A novel deadline and budget constrained scheduling heuristics for computational grids

The conventional deadline and budget constrained （DBC） scheduling heuristics for economic-based computational grids does not take the inconsistency of grid heterogeneity into account, which can lead to decline of application completion ratios. Motivated by this fact, a novel DI3C scheduling heuristics was proposed to deal with sequential workflow applications. In order to valuate the inconsistency, the relative cost （RC） metric was introduced, which was used to indicate the task-starving degree for resources. The new algorithm assigns tasks to resources, considering completion time, budget and RC together. The GridSim toolkit and the benchmark suites of the standard performance evaluation corporation （SPEC） were used to simulate the heterogeneous grid environment and applications. The experimental results show that the task and workflow completion ratios of the new heuristics are higher than those of the conventional heuristics.

Intelligent Deer Hunting Optimization Based Grid Scheduling Scheme

The grid environment is a dynamic,heterogeneous,and changeable computing system that distributes various services amongst different clients.To attain the benefits of collaborative resource sharing in Grid computing,a novel and proficient grid resource management system(RMS)is essential.Therefore,detection of an appropriate resource for the presented task is a difficult task.Several scientists have presented algorithms for mapping tasks to the resource.Few of them focus on fault tolerance,user fulfillment,and load balancing.With this motivation,this study designs an intelligent grid scheduling scheme using deer hunting optimization algorithm(DHOA),called IGSS-DHOA which schedules in such a way that the makespan gets minimized in the grid platform.The IGSS-DHOA technique is mainly based on the hunting nature of humans toward deer.It also derives an objective function with candidate solution(schedule)as input and the outcome is the makespan value denoting the quality of the candidate solution.The simulation results highlighted the supremacy of the IGSS-DHOA technique over the recent state of art techniques with the minimal average processing cost of 31717.9.

A Parallel Genetic Simulated Annealing Hybrid Algorithm for Task Scheduling

In this paper combined with the advantages of genetic algorithm and simulated annealing, brings forward a parallel genetic simulated annealing hybrid algorithm （PGSAHA） and applied to solve task scheduling problem in grid computing. It first generates a new group of individuals through genetic operation such as reproduction, crossover, mutation, etc, and than simulated anneals independently all the generated individuals respectively. When the temperature in the process of cooling no longer falls, the result is the optimal solution on the whole. From the analysis and experiment result, it is concluded that this algorithm is superior to genetic algorithm and simulated annealing.