Today Cloud computing is used in a wide range of domains. By using cloud computing a user can utilize services and pool of resources through internet. The cloud computing platform guarantees subscribers that it will live up to the service level agreement (SLA) in providing resources as service and as per needs. However, it is essential that the provider be able to effectively manage the resources. One of the important roles of the cloud computing platform is to balance the load amongst different servers in order to avoid overloading in any host and improve resource utilization. The concept of Genetic algorithm is specifically useful in load balancing for best virtual machines distribution across servers. In this paper, we focus on load balancing and also on efficient use of resources to reduce the energy consumption without degrading cloud performance.
User requests, together with arrival timestamp is called Workload. The workload can be either the synthetic or real workload. The synthetic workloads are useful to carry out the controlled experiment. For performance evaluation of complex multitier applications the synthetic workload generation techniques are required such as in Banking , E-Commerce , Business deployed in the cloud computing environments. In each class the application has its own characteristics of workload. The important requirement is that the generated workload called synthetic workload should maintain the same characteristics and behavior of real workload. Techniques to generate synthetic workload are discussed in this paper.
Internet Application Developers are provided with great opportunities by the advancements in the field of Cloud Computing. To the users it is a necessity to get all resources without any time delay and within specific cost constraints. Now the challenge of providing resources to accommodate the large demand of ever-growing Cloud users is to be met by the Cloud Providers. Now, the competition among providers has progressed to such a stage where the load forecasting and dynamic resource allocation are among the major concerns to them. This paper illustrates the need for proper prediction mechanisms and the types of load forecasting mechanisms. A number of dominant working models are discussed in this paper. The analysis of every method belonging to a particular model is explained in detail.
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