Parallel data processing has become more and more reliable phenomenon due to the realization of could computing, especially using IaaS (Infrastructure as a Service) clouds. The cloud service providers such as IBM, Google, Microsoft and Oracle have made provisions for parallel data processing in their cloud services. Nevertheless, the frameworks used as of now are static and homogenous in nature in a cluster environment. The problem with these frameworks is that the resource allocation when large jobs are submitted is not efficient as they take more time for processing besides incurring more cost. In this paper we discuss the possibilities of parallel processing and its challenges. One of the IaaS products meant for parallel processing is presented in this paper. VMs are allocated to tasks dynamically for execution of jobs. With proposed framework we performed parallel job processing which involves Map Reduce, a new programming phenomenon. We also compare this with Hadoop.
Most real-life optimization problems involve multiple objective functions. Finding a solution that satisfies the decision-maker is very difficult owing to conflict between the objectives. Furthermore, the solution depends on the decision-maker’s preference. Metaheuristic solution methods have become common tools to solve these problems. The task of obtaining solutions that take account of a decision-maker’s preference is at the forefront of current research. It is also possible to have multiple decision-makers with different preferences and with different decision-making powers. It may not be easy to express a preference using crisp numbers. In this study, the preferences of multiple decision-makers were simulated and a solution based on a genetic algorithm was developed to solve multi-objective optimization problems. The preferences were collected as fuzzy conditional trade-offs and they were updated while running the algorithm interactively with the decision-makers. The proposed method was tested using well-known benchmark problems. The solutions were found to converge around the Pareto front of the problems.
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