A Network Performance Aware QoS Based
Workflow Scheduling for Grid Services
Shinu
John1 and Maluk Mohamed2
1Department
of Computer Science and Engineering,
St Thomas
College of Engineering and Technology, India
2Department
of Computer Science and Engineering, MAM College of Engineering and Technology,
India
Abstract: Grids enable sharing,
selection and aggregation of geographically distributed resources among various
organizations. They are now emerging as promising computing paradigms for
resource and compute intensive scientific workflow applications modeled as a
Directed Acyclic Graph (DAG) with intricate inter-task dependencies. Job scheduling is an
important and challenging issue in a grid environment. There are various
scheduling algorithm proposed for grid environments to distribute the load
among processors and maximize resource utilization while reducing task
execution time. Task execution time is not the only parameter to be improved;
various Quality of Service (QoS)
parameters are also to be considered in job scheduling in grid computing. In
this Research we have studied the existing QoS based Task scheduling, work flow
scheduling and formulated the problem. The possible solutions are developed for
the problems identified in existing algorithms. The scheduling of dependent
task (work flow) is more challenging than independent task scheduling. The
scheduling of both dependent and independent tasks with satisfying QOS
requirements of users is a very challenging issue in grid computing. This paper
proposes a Novel Network aware QoS workflow scheduling method for Grid
Services. The proposed scheduling algorithm considers network and QoS
constraints. The goal of the proposed scheduling algorithm is to implement the
workflow schedule so that it reduces execution time and resource cost and yet meets
the deadline imposed by the user. The experimental result shows that the
proposed algorithm improves the success ratio of tasks and throughput of
resources while reducing makespan and workflow execution cost.
Keywords: Grid scheduling, QoS, DAG, execution
time, deadline, trust rate.
Received June 25, 2014; accepted September 7, 2015