Academia.eduAcademia.edu

Outline

An approach to enhance the efficiency of opportunistic grids

2011, Concurrency and Computation: Practice and Experience

Abstract

Opportunistic grid computing middleware has as a main concern the need to preserve the performance of the local applications running on machines that donate resources to the grid. This concern, together with the fact that it happens in an extremely dynamic environment, causes the adoption of a treatment based on the best-effort principle for grid applications. This means that efficient application management schemes are usually not employed, which results in less than optimal performance as grid applications often need to be restarted due to (often temporary) resource claims by local user applications. This paper presents a method to improve the performance of grid applications, taking into account resource usage profiles for local applications, trying to identify when such resource claims are temporary and avoiding costly actions such as the migration of grid tasks. The approach is proposed as an extension to the InteGrade middleware and its evaluation shows promising results for the efficient management of grid applications.

References (23)

  1. Kondo D, Taufer M, Brooks C, Casanova H, Chien A. Characterizing evaluating desktop grids: An empirical study. Parallel and Distributed Processing Symposium, Santa Fe, NM, U.S.A., 2004.
  2. Cirne W, Brasileiro F, Andrade N, Costa L, Andrade A, Novaes R, Mowbray M. Labs of the world, unite!!! Journal of Grid Computing 2006; 4(3):225-246.
  3. Goldchleger A, Kon F, Goldman A, Finger M, Bezerra GC. Integrade: Object-oriented grid middleware leveraging idle computing power of desktop machines. Concurrency and Computation: Practice and Experience 2004; 16:449-459.
  4. Finger M, Bezerra GC, Conde DR. Resource use pattern analysis for predicting resource availability in opportunistic grids. Concurrency and Computation: Practice and Experience 2010; 22(3):295-313.
  5. Choi S. Group-based adaptive scheduling mechanism in desktop grid. PhD Thesis, Department of Computer Science and Engineering Graduate School, Korea University, June 2007.
  6. Wolski R, Spring N, Hayes J. The network weather service: A distributed resource performance forecasting service for metacomputing. Journal of Future Generation Computing Systems 1999; 15(5-6):757-768.
  7. Yang L, Foster I, Schopf J. Homeostatic and tendency-based CPU load predictions. International Parallel and Distributed Processing Symposium (IPDPS2003), Nice, France, 2003.
  8. de Mello RF, Yang LT. Prediction of dynamical, nonlinear, and unstable process behavior. The Journal of Supercomputing 2008; 49:22-41.
  9. Wu M, Sun X. Self-adaptive task allocation and scheduling of meta-tasks in non-dedicated heterogeneous computing. International Journal of High Performance Computing and Networking 2004; 2(2):186-197.
  10. Vadhiyar SS, Dongarra JJ. A performance oriented migration framework for the grid. Cluster Computing and the Grid 2003. Proceedings of the Third IEEE/ACM International Symposium on CCGrid, Tokyo, Japan, 2003; 130-137.
  11. Litzkow M, Livny M, Mutka M. Condor-A hunter of idle workstations. Proceedings of the Eighth International Conference of Distributed Computing Systems, San Jose, CA, U.S.A., vol. 43, 1988.
  12. Mutka M, Livny M. The available capacity of a privately owned workstation environment. Performance Evaluation 1991; 12(4):269-284.
  13. Harchol Balter M, Downey A. Exploiting process lifetime distributions for dynamic load balancing. ACM Transactions on Computer Systems (TOCS) 1997; 15(3):253-285.
  14. Dinda P. The statistical properties of host load. Scientific Programming 1999; 7(3):211-229.
  15. Arabie P, Hubert L, de Soete G. Clustering and Classification. World Scientific Publishing Company Inc.: Singapore, 1996.
  16. Maia R, Cerqueira R, Cosme R. OiL: An object request broker in the Lua language. Tools Session of the Brazilian Symposium on Computer Networks (SBRC2006), Curitiba, Paraná, Brazil, vol. 5, 2006.
  17. Ierusalimschy R, De Figueiredo L, Filho W. Lua-An extensible extension language. Software Practice and Experience 1996; 26(6):635-652.
  18. Brose G. JacORB: Implementation and design of a Java ORB. Proceedings of Distributed Applications and Interoperable Systems, Cottbus, Germany, 1997; 143-154.
  19. de Camargo R, Kon F, Cerqueira R. Strategies for checkpoint storage on opportunistic grids. IEEE Distributed Systems Online 2006; 7(9):1-1.
  20. de Camargo R, Goldchleger A, Kon F, Goldman A. Checkpointing BSP parallel applications on the InteGrade Grid middleware. Software Focus 2006; 18(6):567-579.
  21. Kaeli D. Issues in trace-driven simulation. Performance Evaluation of Computer and Communication Systems (Lecture Notes in Computer Science, vol. 729), Donatiello L, Nelson R (eds). Springer: Berlin/Heidelberg, 1993; 224-244.
  22. de Camargo R, Goldchleger A, Kon F, Goldman A. Checkpointing-based rollback recovery for parallel applications on the integrade grid middleware. Proceedings of the Second Workshop on Middleware for Grid Computing. ACM: New York, NY, U.S.A., 2004; 35-40.
  23. de Camargo R, Kon F, Goldman A. Portable checkpointing and communication for BSP applications on dynamic heterogeneous Grid environments. SBAC-PAD'05: The 17th International Symposium on Computer Architecture and High Performance Computing, Citeseer, 2005; 226-233.