الفهرس 
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Abstract
Cloud computing is a term, which involves virtualization, distributed computing, networking, software and web services. A cloud consists of several elements such as clients, datacenter and distributed servers. It includes fault tolerance, high availability, scalability, flexibility, reduced overhead for users, reduced cost of ownership, on demand services etc. Central to these issues lies the establishment of an effective load balancing algorithm. The load can be CPU load, memory capacity, delay or network load. Load balancing is the process of distributing the load among various nodes of a distributed system to improve both resource utilization and job response time while also avoiding a situation where some of the nodes are heavily loaded while other nodes are idle or doing very little work. Load balancing ensures that all the processor in the system or every node in the network does approximately the equal amount of work at any instant of time. Our objective is to develop an effective load balancing algorithm using Divisible load scheduling theorem to maximize or minimize different performance parameters (throughput, latency for example) for the clouds of different sizes (virtual topology depending on the application requirement). To present a better approach for solving the problem of resource load balancing in a cloud computing environment, this thesis use a genetic algorithm with some modification based virtual resources scheduling strategy that focuses on system load balancing. The modified genetic algorithm approach here is used to generate the population of by itself. GAs are appropriate to taking care of generation scheduling issues, in light of the fact that not at all like heuristic techniques genetic algorithms work on a populace of arrangements instead of a solitary arrangement. A fitness equation is applied for checking the fitness of the scheduling results. The experimental results showed that the improved genetic algorithm minimized the makespan and utilizes the resources effectively than the standard genetic algorithm. This idea can be further extended in which the execution cost of the resources as fitness criteria cold be used. This method can be adapted in existing CloudSim systems for decreasing makespan and better resource utilization. The approach presented here solves the problem of load balance and high migration costs. Usually load balance and high number of resources migrations occur if the scheduling is performed using the traditional algorithms.