Many-Objective Quantum-Inspired Particle Swarm Optimization Algorithm for Placement of Virtual Machines in Smart Computing Cloud

Abstract

Particle swarm optimization algorithm (PSO) is an effective metaheuristic that can determine Pareto-optimal solutions. We propose an extended PSO by introducing quantum gates in order to ensure the diversity of particle populations that are looking for efficient alternatives. The quality of solutions was verified in the issue of assignment of resources in the computing cloud to improve the live migration of virtual machines. We consider the multi-criteria optimization problem of deep learning-based models embedded into virtual machines. Computing clouds with deep learning agents can support several areas of education, smart city or economy. Because deep learning agents require lots of computer resources, seven criteria are studied such as electric power of hosts, reliability of cloud, CPU workload of the bottleneck host, communication capacity of the critical node, a free RAM capacity of the most loaded memory, a free disc memory capacity of the most busy storage, and overall computer costs. Quantum gates modify an accepted position for the current location of a particle. To verify the above concept, various simulations have been carried out on the laboratory cloud based on the OpenStack platform. Numerical experiments have confirmed that multi-objective quantum-inspired particle swarm optimization algorithm provides better solutions than the other metaheuristics.

Improved Quantum-inspired Hybrid Particle Swarm Optimization Based Resource Allocation for Quantum- Enabled IoT Devices in Ad Hoc Networks

“…The IQHPSO algorithm starts by initializing the swarm with random positions and velocities. The positions and velocities are then updated iteratively using the QRG operator and the adaptive parameter tuning technique [26]. The global best particle is updated using the global best selection strategy.…”

TMaLB: A Tolerable Many-Objective Load Balancing Technique for IoT Workflows Allocation

“…The multi-objective optimization problem usually focuses on two/three objectives where the values of the objectives that create the best trade-off are considered optimal solutions. A special type of multi-objective problem, with the optimization of four or more objectives, is usually presented as a many-objective problem [12], [14], [25], [30].…”

“…It requires higher allocated memory to the archived solutions. So the number of Pareto front solutions and their order in the memory will be important in the evolutionary algorithms [14], [34].…”

“…Recently, the importance of traffic engineering and solving the problem of efficient load-balancing among resources with limited capacity has emerged as a key research topic in ensuring the improvement of the QoS and the total performance of the IoT network [9], [10] , [11]. The problem of load-balancing is usually considered by determining how to allocate tasks to resources for optimizing the QoS [12], [13], [14], [15]. Thus, there is a need for load-balancing approaches to deal with growing network traffic and to improve the QoS considering users, service providers, and infrastructure providers.…”

Trajectory Planning in Robot Joint Space Based on Improved Quantum Particle Swarm Optimization Algorithm

“…Reference [32] proposes a quantum behavior particle swarm optimization algorithm with a dynamic learning strategy, aiming to improve the performance and adaptability of the algorithm. Jerzy Balicki conducted a many-objective quantum-inspired particle swarm optimization algorithm and confirmed that multi-objective quantum-inspired particle swarm optimization algorithms provide better solutions than other metaheuristics [33]. Arnaud Flori proposed a quantum particle swarm optimization (QUAPSO), which simplifies the setup of the algorithm by setting the velocity PSO parameter on top of the quantum superposition [34].…”