Abstract
Due to the high power bills and the negative environmental impacts, workflow scheduling with energy consciousness has been an emerging need for modern heterogeneous computing systems. A number of approaches have been developed to find suboptimal schedules through heuristics by means of slack reclamation or trade-off functions. In this article, a memetic algorithm for energy-efficient workflow scheduling is proposed for a quality-guaranteed solution with high runtime efficiency. The basic idea is to retain the advantages of population-based, heuristic-based, and local search methods while avoiding their drawbacks. Specifically, the proposed algorithm incorporates an improved non-dominated sorting genetic algorithm (NSGA-II) to explore potential task priorities and allocates tasks to processors by an earliest finish time (EFT)-based heuristic to provide a time-efficient candidate. Then, a local search method integrated with a pruning technique is launched with a low possibility, to exploit the feasible region indicated by the candidate schedule. Experimental results on workflows from both randomly-generated and real-world applications suggest that the proposed algorithm achieves bi-objective optimization, improving makespan, and energy saving by 4.9% and 24.3%, respectively. Meanwhile, it has a low time complexity compared to the similar work HECS.
Highlights
A heterogeneous computing system (HCS) refers to a system that incorporates different types of processing units (PUs)
The main algorithm flowchart of the proposed memetic algorithm (MA)-dynamic voltage frequency scaling (DVFS) is demonstrated in Figure 2, which can be divided into three parts: First, a multi-objective evolutionary algorithm, e.g., NSGA-II, is employed as the main framework to explore a new solution space, as well as to evaluate individuals in each generation
The initial population consists of popSize individuals, in each of which the task segment can be randomly generated under precedence constraints while the rest of the segments can be filled by an earliest finish time (EFT)-based heuristic and the local search method described later
Summary
A heterogeneous computing system (HCS) refers to a system that incorporates different types of processing units (PUs). The heuristic-based algorithms normally have high runtime efficiency as they narrow down the search process to an extremely limited solution space by a set of efficient rule-based policies These rules have significant effects on the results, but are not likely to be consistent for a wide range of problems. The metaheuristic-based algorithms are less efficient because of the high computational cost generated by the incorporated combinatorial process, but they have demonstrated robust performance in various scheduling problems due to their power in searching more solution regions [8,9,10,11,12,13]. A baseline solution generated by a time-efficient scheduling algorithm is introduced as a good seed, as well as a direction for the evolutionary search, ensuring the bi-objective optimization to produce quality-guaranteed schedules.
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