A DYNAMIC APPROACH TO TASK SCHEDULING IN CLOUD COMPUTING USING GENETIC ALGORITHM

Cloud computing is one of device technology trends in the future since it combines the advantages of both device computing and cloud, Recent years have seen the massive migration of enterprise applications to the cloud. Cloud computing used in business organizations and educational institutions. One...

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Bibliographic Details
Published in:Journal of Theoretical and Applied Information Technology 2016-03, Vol.85 (2), p.124-124
Main Authors: Lakshmi, R Durga, Srinivasu, N
Format: Article
Language:English
Subjects:
Algorithms
Cloud computing
Devices
Genetic algorithms
Queues
Resource scheduling
Task scheduling
Tasks
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Summary:Cloud computing is one of device technology trends in the future since it combines the advantages of both device computing and cloud, Recent years have seen the massive migration of enterprise applications to the cloud. Cloud computing used in business organizations and educational institutions. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. To improve the QoS in a system one must need to reduce the waiting time of the system. Genetic Algorithm (GA) is a heuristic search technique which produces the optimal solution of the tasks. This work produces one scheduling algorithm based on GA to optimize the waiting time of overall system. The cloud environment is divided into two parts mainly, one is Cloud User (CU) and another is Cloud Service Provider (CSP). CU sends service requests to the CSP and all the requests are stored in a Request Queue (RQ) inside CSP which directly communicates with GA Module Queue Sequencer (GAQS). GAQS perform background operation, like daemon, with extreme dedication and selects the best sequence of jobs to be executed which minimize the Waiting time (WT) of the tasks using Round Robin (RR) scheduling Algorithm and store them into Buffer Queue (BQ). Then the jobs must be scheduled by the Job Scheduler (JS) and select the particular resource from resource pool (RP) which it needs for execution.
ISSN:1817-3195