Optimizing end-to-end performance of data-intensive computing pipelines in heterogeneous network environments

Journal of Parallel and Distributed Computing

Volumn 71, Issue 2, 2011, Pages 254-265

  Wu, Qishi ^a   Gu, Yi ^a  

^a University of Memphis ^*  (United States)

Author keywords

Data intensive computing; End to end delay; Frame rate; Performance optimization; Pipeline

Indexed keywords

DATA-INTENSIVE COMPUTING; END TO END DELAY; END-TO-END PERFORMANCE; EXISTING METHOD; EXTENSIVE SIMULATIONS; FRAME RATE; HETEROGENEOUS NETWORK ENVIRONMENTS; MAPPING PROBLEM; NETWORK CONSTRAINTS; NP-COMPLETENESS; OPTIMAL SOLUTIONS; OPTIMIZATION PROCEDURES; PERFORMANCE DATA; PERFORMANCE OPTIMIZATIONS; SCIENTIFIC APPLICATIONS; STREAMING APPLICATIONS;

COMPUTER SIMULATION; DYNAMIC PROGRAMMING; HETEROGENEOUS NETWORKS; HEURISTIC ALGORITHMS; OPTIMIZATION; PIPELINES;

DISTRIBUTED COMPUTER SYSTEMS;

EID: 78650417399     PISSN: 07437315     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jpdc.2010.08.003     Document Type: Article

References (28)

1. B. Agarwalla, N. Ahmed, D. Hilley, U. Ramachandran, Streamline: a scheduling heuristic for streaming application on the grid, in: Proc. of the 13th Multimedia Comp. and Net. Conf., San Jose, CA, 2006.
2. G. Ausiello, P. Crescenzi, G. Gambosi, V. Kann, A. Marchetti-Spaccamela, and M. Protasi Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties 1999 Springer Verlag
3. A. Bashir, V. Susarla, and K. Vairavan A statistical study of the performance of a task scheduling algorithm IEEE Trans. Comput. 32 12 1975 774 777
4. A. Benoit, and Y. Robert Mapping pipeline skeletons onto heterogeneous platforms J. Parallel Distrib. Comput. 68 6 2008 790 808 (Pubitemid 350269695)
5. S. Chatterjee, J. Strosnider, Distributed pipeline scheduling: end-to-end analysis of heterogeneous, multi-resource real-time systems, in: Proc. of the 15th Int. Conf. on Distributed Computing Systems, 1995, pp. 204211.
6. V. Chaudhary, and J. Aggarwal A generalized scheme for mapping parallel algorithms IEEE Trans. Parallel Distrib. Syst. 4 3 1993 328 346
7. L. Chen, G. Agrawal, Resource allocation in a middleware for streaming data, in: Proc. of the 2nd Workshop on Middleware for Grid Comp., Toronto, Canada, 2004.
8. G. Cheng, N. Ansari, Finding all hops shortest paths, IEEE Commun. Lett. 8 (2).
9. S. Choi, J. Turner, and T. Wolf Configuring sessions in programmable networks Proc. of IEEE INFOCOM 2001 60 66 (Pubitemid 32474222)
10. S. Choi, J. Turner, and T. Wolf Configuring sessions in programmable networks Int. J. Comput. Telecomm. Network. 41 2003 269 284 (Pubitemid 32474222)
11. R. Cohen, and G. Nakibli On the computational complexity and effectiveness of N-hub shortest-path routing Proc. of IEEE INFOCOM 2004
12. K. Elsayed, HCASP: a hop-constrained adaptive shortest-path algorithm for routing bandwidth-guaranteed tunnels in MPLS networks, in: Proc. of the 9th Int. Symp. on Computers and Communications, vol. 2, 2004, pp. 846851. (Pubitemid 40006278)
13. M. Ercan, and C. Ouz Performance of local search heuristics on scheduling a class of pipelined multiprocessor tasks J. Comput. Electr. Eng. 31 8 2005 537 555 (Pubitemid 43227261)
14. S. Fortune, J. Hopcroft, and J. Wyllie The directed subgraph homeomorphism problem Theoret. Comput. Sci. 10 1980 111 121
15. M. Garey, and D. Johnson Computers and Intractability: A Guide to the Theory of NP-Completeness 1979 W.H. Freeman and Company San Francisco
16. A. Gerasoulis, and T. Yang A comparison of clustering heuristics for scheduling DAGs on multiprocessors J. Parallel Distrib. Comput. 16 4 1992 276 291
17. R. Guerin, and A. Orda Computing shortest paths for any number of hops IEEE/ACM Trans. Network. 10 5 2002 613 620 (Pubitemid 35312140)
18. Y. Gu, Q. Wu, A. Benoit, Y. Robert, Optimizing end-to-end performance of distributed applications with linear computing pipelines, in: Proc. of the 15th Int. Conf. on Para. and Dist. Sys., Shenzhen, China, 2009.
19. S. Kim, and J. Browne A general approach to mapping of parallel computation upon multiprocessors architectures Proc. of Int. Conf. on Parallel Processing 1988 1 8
20. V. Kravtsov, P. Bar, D. Carmeli, A. Schuster, and M. Swain A scheduling framework for large-scale, parallel, and topology-aware applications J. Parallel Distrib. Comput. 70 9 2010 983 992
21. Y. Kwok, and I. Ahmad Dynamic critical-path scheduling: an effective technique for allocating task graph to multiprocessors IEEE Trans. Parallel Distrib. Syst. 7 5 1996 506 521 (Pubitemid 126782383)
22. C. McCreary, M. Cleveland, A. Khan, The problem with critical path scheduling algorithms, Tech. Rep., Auburn University, 1996.
23. F. Omara, and M. Arafa Genetic algorithms for task scheduling problem J. Parallel Distrib. Comput. 70 1 2010 13 22
24. B. Shirazi, M. Wang, and G. Pathak Analysis and evaluation of heuristic methods for static scheduling J. Parallel Distrib. Comput. 10 1990 222 232
25. Terascale Supernova Initiative (TSI). http://www.phy.ornl.gov/tsi.
26. Q. Wu, Y. Gu, M. Zhu, N. Rao, Optimizing network performance of computing pipelines in distributed environments, in: Proc. of the 22nd IEEE Int. Parallel and Distributed Processing Symp., Miami, Florida, 2008.
27. Q. Wu, N.S.V. Rao, On transport daemons for small collaborative applications over wide-area networks, in: Proc. of the 24th IEEE Int. Performance Computing and Communications Conf., Phoenix, AZ, 2005, pp. 159166. (Pubitemid 41729947)
28. Y. Zhu, and B. Li Overlay network with linear capacity constraints IEEE Trans. Parallel Distrib. Syst. 19 2008 159 173 (Pubitemid 41423275)