Constrained Quantum Optimization for Resource Distribution Management

The cloud computing field suffers from the heavy processing caused by the exponentially increasing data traffic. Therefore, optimizing the network performance and achieving a better quality of service (QoS) became a central goal. In cloud computing, the problem of energy consumption of resource dist...

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Bibliographic Details
Published in:International journal of advanced computer science & applications Vol. 12; no. 8
Main Authors: Gaily, Sara El, Imre, Sándor
Format: Journal Article
Language:English
Published: West Yorkshire Science and Information (SAI) Organization Limited 2021
Subjects:
Algorithms
Cloud computing
Complexity
Distribution management
Energy consumption
Energy distribution
Heuristic methods
Optimization
Quality of service architectures
ISSN:2158-107X, 2156-5570
Online Access:Get full text
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Summary:The cloud computing field suffers from the heavy processing caused by the exponentially increasing data traffic. Therefore, optimizing the network performance and achieving a better quality of service (QoS) became a central goal. In cloud computing, the problem of energy consumption of resource distribution management system (RDMS) is presented as an optimization problem. Most of the existing classical optimization approaches, such as heuristic and metaheuristic have high computational complexity. In this work, we proposed a quantum optimization strategy that executes the tasks exponentially faster and with high accuracy named constrained quantum optimization algorithm (CQOA). We exploit the CQOA in RDMS as a toy example for pointing out the efficiency of the proposed quantum strategy in reducing energy consumption and computational complexity. Following that, we investigate the CQOA's implementation, setup, and computational complexity. Finally, we create a simulation environment to evaluate the efficiency of the suggested implemented constrained quantum strategy.
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ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2021.0120806