An Integrated Technique for Task Matching and Scheduling onto Distributed Heterogeneous Computing Systems

Abstract

This paper presents a problem-space genetic algorithm (PSGA)-based technique for efficient matching and scheduling of an application program that can be represented by a directed acyclic graph, onto a mixed-machine distributed heterogeneous computing (DHC) system. PSGA is an evolutionary technique that combines the search capability of genetic algorithms with a known fast problem-specific heuristic to provide the best-possible solution to a problem in an efficient manner as compared to other probabilistic techniques. The goal of the algorithm is to reduce the overall completion time through proper task matching, task scheduling, and inter-machine data transfer scheduling in an integrated fashion. The algorithm is based on a new evolutionary technique that embeds a known problem-specific fast heuristic into genetic algorithms (GAs). The algorithm is robust in the sense that it explores a large and complex solution space in smaller CPU time and uses less memory space as compared to traditional GAs. Consequently, the proposed technique schedules an application program with a comparable schedule length in a very short CPU time, as compared to GA-based heuristics. The paper includes a performance comparison showing the viability and effectiveness of the proposed technique through comparison with existing GA-based techniques.

A Novel Genetic Algorithm for Static Task Scheduling in Distributed Systems

“…A program can be considered as a set of tasks and can be modeled as a Weighted Directed Acyclic Graph as below: WDAG = (T, <, E, D) [13], where…”

“…Fig. 2 The Base Genetic Algorithm (BGA) [13] In the third step, at long as the stop criterion, i.e. the number of generations, is not satisfied, the while loop will be repeated.…”

“…al [13] has used a fixed number of generations (N g ) for each WDAG with any number of tasks. This is a problem in the above algorithm because if the number of tasks is small, there is no need to tolerate high computation time of the algorithm, and if the number of tasks in WDAG is too large, it is possible that the number of generations and the number of iterations are not enough to find an optimal or sub-optimal solution.…”

“…There are many researches under the topics of solving the static task scheduling using GAs in the multiprocessor systems [3,8,10,11,12] and the distributed systems [3,5,6,13,14]. In this paper, a novel GA is presented which has a good ability to solve the above problem using the simulated annealing.…”

Low Complexity Performance Effective Task Scheduling Algorithm for Heterogeneous Computing Environments

“…Because of its key importance on performance, the task scheduling problem in general has been extensively studied and various heuristics have been proposed in the literature [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] . These heuristics are classified into a variety of categories such as list scheduling algorithms, clustering algorithms, guided random search methods and task duplication based algorithms.…”