On-demand physical resource allocation method for cloud virtual machine to support random service requests

作者：Cao Jie; Zeng Guo Sun; Kuang Gui Juan; Zhang Jian Wei; Ma Hai Ying; Hu Ke Kun; Niu Jun

来源：Journal of Software, 2017, 28(2): 457-472.

DOI：10.13328/j.cnki.jos.005054

• Dynamic variations
• Physical resources
• Processor resources
• Random services
• Resource requirements
• Resource utilizations
• Service applications
• Wave forecasting

Low resource utilization is becoming much more serious in cloud platform which allocates processor resources according to the peak load while providing single service application and facing dynamic variation of resource demand. To address the problem, this study uses cloud virtual machine (VM) center to provide a variety of reasonable service applications simultaneously. Gray wave forecasting algorithm is adopted to predict the future load of service requests and a VM service utility function is proposed by taking resource requirements and service priorities into account. Each VM inside a physical machine dynamically configures physical resources to maximize the service utility value of the physical machine. Besides, by applying the global load balancing and multi-time physical resource redistribution for each virtual machine in the same physical machine, the number of physical resources assigned to the VMs whose service request amount is much larger is further increased. In the end, on-demand resource reconfiguration algorithm ODRGWF based on grey wave forecasting is put forward. The simulation results show that the proposed algorithm can effectively improve processor resource utilization, which is of practical significance to improve user request completion rate and service quality.

• 单位
  同济大学