Chemical Reaction Optimization for the Grid Scheduling Problem

Abstract

Grid computing collects geographically dispersed resources ranging from laptops to supercomputers to compute tasks requested by clients. Grid scheduling, i.e., assigning tasks to resources, is an NPhard problem, and thus, metaheuristic methods are employed to find the optimal solutions. In this paper, we propose a Chemical Reaction Optimization (CRO) algorithm for the grid scheduling problem. CRO is a population-based metaheuristics mimicking the interactions between molecules in a chemical reaction. We compare the CRO approach with four generally acknowledged metaheuristics, and show that CRO performs the best.

Chemical Reaction Optimization: a tutorial

“…Interactions between CROs can be carried out at a certain instant without much restriction as each CRO does not need to wait for another CRO to complete certain actions, e.g. computation of the whole population in a generation in GA. An attempt to parallelize CRO can be found in [39].…”

“…In [18], the earliest version of CRO is compared with the variants of some popular evolutionary algorithms and CRO achieves superior performance in many test instances. In [39], a parallel version of CRO with a synchronous communication strategy is proposed to tackle the problem. The computation time and the solution quality of the parallel implementation are improved when compared to those of the sequential version of CRO.…”

“…Grid Scheduling Problem schedules tasks to resources so as to minimize computational overheads and to utilize the resources effectively. Several variants of CRO are proposed with different considerations of solution representation and priority of the elementary reactions [36,37]. CRO outperforms many existing evolutionary methods in most test cases.…”

Chemical Reaction Optimization for Task Scheduling in Grid Computing

“…The canonical framework of CRO [16] has been employed to solve the grid scheduling problem in [32]. Based on that, we develop a modified framework of CRO for the problem and its flowchart is shown in [31, Fig.…”

Maximally Distant Codes Allocation Using Chemical Reaction Optimization with Enhanced Exploration

“…The authors employed this technique in [11] to the population transition problem, to maximize the probability of universal streaming by manipulating population transition probability matrix, and reported better performance than many commonly used methods for controlling population transition in many practical live streaming systems. Researchers have applied this technique to many intractable problems in various fields, like solving the grid scheduling problem in [12], which compared it with four generally acknowledged methods, and reported superior performance, and scheduling Directed Acyclic Graph (DAG) jobs in heterogeneous computing systems, and a Double Molecular Structure-based CRO (DMSCRO) method as in [13], to encode the execution order of the tasks in a DAG job, and the task-tocomputing-node mapping, along with four elementary operations, and a fitness function suitable for DAG scheduling, and verified the effectiveness over a large set of randomly generated and real-world problems graphs, and testing the performance of CRO on three nondeterministic polynomial time hard combinatorial optimization problems reported in [10], claiming that it was very competitive with the existing metaheuristic, and outperformed them in some cases, like the real-world problem.…”