Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Computer Science

Recently, the Cloud computing has gained more popularity in computing technology environment. According the Cloud computing system, the computing resources are introduced to the customers as a service on demand and bill on as pay-as-you-go basis. The Cloud computing environment provides infinite of computing resources to the Cloud customers to execute their applications using its resources in the rate of decreasing or increasing according to the demand. According to task scheduling problem in the Cloud computing environment, the customer{u2019}s application is divided into tasks, these tasks should be scheduled and mapped onto the available resources (i.e., VMs) in a way that the provider achieves high resource utilization and the customers execute their application in minimum time and cost. In this thesis, two task scheduling algorithms; Tournament Selection Genetic Algorithm TSGA and Dependent Task Genetic Algorithm DTGA; based on Standard Genetic Algorithm SGA have been introduced According to the TSGA and DTGA task scheduling algorithms, some modifications are applied to improve the performance of the standard Genetic Algorithm SGA. The objectives of these developed algorithms are minimize completion time and cost, as well as maximize resource utilization, speed up, and efficiency for Cloud computing. The developed TSGA algorithm concerns about scheduling independent tasks, while DTGA algorithm concerns about scheduling dependent tasks

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