Evaluation of nine heuristic algorithms with data-intensive jobs and computing-intensive jobs in a dynamic environment


This study focuses on a dynamic environment where knowledge-intensive jobs and computing-intensive jobs are submitted to a grid at the same time. The authors analyse nine heuristic algorithms in a grid and give a comparison of them in a simulation setting. The nine heuristics are: (i) min-min, (ii) max-min, (iii) duplex, (iv) sufferage, (v) minimum execution time (MET), (vi) opportunistic load balancing (OLB), (vii) fast-match, (viii) best-match and (ix) adaptive scoring job scheduling (ASJS). Within the simulation, different ratios between the info-intensive jobs and computing-intensive jobs are used to analyze for the performance of the 9 heuristics below completely different arrival rates. Five parameters are used to estimate the performance of these methods. Those parameters include average execution time, average waiting time, the amount of finished jobs (FB), the total of file size that has been submitted to the grid (SFS) and the whole variety of directions of all finished jobs (SINI). Simulation results show that four out of the nine heuristics have relative sensible performance in the task scheduling in the grid systems. They're best-match, MET, ASJS and OLB.

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