Abstract
This paper addresses the problem of scheduling multi-user jobs on clusters, both homogeneous and heterogeneous. A user job is composed by a set of dependent tasks and it is described by a direct acyclic graph (DAG). The aim is to maximize the resource usage by allowing a floating mapping of processors to a given job, instead of the common mapping approach that assigns a fixed set of processors to a user for a period of time. The simulation results show a better cluster usage. The scheduling algorithm minimizes the total length of the schedule (makespan) of a given set of parallel jobs, whose priorities are represented in a DAG. The algorithm is presented as producing static schedules although it can be adapted to a dynamic behavior as discussed in the paper.
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References
Amoura, A.K., Bampis, E., König, J.-C.: Scheduling algorithms for parallel gaussian elimination with communication costs. IEEE Transactions on Parallel and Distributed Systems 9(7), 679–686 (1998)
Barbosa, J., Morais, C., Nobrega, R., Monteiro, A.P.: Static scheduling of dependent parallel tasks on heterogeneous clusters. In: Heteropar 2005, pp. 1–8. IEEE Computer Society, Los Alamitos (2005)
Barbosa, J., Padilha, A.J.: Algorithm-dependent method to determine the optimal number of computers in parallel virtual machines. In: Hernández, V., Palma, J.M.L.M., Dongarra, J. (eds.) VECPAR 1998. LNCS, vol. 1573, pp. 508–521. Springer, Heidelberg (1999)
Barbosa, J., Tavares, J., Padilha, A.J.: Linear algebra algorithms in a heterogeneous cluster of personal computers. In: Proceedings of 9th Heterogeneous Computing Workshop, pp. 147–159. IEEE CS Press, Los Alamitos (2000)
Berman, F., Wolski, R., Figueira, S., Schopf, J., Shao, G.: Application-level scheduling on distributed heterogeneous networks. In: Supercomputing 1996 (1996)
Blazewicz, J., Dell’Olmo, P., Drozdowski, M., Maczka, P.: Scheduling multiprocessor tasks on parallel processors with limited availability. European journal of Operational Research (149), 377–389 (2003)
Blazewicz, J., Machowiak, M., Weglarz, J., Kovalyov, M., Trystram, D.: Scheduling malleable tasks on parallel processors to minimize the makespan. Annals of Operations Research (129), 65–80 (2004)
Shivle, S., et al.: Mapping of subtasks with multiple versions in a heterogeneous ad hoc grid environment. In: Heteropar 2004. IEEE Computer Society, Los Alamitos (2004)
Kim, J.-K., et al.: Dynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environment. Journal of Parallel and Distributed Computing 67, 154–169 (2007)
Geijn, R., Watts, J.: Summa: Scalable universal matrix multiplication algorithm. Technical Report CS-95-286, University of Tennessee, Knoxville (1995)
Gerasoulis, A., Yang, T.: On the granularity and clustering of directed acyclic task graphs. IEEE Transactions on Parallel and Distributed Systems, 686–701 (June 1993)
Jansen, K.: Scheduling malleable parallel tasks: An asymptotic fully polynomial time approximation scheme. Algorithmica 39, 59–81 (2004)
Kwok, Y., Ahmad, I.: Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Computing Surveys 31(4), 406–471 (1999)
Kwok, Y., Ahmad, I.: On multiprocessor task scheduling using efficient state space search approaches. Journal of Parallel and Distributed Computing 65, 1515–1532 (2005)
Lepère, R., Mounié, G., Trystram, D.: An approximation algorithm for scheduling trees of malleable tasks. European journal of Operational Research (142), 242–249 (2002)
Oh-Heum, Chwa, K.-Y.: Scheduling parallel tasks with individual deadlines. Theoretical Computer Science 215, 209–223 (1999)
Park, G.-L.: Performance evaluation of a list scheduling algorithm in distributed memory multiprocessor systems. Future Generation Computer Systems (20), 249–256 (2004)
Shirazi, B., Wang, M., Pathak, G.: Analysis and evaluation of heuristic methods for static task scheduling. Journal of Parallel and Distributing Computing 10, 222–232 (1990)
Sinnen, O., Sousa, L.: List scheduling: extension for contention awareness and evaluation of node priorities for heterogeneous cluster architectures. Parallel Computing (30), 81–101 (2004)
Topcuoglu, H., Hariri, S., Wu, M.-Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems 13(3), 260–274 (2002)
Trystram, D.: Scheduling parallel applications using malleable tasks on clusters. In: 15th International Conference on Parallel and Distributed Processing Symposium (2001)
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Barbosa, J., Monteiro, A.P. (2008). A List Scheduling Algorithm for Scheduling Multi-user Jobs on Clusters. In: Palma, J.M.L.M., Amestoy, P.R., Daydé, M., Mattoso, M., Lopes, J.C. (eds) High Performance Computing for Computational Science - VECPAR 2008. VECPAR 2008. Lecture Notes in Computer Science, vol 5336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92859-1_13
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DOI: https://doi.org/10.1007/978-3-540-92859-1_13
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