Defending against Co-residence Attack in Energy-Efficient Cloud: An Optimization based Real-time Secure VM Allocation Strategy
Lu Cao, Ruiwen Li, Xiaojun Ruan, Yuhong Liu

TL;DR
This paper presents a real-time, energy-efficient VM allocation strategy that minimizes security risks, power consumption, and workload imbalance to defend against co-residence attacks in cloud environments.
Contribution
It introduces an optimization-based approach using clustering and Ant Colony Optimization to dynamically allocate VMs securely and efficiently in real-time.
Findings
Effectively reduces security risks and energy consumption.
Balances workloads across physical servers.
Validated with real-world cloud trace experiments.
Abstract
Resource sharing among users serves as the foundation of cloud computing, which, however, may also cause vulnerabilities to diverse co-residence attacks launched by malicious virtual machines (VM) residing in the same physical server with the victim VMs. In this paper, we aim to defend against such co-residence attacks through a secure, workload-balanced, and energy-efficient VM allocation strategy. Specifically, we model the problem as an optimization problem by quantifying and minimizing three key factors: (1) the security risks, (2) the power consumption and (3) the unbalanced workloads among different physical servers. Furthermore, this work considers a realistic environmental setting by assuming a random number of VMs from different users arriving at random timings, which requires the optimization solution to be continuously evolving. As the optimization problem is NP-hard, we…
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Taxonomy
TopicsCloud Computing and Resource Management · IoT and Edge/Fog Computing · Software-Defined Networks and 5G
