Journal
2021 24TH CONFERENCE ON INNOVATION IN CLOUDS, INTERNET AND NETWORKS AND WORKSHOPS (ICIN)
Volume -, Issue -, Pages -Publisher
IEEE
DOI: 10.1109/ICIN51074.2021.9385557
Keywords
Cloud computing; green computing; power management; resource allocation; energy consumption; energy efficiency; memory management; tabu search; greedy local search; simulated annealing; metaheuristics; knapsack problem
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The paper proposes an efficient algorithm for energy-efficient user allocation in data centers, comparing its performance with traditional approaches. The results show that the algorithm is significantly effective in real-time environments.
Energy efficiency is a major topic in every scientific field, since being energy efficient means producing more for a smaller cost. Data centers are no exception to this rule as their energy use represents a large portion of the global consumption, and it is needless to say that they ought to perform optimally while being eco-friendly in order to preserve natural resources as much as possible while providing a high quality service for the users. In this paper, we propose an efficient algorithm for allocating users to a pool of servers in an energy-efficient way. Our allocation model emphasizes the critical importance of non-dominant resource types such as memory, which usually tend to be wasted by homogeneous allocation approaches. We show that the performance of the algorithm makes it worthy of being used in real-time environments where split-second decisions must be made. We compare our algorithm to the most well-known metaheuristics used in operations research and we show that they do not provide a significant improvement in a reasonable time.
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