Paper Info
Title
Scheduling using improved genetic algorithm in cloud computing for independent tasks
Abstract
Cloud computing is a new technology and it is becoming popular day by day because of its great features. In this technology almost everything like hardware, software and platform are provided as a service. These services are charged from users on the pay-per-use bases. A cloud provider in cloud computing provides services on the basis of clients' requests. An important issue in cloud computing is the scheduling of users' requests means how to allocate resources to these requests, so that the requested tasks can be completed in a minimum time according to the user defined time. A good scheduling technique also helps in efficient utilization of the resources. Many scheduling algorithms have been researched like Min-Min, Max-Min, X-Sufferage, Genetic Algorithm, Particle Swarm Optimization etc. In this paper the three scheduling techniques Min-Min, Max-Min and Genetic Algorithm have been discussed and performance metrics of Min-Min and Max-Min have been shown. The performance of the standard Genetic Algorithm and the proposed Improved Genetic Algorithm have been checked against the sample data. A new scheduling idea is also proposed in which Min-Min and Max-Min can be combined in Genetic Algorithm.
Year
DOI
Venue
2012
10.1145/2345396.2345420
ICACCI
Keywords
Field
DocType
minimum time,standard genetic algorithm,cloud provider,new scheduling idea,independent task,scheduling algorithm,genetic algorithm,good scheduling technique,proposed improved genetic algorithm,cloud computing,scheduling techniques min-min
Fair-share scheduling,Computer science,Flow shop scheduling,Genetic algorithm scheduling,Rate-monotonic scheduling,Dynamic priority scheduling,Earliest deadline first scheduling,Population-based incremental learning,Cloud computing,Distributed computing
Conference
Citations 
PageRank 
References 
12
0.91
4
Authors
2
Name
Order
Citations
PageRank
Pardeep Kumar124324.38
Amandeep Verma2904.98