Cloud Computing Resource Scheduling Strategy Based on Improved RAQPSO Algorithm Research
ZHAO Yu
HUI Xiaobin
XU Jianhong
ZHONG Jilong
Abstract：In'INTERNET + TIME',cloud computing represents a novel business model.However,the cloud user tasks in the system and compute node scheduling problem significantly affects the system per-formance and competitiveness of cloud.An improved algorithm of quantum particles-adaptive quantum particle swarm optimization (RAQPSO),based on the inertia weight adj ustment of parameters and reverse learning to improve the global search ability of the algorithm,and applied to cloud computing resource scheduling problem to verify the effectiveness of the algorithm.With cloud computing resource scheduling model is established.And then uses the adaptive mechanism,the change of the fitness function as update of inertia weight factor,avoids simply value according to the linear function of the number of iterations. Add the particle reverse learning operator,to strengthen the global search ability particles.The experi-mental results show that the RAQPSO algorithm greatly save the task completion time,and keep a good computing nodes load balancing.
Keywords：Cloud computingResource schedulingQuantum particle swarm optimizationThe inertia
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 70-75 )