Abstract
So far, the genetic algorithm has been presented for the energy-aware scheduling of virtual machines to minimize the total busy time of servers. However, this algorithm does not consider the criteria for service-level policies on real-time applications. The convergence speed of the genetic algorithm is quite low in solving many of the large hybrid optimization problems. In other similar studies, heuristic algorithms were used to solve the interval scheduling problem. Such algorithms are not able to find nearly optimal solutions to hard problems. Since the optimization of scheduling is part of the hard problems, it is wise to use meta-heuristic algorithms to find nearly optimal solutions. Accordingly, an energy-aware meta-heuristic scheduling algorithm is presented in this paper for real-time virtual machines. The main goal of this algorithm is to minimize the total busy time of the physical machines in an interval without violating the deadline for virtual machines. The results were collected from the genetic algorithm, the smart water drop algorithm, the optimization of the ant colony, and the first possible downward algorithm for comparison and evaluation. The optimization of the ant colony and the smart algorithm of water drops showed better results than did the other two algorithms.
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Partovi, B., Bagheri, A., Kazarji, M.H., Pahl, C., Barzegar, H.R. (2021). Virtual Machine Placement for Edge and Cloud Computing. In: Zirpins, C., et al. Advances in Service-Oriented and Cloud Computing. ESOCC 2020. Communications in Computer and Information Science, vol 1360. Springer, Cham. https://doi.org/10.1007/978-3-030-71906-7_5
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