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International Conference on Telecommunications and Signal Processing (TSP)

Derivation of Parameters of Quantum optimization in Resource Distribution Management

Sara El Gaily, Sándor Imre

Published Jul 1, 2019• 6 citations
Publisher

Abstract

In the recent decades, the ubiquity of computational complexity in the process of a large scale of resource distribution management became one of the key interest of scientific research community, so to solve this large computationally hard problem, it requires selecting the best and the optimum computing framework. In this paper we will present a resource distribution strategy based on the quantum method, where we explained how we can use the quantum algorithm as a computational platform for resource distribution, then a mathematical model has been built, in order to define the stochastic parameters of the quantum strategy related to the given resource distribution model, finally.

DOI
10.1109/tsp.2019.8769092

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In the recent decades, the ubiquity of computational complexity in the process of a large scale of resource distribution management became one of the key interest of scientific research community, so to solve this large computationally hard problem, it requires selecting the best and the optimum computing framework. In this paper we will present a resource distribution strategy based on the quantum method, where we explained how we can use the quantum algorithm as a computational platform for resource distribution, then a mathematical model has been built, in order to define the stochastic parameters of the quantum strategy related to the given resource distribution model, finally.
Classical computing methods do not enable to withstand the cumbersome computational operations of complex applications and a bunch of untouched problems that still exist until today. The proposed quantum method tackles the computational complexity problem by exploiting the parallel processing techniques of quantum computing. This paper aims to take advantage of quantum computing power in resource distribution management for a large scale of resources. To achieve this, we use quantum extreme value searching algorithm as a minimum searching algorithm. Moreover, to prove the efficiency of the quantum strategy, we compare the quantum method and the randomized one.
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.
Cloud resource demands are rapidly increasing exponentially with the development of cloud computing. For this sake, new modern methods and techniques are needed to improve its quality of service. In this work, the problem of resource distribution management in cloud computing is formulated as an optimisation problem. To this end, we exploit the constrained quantum optimisation algorithm (CQOA) in resource distribution management by considering queueing aspect in order to optimise and improve its performance in terms of computational complexity, accuracy, and energy consumption. Next, we study the implementation and the configuration of the CQOA. After, we investigate the computational complexity of selecting the optimal distribution scenario that corresponds to minimum energy consumption. We also express the lower and upper bounds of the overall possible assignment scenarios within the database. Finally, we validate the efficiency of the proposed implemented CQOA by constructing a simulation environment.
Quantum computing and communications try to identify more efficient solutions to the most challenging classical problems such as optimization, secure information transfer, etc. This paper will describe a new quantum method for the distribution of resources in computing platforms that consist of a large number of computing units. Furthermore, a simulation environment was developed and the performance of the new method compared to a classical reference strategy will be demonstrated. Moreover, it will be proven that the proposed solution tackles the problems of computational complexity, computing units that are time-consuming and slow to process, as well as the accuracy in determining the optimum result.