Balicki Jerzy
Faculty of Mathematics and Computer Science, Warsaw University of Technology, 00-662 Warsaw, Poland.
Entropy (Basel). 2021 Dec 28;24(1):58. doi: 10.3390/e24010058.
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.
粒子群优化算法（PSO）是一种有效的元启发式算法，能够确定帕累托最优解。我们通过引入量子门提出了一种扩展的粒子群优化算法，以确保寻找有效替代方案的粒子群体的多样性。在计算云中资源分配问题上验证了解的质量，以改进虚拟机的实时迁移。我们考虑嵌入到虚拟机中的基于深度学习的模型的多准则优化问题。具有深度学习代理的计算云可以支持教育、智慧城市或经济等多个领域。由于深度学习代理需要大量计算机资源，因此研究了七个准则，如主机的电力、云的可靠性、瓶颈主机的CPU工作负载、关键节点的通信容量、负载最重内存的可用随机存取存储器容量、最繁忙存储的可用磁盘存储容量以及总体计算机成本。量子门修改粒子当前位置的接受位置。为了验证上述概念，基于OpenStack平台在实验室云中进行了各种模拟。数值实验证实，多目标量子启发粒子群优化算法比其他元启发式算法能提供更好的解决方案。
