Abstract
The Area of a schedule Σ for a dag
measures the rate at which Σ renders
’s nodes eligible for execution. Specifically, AREA(Σ) is the average number of nodes that are eligible for execution as Σ executes
node by node. Extensive simulations suggest that, for many distributions of processor availability and power, schedules having larger Areas execute dags faster on platforms that are dynamically heterogeneous: their processors change power and availability status in unpredictable ways and at unpredictable times. While Area-maximal schedules exist for every dag, efficient generators of such schedules are known only for well-structured dags. We prove that the general problem of crafting Area-maximal schedules is NP-complete, hence likely computationally intractable. This situation motivates the development of heuristics for producing dag-schedules that have large Areas. We build on the Sidney decomposition of a dag to develop a polynomial-time heuristic, Sidney, whose schedules have quite large Areas. (1) Simulations on dags having random structure indicate that Sidney’s schedules have Areas: (a) at least 85% of maximal; (b) at least 1.25 times larger than those produced by previous heuristics. (2) Simulations on dags having the structure of random “LEGO
®” dags indicate that Sidney’s schedules have Areas that are at least 1.5 times larger than those produced by previous heuristics. The “85%” result emerges from an LP-based formulation of the Area-maximization problem. (3) Our results on random dags are roughly matched by a second heuristic that emerges directly from the LP formulation.
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Keywords
- Completion Time
- Linear Program Formulation
- Desktop Grid
- Total Weighted Completion Time
- Dynamic Heterogeneity
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Bender, M.A., Phillips, C.A.: Scheduling DAGs on asynchronous processors. In: 19th ACM Symp. on Parallel Algorithms and Architectures, pp. 35–45 (2007)
Boutammine, S.-S., Millot, D., Parrot, C.: An Adaptive Scheduling Method for Grid Computing. In: Nagel, W.E., Walter, W.V., Lehner, W. (eds.) Euro-Par 2006. LNCS, vol. 4128, pp. 188–197. Springer, Heidelberg (2006)
Casanova, H., Dufossé, F., Robert, Y., Vivien, F.: Scheduling parallel iterative applications on volatile resources. In: 25th IEEE Int’l Parallel and Distributed Processing Symp. (2011)
Chekuri, C., Motwani, R.: Precedence constrained scheduling to minimize sum of weighted completion times on a single machine. Discrete Applied Math. 98, 29–38 (1999)
Cordasco, G., De Chiara, R., Rosenberg, A.L.: On scheduling DAGs for volatile computing platforms: Area-maximizing schedules. J. Parallel and Distr. Computing 72, 1347–1360 (2012)
Cordasco, G., De Chiara, R., Rosenberg, A.L.: An AREA-oriented heuristic for scheduling DAGs on volatile computing platforms (2013) (submitted for publication), See also Assessing the Computational Benefits of AREA-Oriented DAG-Scheduling. In: Jeannot, E., Namyst, R., Roman, J. (eds.) Euro-Par 2011, Part I. LNCS, vol. 6852, pp. 180–192. Springer, Heidelberg (2011)
Cordasco, G., Malewicz, G., Rosenberg, A.L.: Applying IC-scheduling theory to some familiar computations. In: Wkshp. on Large-Scale, Volatile Desktop Grids (2007)
Cordasco, G., Rosenberg, A.L.: On scheduling series-parallel DAGs to maximize AREA. In: Int’l J. Foundations of Computer Science (to appear, 2014)
Estrada, T., Taufer, M., Reed, K.: Modeling job lifespan delays in volunteer computing projects. In: 9th IEEE Int’l Symp. on Cluster, Cloud, and Grid Computing (2009)
Gallo, G., Grigoriadis, M.D., Tarjan, R.E.: A fast parametric maximum flow algorithm and applications. SIAM J. Comput. 18, 30–55
Georgiou, C., Kowalski, D.R.: Performing dynamically injected tasks on processes prone to crashes and restarts. In: Peleg, D. (ed.) DISC 2011. LNCS, vol. 6950, pp. 165–180. Springer, Heidelberg (2011)
González-Escribano, A., van Gemund, A., Cardeñoso-Payo, V.: Mapping unstructured applications into nested parallelism. In: High Performance Computing for Computational Sci. (2002)
Hall, R., Rosenberg, A.L., Venkataramani, A.: A comparison of DAG -scheduling strategies for Internet-based computing. In: 21st IEEE Int’l Parallel and Distr. Processing Symp. (2007)
Isard, M., Prabhakaran, V., Currey, J., Wieder, U., Talwar, K., Goldberg, A.: Quincy: Fair scheduling for distributed computing clusters. In: ACM Symp. on Operating Systs. Principles (2009)
Kondo, D., Casanova, H., Wing, E., Berman, F.: Models and scheduling mechanisms for global computing applications. In: 16th Int’l Parallel and Distr. Processing Symp. (2002)
Korpela, E., Werthimer, D., Anderson, D., Cobb, J., Lebofsky, M.: SETI@home: massively distributed computing for SETI. In: Dubois, P.F. (ed.) Computing in Science and Engineering. IEEE Computer Soc. Press (2000)
Lawler, E.L.: Sequencing jobs to minimize total weighted completion time subject to precedence constraints. Annals of Discrete Math. 2, 75–90 (1978)
Lombardi, M.: Robust scheduling of task graphs under execution time uncertainty. IEEE Trans. Computers 62, 98–111 (2013)
Millot, D.: Scheduling on unspecified heterogeneous distributed resources. In: IEEE Int’l Symp. on Parallel and Distributed Processing: Wkshps. and PhD Forum, pp.45–56 (2011)
Malewicz, G., Foster, I., Rosenberg, A.L., Wilde, M.: A tool for prioritizing DAGMan jobs and its evaluation. J. Grid Computing 5, 197–212 (2007)
Malewicz, G., Rosenberg, A.L., Yurkewych, M.: Toward a theory for scheduling sc DAGs in Internet-based computing. IEEE Trans. Comput. 55, 757–768 (2006)
Nurmi, D., Wolski, R., Brevik, J.: Model-based checkpoint scheduling for volatile resource environments. In: Cluster 2005 (2005)
Policella, N.: Scheduling with uncertainty: A proactive approach using partial order schedules. AI Communications 18, 165–167 (2005)
Radulescu, A., van Gemund, A.J.C.: On the complexity of list scheduling algorithms for distributed memory systems. In: 13th Int’l Conf. on Supercomputing, pp.68–75 (1999)
Rosenberg, A.L.: On scheduling mesh-structured computations for Internet-based computing. IEEE Trans. Comput. 53, 1176–1186 (2004)
Sarkar, V.: Partitioning and Scheduling Parallel Programs for Multiprocessors. MIT Press, Cambridge (1989)
Sidney, J.B.: Decomposition algorithms for single-machine sequencing with precedence relations and deferral costs. Operations Res. 23(2), 283–298 (1975)
Smith, W.: Various optimizers for single-stage production. Naval Res. Logistics Quart. 3, 59–66 (1956)
Topcuoglu, H., Hariri, S., Wu, M.Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel and Distr. Systs. 13, 260–274 (2002)
Woeginger, G.J.: On the approximability of average completion time scheduling under precedence constraints. Discr. Appl. Math. 131, 237–252 (2003)
Yao, S., Lee, H.-H.S.: Using mathematical modeling in provisioning a heterogeneous cloud computing environment., pp. 55–62. IEEE Computer (August 2011)
Zaharia, M., Konwinski, A., Joseph, A.D., Katz, R., Stoica, I.: Improving MapReduce performance in heterogeneous environments. In: 7th USENIX Symp. on Operating System Design and Implementation (2008)
Zheng, W.: A monte-carlo approach for full-ahead stochastic DAG-scheduling. In: 26th IEEE Int’l Parallel and Distributed Processing Symp.: Wkshps. and PhD Forum, pp. 99–112 (2012)
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Roche, S.T., Rosenberg, A.L., Rajaraman, R. (2014). On Constructing DAG-Schedules with Large AREAs. In: Silva, F., Dutra, I., Santos Costa, V. (eds) Euro-Par 2014 Parallel Processing. Euro-Par 2014. Lecture Notes in Computer Science, vol 8632. Springer, Cham. https://doi.org/10.1007/978-3-319-09873-9_52
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