Gao, X;
Liu, R;
Kaushik, A;
(2020)
Hierarchical Multi-Agent Optimization for Resource Allocation in Cloud Computing.
IEEE Transactions on Parallel and Distributed Systems
10.1109/TPDS.2020.3030920.
(In press).
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Abstract
In cloud computing, an important concern is to allocate the available resources of service nodes to the requested tasks on demand and to make the objective function optimum, i.e., maximizing resource utilization, payoffs and available bandwidth. This paper proposes a hierarchical multi-agent optimization (HMAO) algorithm in order to maximize the resource utilization and make the bandwidth cost minimum for cloud computing. The proposed HMAO algorithm is a combination of the genetic algorithm (GA) and the multi-agent optimization (MAO) algorithm. With maximizing the resource utilization, an improved GA is implemented to find a set of service nodes that are used to deploy the requested tasks. A decentralized-based MAO algorithm is presented to minimize the bandwidth cost. We study the effect of key parameters of the HMAO algorithm by the Taguchi method and evaluate the performance results. The results demonstrate that the HMAO algorithm is more effective than two baseline algorithms of genetic algorithm (GA) and fast elitist non-dominated sorting genetic algorithm (NSGA-II) in solving the large-scale optimization problem of resource allocation. Furthermore, we provide the performance comparison of the HMAO algorithm with two heuristic Greedy and Viterbi algorithms in on-line resource allocation.
| Type: | Article |
|---|---|
| Title: | Hierarchical Multi-Agent Optimization for Resource Allocation in Cloud Computing. |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TPDS.2020.3030920 |
| Publisher version: | https://doi.org/10.1109/TPDS.2020.3030920 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Cloud computing, resource allocation, resource utilization, bandwidth cost, genetic algorithm, multi-agent optimization |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10112870 |
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