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Multi-input cloud resource allocation strategy with limited buffer and virtual machine synchronization failure

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Abstract

Based on virtualization technologies, virtual machines (VMs) provide computing services and network resources for cloud users over the Internet. When cloud users use VMs for an extended period of time, requests generated by other cloud users are easily blocked. When cloud users no longer use VMs, requests’ throughput will be decreased substantially. We propose a multi-input cloud resource allocation strategy with limited buffer and VM synchronization failure, which can improve the throughput of requests. A limited buffer is added to the system to reduce the possible blocking behaviors in the proposed strategy. Moreover, we assume that a physical machine failure will put all the VMs in failure states. The system recovery will take a period of time after the failure, which is called repair time. After the failure is repaired, the system continues to provide services for cloud users. A 2-dimensional Markov chain is established to measure the performance indexes of requests. In addition, we show the blocking rate, the loss rate, the throughput, the average quantity, and the average latency of requests by system state transition probability matrix and analyze their changing trends through numerical experiments. Finally, considering the balance between requests’ blocking rate and requests’ throughput, we construct a system profit function to ascertain the optimal number of request streams and obtain the maximal system profit.

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Data Availability

The data presented in this study are available on request from the corresponding author.

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Funding

This research was supported by the National Natural Science Foundation of China [Grant Number 61701097], and the Natural Science Foundation of Hebei Province [Grant Number F2016501073], China.

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Correspondence to Yuan Zhao.

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Zhao, Y., Chen, K., Ye, Z. et al. Multi-input cloud resource allocation strategy with limited buffer and virtual machine synchronization failure. Cluster Comput 27, 119–135 (2024). https://doi.org/10.1007/s10586-022-03915-6

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