Abstract
Virtualization makes the resources management easily in cloud datacenters by enabling virtual machine (VM) migration to eliminate the hotspots. Many migration strategies have been adopted in order to mitigate the resources competition and maintain the VM performance. However, the hotspots are not all accurately flagged without delay in the recent cloud workload and the cascading overloads are probably triggered after VM migration at the same time. In this paper, we present a workload-aware migration strategy called Chameleon targeting the recent cloud workload. Chameleon constructs a novel indicator and the corresponding threshold to flag the hotspots accurately. Chameleon also predicts the resource provision of VM in the complex workload pressure to avoid the secondary overload in the physical machine (PM), which the migrated VM moves to. We performed our evaluation on a virtual datacenter simulated by Xen. Our evaluation results show that Chameleon can flag the hotspots accurately and timely. Furthermore, the policy of resources estimation for the VMs helps Chameleon to make the decision of selecting the under-load PM, in order to mitigate the risk of secondary hotspot.
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Liu, Y. (2016). Chameleon: Virtual Machine Migration Supporting Cascading Overload Management in Cloud. In: Huang, X., Xiang, Y., Li, KC. (eds) Green, Pervasive, and Cloud Computing. Lecture Notes in Computer Science(), vol 9663. Springer, Cham. https://doi.org/10.1007/978-3-319-39077-2_9
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DOI: https://doi.org/10.1007/978-3-319-39077-2_9
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