with replication (RAKAR). RAPAR and RAKAR address the scheduling of multiple BoT applications with heterogeneous processing requirements on Grids. RAPAR works in scenarios where inaccurate performance prediction information is provided whereas RAKAR works without any prediction information. Performance evaluation results are presented to demonstrate the effectiveness and competitiveness of our approaches when compared to existing algorithms.
Program processors, Processor scheduling, Prediction algorithms, Scheduling, Resource management, Heuristic algorithms, Schedules, parallel computing, Task scheduling, grid computing, bag-of-tasks applications
Menglan Hu, Bharadwaj Veeravalli, "Requirement-Aware Scheduling of Bag-of-Tasks Applications on Grids with Dynamic Resilience", IEEE Transactions on Computers, vol.62, no. 10, pp. 2108-2114, Oct. 2013, doi:10.1109/TC.2012.164
[1] The Grid: Blueprint for a Future Computing Infrastructure, I. Foster and C. Kesselman, eds. Morgan Kaufmann, 1999.
[2] D.P. Anderson, J. Cobb, E. Korpela, M. Lebofsky, and D. Werthimer, "SETI@home: An Experiment in Public-Resource Computing," Comm. ACM, vol. 45, no. 11, pp. 56-61, 2002.
[3] Folding@Home, http:/, 2012.
[4] Rosetta@Home, http://boinc.bakerlab.orgrosetta/, 2012.
[5] Einstein@Home, http:/, 2012.
[6] D.P. Anderson and K. Reed, "Celebrating Diversity in Volunteer Computing," Proc. 42nd Hawaii Int'l Conf. System Sciences, 2009.
[7] A. Grama, A. Gupta, G. Karypis, and V. Kumar, Introduction to Parallel Computing, second ed. Addison Wesley, 2003.
[8] O.H. Ibarra and C.E. Kim, "Heuristic Algorithms for Scheduling Independent Tasks on Nonidentical Processors," J. ACM, vol. 24, no. 2, pp. 280-289, 1977.
[9] M. Maheswaran, S. Ali, H.J. Siegel, D. Hensgen, and R. Freund, "Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems," Proc. IEEE Eighth Heterogeneous Computing Workshop, pp. 30-44, 1999.
[10] H. Casanova, A. Legrand, D. Zagorodnov, and F. Berman, "Heuristics for Scheduling Parameter Sweep Applications in Grid Environments," Proc. Ninth Heterogeneous Computing Workshop, pp. 349-363, May 2000.
[11] E. Santos-Neto, W. Cirne, F. Brasileiro, and A. Lima, "Exploiting Replication and Data Reuse to Efficiently Schedule Data-Intensive Applications on Grids," Proc. 10th Workshop Job Scheduling Strategies for Parallel Processing, pp. 210-232, 2004.
[12] N. Fujimoto and K. Hagihara, "Near-Optimal Dynamic Task Scheduling of Independent Coarse-Grained Tasks onto a Computational Grid," Proc. Int'l Conf. Parallel Processing, pp. 391-398, 2003.
[13] W. Cirne, F. Brasileiro, D. Paranhos, L.F.W. Ges, and W. Voorsluys, "On the Efficacy, Efficiency and Emergent Behavior of Task Replication in Large Distributed Systems," Parallel Computing, vol. 33, no. 3, pp. 213-234, Apr. 2004.
[14] Y.C. Lee and A.Y. Zomaya, "Practical Scheduling of Bag-of-Tasks Applications on Grids with Dynamic Resilience," IEEE Trans. Computers, vol. 56, no. 6, pp. 815-825, June 2007.
[15] C. Anglano, J. Brevik, M. Canonico, D. Nurmi, and R. Wolski, "Fault-Aware Acheduling for Bag-of-Tasks Applications on Desktop Grids," Proc. IEEE/ACM Seventh Int'l Conf. Grid Computing, 2007.
[16] A. Legrand and C. Touati, "Non-Cooperative Scheduling of Multiple Bag-of-Task Applications," Proc. IEEE INFOCOM, 2007.
[17] C. Anglano and M. Canonico, "Scheduling Algorithms for Multiple Bag-of-Task Applications on Desktop Grids: A Knowledge-Free Approach," Proc. IEEE Int'l Symp. Parallel and Distributed Processing (IPDPS), 2008.
[18] A. Iosup, O. Sonmez, S. Anoep, and D. Epema, "The Performance of Bags-of-Tasks in Large-Scale Distributed Systems," Proc. Int'l Symp. High Performance Distributed Computing (HPDC '08), 2008.
[19] O. Beaumont, L. Carter, J. Ferrante, A. Legrand, L. Marchal, and Y. Robert, "Centralized versus Distributed Schedulers for Multiple Bag-of-Task Applications," IEEE Trans. Parallel Distributed Systems, vol. 19, no. 5, pp. 698-709, Apr. 2008.
[20] R. Bertin, A. Legrand, and C. Touati, "Toward a Fully Decentralized Algorithm for Multiple Bag-of-Tasks Application Scheduling on Grids," Proc. IEEE/ACM Int'l Conf. Grid Computing, pp. 118-125, 2008.
[21] A. Benoit, L. Marchal, J.F. Pineau, Y. Robert, and F. Vivien, "Scheduling Concurrent Bag-of-Tasks Applications on Heterogeneous Platforms," IEEE Trans. Computers, vol. 59, no. 2, pp. 202-217, Feb. 2010.
[22] H. Casanova, M. Gallet, and F. Vivien, "Non-Clairvoyant Scheduling of Multiple Bag-of-Tasks Applications," Proc. 16th Int'l Euro-Par Conf. Parallel Processing (EuroPar '10), pp. 168-179, 2010.
[23] D. Bertsimas and D. Gamarnik, "Asymptotically Optimal Algorithm for Job Shop Scheduling and Packet Routing," J. Algorithms, vol. 33, no. 2, pp. 296-318, 1999.
[24] R. Shah, B. Veeravalli, and M. Misra, "On the Design of Adaptive and Decentralized Load-Balancing Algorithms with Load Estimation for Computational Grid Environments," IEEE Trans. Parallel and Distributed Systems, vol. 18, no. 12, pp. 1675-1686, Dec. 2007.
[25] N. Loc and S. Elnaffar, "A Dynamic Scheduling Algorithm for Divisible Loads in Grid Environments," J. Comm., vol. 2, no. 4, pp. 57-64, June 2007.
[26] X. Qin and T. Xie, "An Availability-Aware Task Scheduling Strategy for Heterogeneous Systems," IEEE Trans. Computers, vol. 57, no. 2, pp. 188-199, Feb. 2008.
[27] L. Xiao, Y. Zhu, L.M. Ni, and Z. Xu, "Incentive-Based Scheduling for Market-Like Computational Grids," IEEE Trans. Parallel and Distributed Systems, vol. 19, no. 7, pp. 903-913, July 2008.
[28] Y. Yang and H. Casanova, "RUMR: Robust Scheduling for Divisible Workloads," Proc. IEEE Int'l Symp. High Performance Distributed Computing, pp. 114-123, June 2003.
14 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool