Jian Peng

Bio: Jian Peng is an academic researcher from Sichuan University. The author has contributed to research in topics: User requirements document & Dynamic priority scheduling. The author has an hindex of 2, co-authored 2 publications receiving 5 citations. Previous affiliations of Jian Peng include University of Maryland, College Park.

A Grid Resource Scheduling Algorithm Based on the Utility Optimization

Abstract: To solve the problem of heterogeneity of user requirements in grid resource allocation, a grid resource scheduling algorithm based on utility function is proposed by analyzing the relationship between the executing time and cost and the user utility function, the theory of economics is used to solve the optimal problem of the user utility function. The result of experiment shows, when the system finished the same set of gridlets, the algorithm achieves better performance not only in cost than the algorithm based on the time optimization when they spent equal time, but also in time than the algorithm based on the cost optimization on the assumption that they consumed the equal quantity of cost.

A Grid Resource Scheduling Algorithm Based on the Utility Optimization

Abstract: To solve the problem of heterogeneity of user requirements in grid resource allocation, a grid resource scheduling algorithm based on utility function is proposed by analyzing the relationship between the executing time and cost and the user utility function, the theory of economics is used to solve the optimal problem of the user utility function. The result of experiment shows, when the system finished the same set of gridlets, the algorithm achieves better performance not only in cost than the algorithm based on the time optimization when they spent equal time, but also in time than the algorithm based on the cost optimization on the assumption that they consumed the equal quantity of cost.

Dwelling-time based resource scheduling algorithm using fuzzy logic in grid computing

Abstract: Providing efficient resource allocation in the grid computing is a major task. In this paper, a simple and proficient fuzzy based resource allocation algorithm is proposed that not only provides efficient allocation of resources but also ensures high utilization of the resources which are dynamic. The proposed technique constitutes of three different stages namely classification of grid resources, generation of fuzzy rules, and resource allocation based on those fuzzy rules. In the first stage, the grid resource is classified on the basis of dwelling time. In the second stage, the fuzzy rules are developed to allocate the resources to the particular job. In the third & final stage of the technique, the resources are allocated to the submitted jobs based on the generated fuzzy rules. The performance of the proposed algorithm is evaluated on the basis of utilization, failure rate and makespan, and is compared with conventional scheduling algorithms like First Come First Serve (FCFS), Round robin and Random. The results shows that the proposed fuzzy based resource allocation of resource and thereby improving the performance.

A Review of Resource Allocation and Task scheduling for Computational Grids based on Meta-heuristic Function

Abstract: The current scenario of grid computing faced a problem of job failure and increase of execution time of jobs. The failure of job degraded the performance of grid computing. The failure and increase execution time depend on resource allocation and task scheduling of job in grid computing. The proper sharing of resource of grid computing are related to distributes the system workload based on the processing elements capacity which leads to Minimize the overall job mean response time and maximize the system utilization and throughput at the working mode. The reduction of failure and task scheduling of resource need an optimization of process of allocation and execution of task. Various authors proposed a different optimization technique for process scheduling in grid computing such as ant colony optimization, genetic algorithm and fuzzy logic. These entire optimization factors reduce the rate of failure and execution time of grid. In this paper we discuss various method of grid computing related to resource allocation and task scheduling based on meta-heuristic function.