Abstract
Cloud computing has become the popular choice in developing countries such as Nigeria where access to high performance computing facilities is inadequate. Individuals, organizations and institutions in need of high performance computing facilities can subscribe to cloud computing facilities on a pay-as-you-go basis. Whenever a customer requests for cloud computing services, there is a problem of allocating virtual machines for such services on available physical machines while minimizing energy consumption and carbon-dioxide emission, as well as maximizing resource utilization. The Modified Best First Decreasing(MBFD) algorithm is a baseline algorithm for evaluating the performance of virtual machine placement algorithms in cloud computing. This research carries out a performance evaluation of the MBFD algorithms in the CloudSim 3.0.3 simulator under three different configurations A, B and C. Configuration A has fewer processor resources than configuration B which has fewer processor resources than configuration C. CloudSim 3.0.3 is installed in Eclipse Integrated Development Environment on a Laptop with two Intel Celeron Processors at 1.60 GHz each and 4 GB memory. Configurations B and C have lower energy consumption and faster computation time compared to configuration A. The lowest average energy consumption is 101.97 KWh and standard deviation of 8.63 KWh when Local Regression Robust with minimum utilization heuristic algorithm was executed under configuration B. The fastest average computation time is 0.21898 s for static threshold with minimum utilization heuristic algorithm under configuration B. This work recommends evaluating the adaptive heuristics on real-life cloud computing testbed to further validate the results.
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Akinwumi, J., Adeyanju, I. (2022). Performance Evaluation of Modified Best First Decreasing Algorithms for Dynamic Virtual Machine Placement in Cloud Computing. In: Ye, K., Zhang, LJ. (eds) Cloud Computing – CLOUD 2022. CLOUD 2022. Lecture Notes in Computer Science, vol 13731. Springer, Cham. https://doi.org/10.1007/978-3-031-23498-9_1
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