Cloud computing is an emerging trend in the field of Information Technology and includes a large of distributed resources. The main goal of cloud computing service providers is to provide resources to workloads efficiently. Load balancing is an important technique for the goal. The technique is responsible for optimizing resource utilization, maximizing throughput, minimizing response time, and avoiding overloading of any single resources. So far, many load balancing algorithms have been proposed but their performance has to be still desired. In this paper, we propose a novel load balancing algorithm that is based on the method of estimating the end of service time. The simulation results show that our proposed algorithm improves response time and processing time.

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In this study, a novel method for generating multiple-choice tests is presented, which extracts the required number of tests of the same levels of difficulty in a single attempt and approximates the difficulty level requirement given by users. We propose an approach using parallelism and Pareto optimization for multi-swarm migration in a particle swarm optimization (PSO) algorithm. Multi-PSO is proposed for shortening the computing time. The proposed migration of PSOs increases the diversity of tests and controls the overlap of extracted tests. The experimental results show that the proposed method can generate many tests from question banks satisfying predefined levels of difficulty. Additionally, the developed method is shown to be effective in terms of many criteria when compared with other methods such as manually extracted tests, a simulated annealing algorithm (SA), random methods and PSO-based approaches in terms of the number of successful solutions, accuracy, standard deviation, search speed, and the number of questions overlapping between the exam questions, as well as for changing the search space, changing the number of individuals, changing the number of swarms, and changing the difficulty requirements. INDEX TERMS multiple-choice tests, multi-swarm optimization, multi-objective optimization, parallelism

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