A multi-objective optimization for resource allocation of emergent demands in cloud computing

Abstract Cloud resource demands, especially some unclear and emergent are growing rapidly with the development of cloud computing, big data artificial intelligence. The traditional allocation methods do not support mode in guaranteeing timeliness optimization allocation. This paper proposes a algorithm for demands computing. After building priority matching distances performance proportion to respond multi-objective model is established based on minimum number physical servers used proportion. Then, an improved evolutionary algorithm, RAA-PI-NSGAII, presented solve model, which only improves quality distribution uniformity solution set but also accelerates solving speed. experimental results show that our can allocate resources quickly optimally balance utilization all kinds resources.