A Resource Scheduling Algorithm Based on Maximum Discrete VM in Heterogeneity Cloud

Due to workload fluctuation, the cloud vendors expect to rent their idle resources with virtual machines (VMs) configured in advance when their workload is lower. By integrating volunteer computing into cloud architectures, we envision a gigantic self-organizing cloud (SOC) being formed to reap the huge potential of untapped commodity computing power via the Internet. However, due to heterogeneity of the resources, the performance difference exists generally. To reduce the risk of the single-point-of-failure, we proposed an election-based resource provision algorithm using those VMs as discrete as possible. We formulate the concept of discreteness for allocating VM for a request that needs k virtual machines. Each VM consists of several servers in multiple cabinets. The discreteness is measured to be the number of cabinets that k virtual machines span. We show that it is an NP-hard problem, and provide a heuristic election algorithm based maximum cost performance. The technologies of analytic hierarchy process (AHP) and simple additive weighting (SAW) are adopted to guarantee VM performance that users hope to obtain. The experiment results show more benefits of our algorithm.