Summary
Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing systems and represents an NP-complete problem. Therefore, using meta-heuristic algorithms is a suitable approach in order to cope with its difficulty. In many meta-heuristic algorithms, generating individuals in the initial step has an important effect on the convergence behavior of the algorithm and final solutions. Using some pure heuristics for generating one or more near-optimal individuals in the initial step can improve the final solutions obtained by meta-heuristic algorithms. Pure heuristics may be used solitary for generating schedules in many real-world situations in which using the meta-heuristic methods are too difficult or inappropriate. Different criteria can be used for evaluating the efficiency of scheduling algorithms, the most important of which are makespan and flowtime. In this paper, we propose an efficient pure heuristic method and then we compare the performance with five popular heuristics for minimizing makespan and flowtime in heterogeneous distributed computing systems. We investigate the effect of these pure heuristics for initializing simulated annealing meta-heuristic approach for scheduling tasks on heterogeneous environments.
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Performance Comparison of Six Efficient Pure Heuristics for Scheduling Meta-Tasks On Heterogeneous Distributed Environments
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1. IntroductionMixed-machine heterogeneous computing (HC) environments utilize a distributed suite of different high-performance machines, interconnected with high-speed links, to perform different computationally intensive applications that have diverse computational requirements [1, 2]. To exploit the different capabilities of a suite of heterogeneous resources, typically a resource management system (RMS) allocates the resources to the tasks and the tasks are ordered for execution on the resources. At a time interval in HC environment a number of tasks are received by RMS from different users. Different tasks have different requirements and different resources have different capabilities. Optimally scheduling is mapping a set of tasks to a set of resources to efficiently exploit the capabilities of such systems and is one of the key problems in HC environments. As mentioned in [3, 4], optimally mapping tasks to machines in an HC suite is an NP-com...See the full content of this document
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