Network Reconfiguration Algorithm (NRA) for scheduling communication-intensive graphs in heterogeneous computing environment

Abstract

Distributed environments are widely used for computing complex applications modeled as task graphs. The computer network becomes more complex when the compute nodes are heterogeneous, however by choosing the appropriate network communication links for communication between a pair of compute tasks can enhance the computing efficiency(called network reconfiguration). One of the steps in heterogeneous network reconfiguration problem is mapping the application task graph edges on the network links. High Performance Computing (HPC) systems are usually heterogeneous, therefore mapping task graph edges on the communication links should consider the two factors: communication cost of task graph edges and the communication capability of network links. The system performance enhances if tasks are mapped on the compute nodes based on the computational costs of the tasks and the processing capability of compute nodes in addition to the edge scheduling on network links. In our earlier algorithm, Heterogeneous Edge and Task Scheduling (HETS) both edge and task mapping simultaneously improve the execution performance of task graphs. The proposed Network Reconfiguration Algorithm (NRA) minimizes the communication overhead and optimizes the schedule length with contention-aware model. NRA reduces an attribute Kirchhoff Index (KI) for optimal network reconfiguration providing minimum execution time. Both synthesized and task graphs of real applications are used for evaluation. The simulation results prove the efficiency of NRA in terms of average schedule length, schedule length ratio, speedup and system throughput. Comparisons with the baseline algorithms show that NRA provides 36% improved results specially for communication-intensive applications.

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Notes

  1. 1.

    http://www.lsst.org/lsst/. Accessed August 10, 2019.

  2. 2.

    http://glaros.dtc.umn.edu/gkhome/metis/metis/overview. Accessed August 10, 2019.

  3. 3.

    https://pegasus.isi.edu/documentation/workflowgallery.php. Accessed August 26, 2018.

  4. 4.

    https://pegasus.isi.edu/documentation/workflowgallery.php. Accessed August 26, 2018.

References

  1. 1.

    Kelly, S.M., Brightwell, R.: Software architecture of the light weight kernel, catamount. In: Proceedings of the 2005 Cray User Group Annual Technical Conference, pp. 16–19 (2005)

  2. 2.

    Foster, I., Zhao, Y., Raicu, I., Lu, S.: Cloud computing and grid computing 360-degree compared. In: IEEE 2008 Grid Computing Environments Workshop, November 2008

  3. 3.

    Shiralkar, G., Fleming, G., Watts, J., Wong, T., Coats, B., Mossbarger, R., Robbana, E., Batten, A.: Development and field application of a high performance, unstructured simulator with parallel capability. In: SPE Reservoir Simulation Symposium. Society of Petroleum Engineers (2005)

  4. 4.

    Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Elsevier, Amsterdam (2003)

    Google Scholar 

  5. 5.

    Kofler, K., Grasso, I., Cosenza, B., Fahringer, T.: An automatic input-sensitive approach for heterogeneous task partitioning. In: Proceedings of the 27th international ACM Conference on Supercomputing. ACM Press, New York (2013)

  6. 6.

    Hackett, A., Ajwani, D., Ali, S., Kirkland, S., Morrison, J.P.: A network configuration algorithm based on optimization of Kirchhoff index. In: IEEE 27th International Symposium on Parallel and Distributed Processing, May 2013

  7. 7.

    Ahmad, S.G., Liew, C.S., Rafique, M.M., Munir, E.U., Khan, S.U.: Data-intensive workflow optimization based on application task graph partitioning in heterogeneous computing systems. In: 2014 IEEE Fourth International Conference on Big Data and Cloud Computing, December 2014

  8. 8.

    Ahmad, S.G., Liew, C.S., Munir, E.U., Ang, T.F., Khan, S.U.: A hybrid genetic algorithm for optimization of scheduling workflow applications in heterogeneous computing systems. J. Parallel Distrib. Comput. 87, 80–90 (2016)

    Article  Google Scholar 

  9. 9.

    Zheng, W., Qin, Y., Bugingo, E., Zhang, D., Chen, J.: Cost optimization for deadline-aware scheduling of big-data processing jobs on clouds. Fut. Gen. Comput. Syst. 82, 244–255 (2018)

    Article  Google Scholar 

  10. 10.

    Masood, A., Munir, E.U., Rafique, M.M., Khan, S.U.: HETS: Heterogeneous edge and task scheduling algorithm for heterogeneous computing systems. In: IEEE 17th International Conference on High Performance Computing and Communications (2015)

  11. 11.

    He, T., Stankovic, J., Lu, C., Abdelzaher, T.: SPEED: a stateless protocol for real-time communication in sensor networks. In: IEEE 23rd International Conference on Distributed Computing Systems (2003)

  12. 12.

    Sow, D., Biem, A., Blount, M., Ebling, M., Verscheure, O.: Body sensor data processing using stream computing. In: Proceedings of the International Conference on Multimedia Information Retrieval. ACM Press, New York (2010)

  13. 13.

    Rixner, Scott, : Stream Processor Architecture. Springer, Berlin (2001)

    Google Scholar 

  14. 14.

    Buck, I., Foley, T., Horn, D., Sugerman, J., Fatahalian, K., Houston, M., Hanrahan, P.: Brook for GPUs. In: ACM SIGGRAPH. ACM Press, New York (2004)

  15. 15.

    Muthukrishnan, S.: Data streams: algorithms and applications. Found. Trends Theor. Comput. Sci. 1(2), 117–236 (2005)

    MathSciNet  Article  Google Scholar 

  16. 16.

    Al-Fares, M., Loukissas, A., Vahdat, A.: A scalable, commodity data center network architecture. ACM SIGCOMM Comput. Commun. Rev. 38(4), 63 (2008)

    Article  Google Scholar 

  17. 17.

    Kamil, S., Oliker, L., Pinar, A., Shalf, J.: Communication requirements and interconnect optimization for high-end scientific applications. IEEE Trans. Parallel Distrib. Syst. 21(2), 188–202 (2010)

    Article  Google Scholar 

  18. 18.

    Topcuoglu, H., Hariri, S., Wu, M.-Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)

    Article  Google Scholar 

  19. 19.

    Munir, E.U., Mohsin, S., Hussain, A., Nisar, M.W., Ali, S.: SDBATS: a novel algorithm for task scheduling in heterogeneous computing systems. In: 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and PhD Forum. May 2013

  20. 20.

    Verma, A., Kaushal, S.: A hybrid multi-objective particle swarm optimization for scientific workflow scheduling. Parallel Comput. 62, 1–19 (2017)

    MathSciNet  Article  Google Scholar 

  21. 21.

    Moon, Y., Yu, H., Gil, J.-M., Lim, J.: A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments. Hum. Centric Comput. Inf. Sci. 7(6), 1–12 (2017)

    Google Scholar 

  22. 22.

    Ahmad, S.G., Munir, E.U., Nisar, W.: PEGA: a performance effective genetic algorithm for task scheduling in heterogeneous systems. In: 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems. IEEE, June 2012

  23. 23.

    Jain, A., Sanyal, S., Das, S., Biswas, R.: “Fastmap: a distributed scheme for mapping large scale applications onto computational grids. In: Proceedings of the IEEE Second International Workshop on Challenges of Large Applications in Distributed Environments (2004)

  24. 24.

    Ajwani, D., Ali, S., Morrison, J.P.: Graph partitioning for reconfigurable topology. In: 2012 IEEE 26th International Parallel and Distributed Processing Symposium, May 2012

  25. 25.

    Pellegrini, F.: Contributions to Multilevel Parallel Graph Partitioning. LaBRI, Universit Bordeaux, Bordeaux (2009)

    Google Scholar 

  26. 26.

    Lasalle, D., Karypis, G.: Multi-threaded graph partitioning. In: 2013 IEEE 27th International Symposium on Parallel and Distributed Processing, May 2013

  27. 27.

    The 10th DIMACS implementation challenge graph partitioning and clustering (Online). http://www.cc.gatech.edu/dimacs10/

  28. 28.

    Walshaw, C.: The graph partitioning archive (Online). http://staffweb.cms.gre.ac.uk/c.walshaw/partition/

  29. 29.

    Lugones, D., Katrinis, K., Collier, M.: A reconfigurable optical/electrical interconnect architecture for large-scale clusters and datacenters. In: Proceedings of the 9th Conference on Computing Frontiers. ACM Press, New York (2012)

  30. 30.

    Alawneh, L., Rawashdeh, E., Al-Ayyoub, M., Jararweh, Y.: GPU parallelization of sequence segmentation using information theoretic models. Simul. Model. Pract. Theory 86, 11–24 (2018)

    Article  Google Scholar 

  31. 31.

    Garg, R., Mittal, M., Son, L.H.: Reliability and energy efficient workflow scheduling in cloud environment. Clust. Comput. (2019). https://doi.org/10.1007/s10586-019-02911-7

  32. 32.

    Maurya, A.K., Tripathi, A.K.: On benchmarking task scheduling algorithms for heterogeneous computing systems. J. Supercomput. 74(7), 3039–3070 (2018)

    Article  Google Scholar 

  33. 33.

    Tariq, R., Aadil, F., Malik, M.F., Ejaz, S., Khan, M.U., Khan, M.F.: Directed acyclic graph based task scheduling algorithm for heterogeneous systems. In: Advances in Intelligent Systems and Computing. Springer, Cham, pp. 936–947 (2018)

  34. 34.

    Walters, J.P., Chaudhary, V., Cha, M., S.G. Jr., Gallo, S.: A comparison of virtualization technologies for HPC. In: IEEE 22nd International Conference on Advanced Information Networking and Applications (AINA 2008) (2008)

  35. 35.

    Wu, Z., Zhang, S., Wang, T.: A cooperative particle swarm optimization with constriction factor based on simulated annealing. Computing 100(8), 861–880 (2018)

    MathSciNet  Article  Google Scholar 

  36. 36.

    Liu, D., Sui, X., Li, L., Jiang, Z., Wang, H., Zhang, Z., Zeng, Y.: A cloud service adaptive framework based on reliable resource allocation. Fut. Gener. Comput. Syst. 89, 455–463 (2018)

    Article  Google Scholar 

  37. 37.

    Melab, N., Zomaya, A.Y., Chakroun, I.: Parallel optimization using/for multi and many-core high performance computing. J. Parallel Distrib. Comput. 112, 109–110 (2018)

    Article  Google Scholar 

  38. 38.

    Khan, Z.A.: Comparison of Dijkstra’s algorithm with other proposed algorithms. https://doi.org/10.13140/RG.2.2.22743.88480 (2016)

  39. 39.

    Mamun, A.-A., Rajasekaran, S.: An efficient minimum spanning tree algorithm. In: 2016 IEEE Symposium on Computers and Communication (ISCC), June 2016

  40. 40.

    Ahmad, S.G., Liew, C.S., Rafique, M.M., Munir, E.U.: Optimization of data-intensive workflows in stream-based data processing models. J. Supercomput. 73(9), 3901–3923 (2017)

    Article  Google Scholar 

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Correspondence to Saima Gulzar Ahmad.

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Masood, A., Gulzar Ahmad, S., Ullah Khan, H. et al. Network Reconfiguration Algorithm (NRA) for scheduling communication-intensive graphs in heterogeneous computing environment. Cluster Comput 23, 1419–1438 (2020). https://doi.org/10.1007/s10586-019-03002-3

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Keywords

  • Heterogeneous parallel systems
  • Network reconfiguration
  • Edge scheduling
  • Kirchhoff Index