Toward a Service Availability-Guaranteed Cloud Through VM Placement

• Published in: IEEE/ACM transactions on networking 2024-10, Vol.32 (5), p.3993-4008
• Main Authors: Liu, Jiawei, Zhao, Gongming, Xu, Hongli, Yang, Peng, Wang, Baoqing, Qiao, Chunming
• Format: Article
• Language: English
• Subjects: approximation; Cloud computing; Data centers; Hardware; Load management; multi-tenant; Resource management; service availability; Service level agreements; SLA; Uncertainty; VM placement
• ISSN: 1063-6692; 1558-2566
• DOI: 10.1109/TNET.2024.3401758

In a multi-tenant cloud, the cloud service provider (CSP) leases physical resources to tenants in the form of virtual machines (VMs) with an agreed service level agreement (SLA). As the most important indicator of SLA, we should guarantee the service availability of tenants when placing the VMs. However, previous works about VM placement mainly concentrate on optimizing the cloud resource utilization, but only a few works consider the service availability by measuring the hardware availability. In fact, abnormal tenants can make the corresponding service unavailable by launching network attacks. That is, both the hardware availability and the tenant uncertainty will affect the service availability of VMs on physical machines (PMs). Without considering this factor, the CSP may fail to meet the tenant's SLA requirements, leading to a reduction in revenue. To solve such a problem, this paper considers the service availability in terms of both the hardware availability and the tenant uncertainty, and studies the service availability-guaranteed VM placement in multi-tenant clouds (SAG-VMP) problem. This problem is very challenging since the service availability actually changes with the tenants served on the PM. To address this issue, we propose a two-phase approach: PM assignment and VM placement. The first phase determines the availability of each PM through a long-term tenant-PM mapping algorithm and the second phase places each VM on a PM that meets the service availability requirement based on a primal-dual online algorithm. Two algorithms with bounded approximation factors are proposed for these two phases, respectively. Both small-scale experiment results and large-scale simulation results show the superior performance of our proposed algorithms compared with other alternatives.