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International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS)

Quantum Resource Distribution Management in Multi-task Environment

Sara El Gaily, Sándor Imre

Published Oct 1, 2019• 6 citations
Publisher

Abstract

A proposed resource distribution management model based on quantum optimization approach is introduced in this paper. The proposed quantum strategy can guarantee low computational complexity and high accuracy. moreover, we focused on setting up the parameters of the quantum algorithm with respect to the constraint's variables of the proposed model.

DOI
10.1109/telsiks46999.2019.9002340

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A proposed resource distribution management model based on quantum optimization approach is introduced in this paper. The proposed quantum strategy can guarantee low computational complexity and high accuracy. moreover, we focused on setting up the parameters of the quantum algorithm with respect to the constraint's variables of the proposed model.
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.
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.
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.
Abstract With the rapid development of quantum communication technology, quantum key distribution (QKD) provides unconditional secure communication based on the principles of quantum mechanics. In the scenario of multi-service concurrency, it faces the challenges of dynamic resource allocation and complex network scheduling. The traditional key pool management mechanism has problems such as low key resource utilization and difficulty in coordinating multi-dimensional constraints in path optimization, which cannot meet the dynamically changing service quality requirements. This study proposes a quantum key pool architecture with dynamic resource management capabilities. The architecture adopts hierarchical key pool management and adds key recovery mechanism and key dynamic transfer function. For the path optimization problem under multiple constraints, the Lagrangian dual decomposition method is used to establish a mathematical model. The model considers multiple key factors such as bandwidth and delay, and designs an efficient heuristic algorithm to solve the optimal routing strategy. The simulation results show that the proposed algorithm has good performance in terms of service request success rate, key resource utilization, key remaining rate, etc. This study provides a new idea for key distribution and resource allocation in quantum networks, which will help promote the further development of quantum communication technology.
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.