Non-evolutionary algorithm for scheduling dependent tasks in distributed heterogeneous computing environments

The Problem of obtaining an optimal matching and scheduling of interdependent tasks in distributed heterogeneous computing (DHC) environments is well known to be an NP-hard problem. In a DHC system, task execution time is dependent on the machine to which it is assigned and task precedence constraints are represented by a directed acyclic graph. Recent research in evolutionary techniques has shown that genetic algorithms usually obtain more efficient schedules that other known algorithms. We propose a non-evolutionary random scheduling (RS) algorithm for efficient matching and scheduling of inter-dependent tasks in a DHC system. RS is a succession of randomized task orderings and a heuristic mapping from task order to schedule. Randomized task ordering is effectively a topological sort where the outcome may be any possible task order for which the task precedent constraints are maintained. A detailed comparison to existing evolutionary techniques (GA and PSGA) shows the proposed algorithm is less complex than evolutionary techniques, computes schedules in less time, requires less memory and fewer tuning parameters. Simulation results show that the average schedules produced by RS are approximately as efficient as PSGA schedules for all cases studied and clearly more efficient than PSGA for certain cases. The standard formulation for the scheduling problem addressed in this paper is Rm|prec|C"m"a"x.,

[1]  Anthony A. Maciejewski,et al.  Task Matching and Scheduling in Heterogenous Computing Environments Using a Genetic-Algorithm-Based Approach , 1997, J. Parallel Distributed Comput..

[2]  Yves Robert,et al.  Parallel Gaussian elimination on an MIMD computer , 1988, Parallel Comput..

[3]  R. Lyman Ott.,et al.  An introduction to statistical methods and data analysis , 1977 .

[4]  Anthony Skjellum,et al.  A High-Performance, Portable Implementation of the MPI Message Passing Interface Standard , 1996, Parallel Comput..

[5]  Martin Grajcar.  Strengths and weaknesses of genetic list scheduling for heterogeneous systems , 2001, Proceedings Second International Conference on Application of Concurrency to System Design.

[6]  Ishfaq Ahmad,et al.  Efficient Scheduling of Arbitrary TAsk Graphs to Multiprocessors Using a Parallel Genetic Algorithm , 1997, J. Parallel Distributed Comput..

[7]  Y.-K. Kwok,et al.  Static scheduling algorithms for allocating directed task graphs to multiprocessors , 1999, CSUR.

[8]  Salim Hariri,et al.  Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing , 2002, IEEE Trans. Parallel Distributed Syst..

[9]  Edward A. Lee,et al.  A Compile-Time Scheduling Heuristic for Interconnection-Constrained Heterogeneous Processor Architectures , 1993, IEEE Trans. Parallel Distributed Syst..

[10]  R. F. Freund,et al.  Dynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systems , 1999, J. Parallel Distributed Comput..

[11]  R. Lyman Ott,et al.  Introduction to Statistical Methods and Data Analysis (with CD-ROM) , 2006 .

[12]  Ladislau Bölöni,et al.  A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems , 2001, J. Parallel Distributed Comput..

[13]  Imtiaz Ahmad,et al.  An Integrated Technique for Task Matching and Scheduling onto Distributed Heterogeneous Computing Systems , 2002, J. Parallel Distributed Comput..

[14]  S. Ranka,et al.  Applications and performance analysis of a compile-time optimization approach for list scheduling algorithms on distributed memory multiprocessors , 1992, Proceedings Supercomputing '92.

[15]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .

[16]  Sung-Bong Yang,et al.  Task scheduling in distributed computing systems with a genetic algorithm , 1997, Proceedings High Performance Computing on the Information Superhighway. HPC Asia '97.