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Resource sharing among users serves as the foundation of cloud computing,which, however, may also cause vulnerabilities to diverse co-residence attackslaunched by malicious virtual machines (VM) residing in the same physicalserver with the victim VMs. In this paper, we aim to defend against suchco-residence attacks through a secure, workload-balanced, and energy-efficientVM allocation strategy. Specifically, we model the problem as an optimizationproblem by quantifying and minimizing three key factors: (1) the securityrisks, (2) the power consumption and (3) the unbalanced workloads amongdifferent physical servers. Furthermore, this work considers a realisticenvironmental setting by assuming a random number of VMs from different usersarriving at random timings, which requires the optimization solution to becontinuously evolving. As the optimization problem is NP-hard, we propose tofirst cluster VMs in time windows, and further adopt the Ant ColonyOptimization (ACO) algorithm to identify the optimal allocation strategy foreach time window. Comprehensive experimental results based on real world cloudtraces validates the effectiveness of the proposed scheme.

Defending against Co-residence Attack in Energy-Efficient Cloud: An Optimization based Real-time Secure VM Allocation Strategy