Potential Finish Time and Min-mean Algorithm for allocating Meta-Tasks on distributed Computational Grid

Abstract

Grid is widely distributed and promising technology that enables the integrated and heterogeneous resource sharing for solving computationally challenging scientific engineering problems. In a distributed grid environment, allocating the tasks to the available computing resources proves complex and it is an NP-Complete problem as resources are geo-graphically distributed. In this paper presents a new task scheduling algorithm, called Potential Finish Time Min-mean Task Scheduling Algorithm (PFTSA), to enhance the selection of the suitable resources, which is responsible for the scheduling process. The proposed algorithm (PFTSA) schedules the tasks to the suitable resources by considering the potential finish time of the tasks, average execution time and waiting time of the tasks and average completion time of the resources. The proposed algorithm (PFTSA) results in minimum makespan as well as improved resource utilization. The experimental results indicate the PFTSA is a promising algorithm than the existing Min-min algorithm.

A Pair-Task Heuristic for Scheduling Tasks in Heterogeneous Multi-cloud Environment

“…We can control the resource allocation through task scheduling, which in turn improves the system's throughput. Task-cloud scheduling [4], [5] means mapping task to particular Vm, so the usage of resources improves.An innovative algorithm for task scheduling increases the overall e ciency of the system and allows service providers provide high QoS (Quality of Service).A signi cant position is played by cloud computing dealers.The list of Vms and its QoS is available to dealers.The strong QoS is allocated to a highly e cient Vm. Dealers receive the user's requests and the request is submitted to a Vm that satisfy the (SLA) Service Level Argument[6] and requirement of customer.QoS should not be decreased for the request or task during scheduling a speci c Vm.…”