LAUSR

As a simple and low-cost way to obtain enough computing resources, more and more tenants migrate their tasks to the cloud. However, the frequent occurrence of abnormal events (e.g., malicious tenants and node failures) in the cloud will seriously affect the tenants’ QoS. Conventionally, the cloud vendors reduce the frequency of abnormal events by deploying auxiliary systems, which requires additional costs and increases network complexity. Considering that it is an unrealistic expectation to eliminate the occurrence of abnormal events in clouds, this paper proposes a complementary scheme to alleviate the negative impact scope when an abnormal event occurs through multi-constrained VM placement without consuming additional resources. Specifically, when deploying VMs, we limit the number of pods (or service nodes) each tenant can access and the number of tenants hosted by each pod (or service node). However, the multi-dimensional interaction among numerous system parameters and performance/resource considerations makes the problem of multi-constrained VM placement for alleviating the impact of abnormal events very challenging. To solve this problem, we formulate an integer linear programming and propose a rounding-based algorithm with a logarithmic approximation ratio. We implement our proposed algorithm on a physical testbed. The experimental and simulation results show the high efficiency of the proposed algorithm. For example, our algorithm reduces the impact scope of service node failure by 60%, the impact scope of malicious tenants by 40%, and the tenant task makespan by 25% compared with other alternatives.