Abstract
Grid computing is a computer network in which many resources and services are shared for performing a specific task. The term grid appeared in the mid-1990s and due to the computational capabilities, efficiency and scalability provided by the shared resources, it is used nowadays in many areas, including business, e-libraries, e-learning, military applications, medicine, physics, and genetics. In this paper, we propose WorkStealing-Grid Cost Dependency Matrix (WS-GCDM) which schedule DAG tasks according to their data transfer cost, dependency between tasks and load of the available resources. WS-GCDM algorithm is an enhanced version from GCDM algorithm. WS-GCDM algorithm balances load between all the available resources in grid system unlike GCDM which uses specific number of resources regardless how many resources are available. WS-GCDM introduces better makespan than GCDM algorithm and enhances system performance from 13% up to 17% when we experiment algorithms using DAG with dependent tasks.
Highlights
Importance of grid computing comes from the need to access resources which are geographically distributed and cannot be moved or duplicated to the same location
Assigning tasks to processors /machines is an important issue as it improves the performance of the whole job so that our concern will be on scheduling resources in grid computing
We describe our proposed algorithm and how the WorkStealing-Grid Cost Dependency Matrix (WS-Grid costs and dependence matrix (GCDM)) balance task scheduling between the available resources
Summary
Importance of grid computing comes from the need to access resources which are geographically distributed and cannot be moved or duplicated to the same location. Assigning tasks to processors /machines is an important issue as it improves the performance of the whole job so that our concern will be on scheduling resources in grid computing. There are number of factors, which can affect the grid application performance; load balancing is one of the most critical features of Grid infrastructure. Scheduling tasks in grid computing is critical as it influences the execution of the whole application. The problem of mapping tasks in grid computing is to find proper assignment of tasks to the available processors in order to optimize system utilization and load balancing and to minimize execution time [4]. We describe our proposed algorithm and how the WS-GCDM balance task scheduling between the available resources.
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