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Cloud computing is Internet-based computing, whereby shared and distributed resources and information are provided on demand. It involves provision of dynamically scalable and virtualized resources. Perhaps, with such high provisioning of scalability and on-demand resource availability and high computational facilities, cloud also faces many issues. Service availability on demand, unpredictability of performance, on-time availability of resources, data confidentiality, security, and privacy are the major challenges in cloud computing e. Different simulation tools are available to analyze and test the execution of algorithm. CloudAnalyst is one of the simulation tools used to model and analyze cloud computing environment before the actual deployment. Cloud Application Service Broker determines which data center should service the request from each user base. Service proximity-based routing selects the data center which has lowest network latency or minimum transmission delay from a user base. If there are more than one data centers in a region in close proximity, then one of the data centers is selected at random to service the incoming request. However, other factors such as cost, workload, number of virtual machines, processing time etc., are not taken into consideration. Randomly selected data center gives undesirable results in terms of response time, data processing time, cost, and other parameters. In this paper, we propose a weight-based data center selection algorithm which proves to improvise the randomized service proximity-based routing in terms of processing time, i.e., performance and costs. |
