Cloud computing is a prominent way to support demand on services. It is a mode of computing where scalable resources are delivered as a service to customers over Internet. Scheduling in cloud is responsible for selection of best suitable resources for task execution, by taking some parameters and restrictions of tasks into consideration. From the users view, efficient scheduling may provide factors like fast service, minimum task execution cost etc. On the other hand Service providers should gain factors like to maximum profit, utilize their service efficiently and importantly regular customers. This paper proposes an efficient scheduling algorithm which addresses these major challenges of task scheduling in cloud. The incoming tasks/users can select their method on the basis of task requirement like minimum execution time or cost and then it is prioritized. So the algorithm is named as “TPD Scheduling Algorithm”, Here T stands for Task Selection, P Stands for Priority(in terms of cost) and D stands for Deadline. The proposed model is implemented and tested on simulation toolkit. Results validate the correctness of the framework and show a significant improvement over other scheduling methods.
[1]
Clifford Stein,et al.
Introduction to algorithms. Chapter 16. 2nd Edition
,
2001
.
[2]
Hilary Johnson,et al.
Understanding Task Grouping Strategies
,
2004
.
[3]
Xin-She Yang,et al.
Introduction to Algorithms
,
2021,
Nature-Inspired Optimization Algorithms.
[4]
Sateesh Kumar Peddoju,et al.
A Dynamic Optimization Algorithm for Task Scheduling in Cloud Environment
,
2012
.
[5]
Rajkumar Buyya,et al.
CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services
,
2009,
ArXiv.
[6]
Sumit Chavan,et al.
An Optimized Algorithm for Task Scheduling based on Activity based Costing in Cloud Computing
,
2011
.
[7]
Krishna Kant,et al.
Greedy grid scheduling algorithm in dynamic job submission environment
,
2011,
2011 International Conference on Emerging Trends in Electrical and Computer Technology.