论文标题
防御能节能云中的共同居住攻击：基于优化的实时安全VM分配策略
Defending against Co-residence Attack in Energy-Efficient Cloud: An Optimization based Real-time Secure VM Allocation Strategy
论文作者
论文摘要
用户之间的资源共享是云计算的基础，但是，这也可能导致与受害者VMS驻留在同一物理服务器中的恶意虚拟机（VM）发起的各种共同居住攻击。在本文中，我们旨在通过安全，工作均衡和节能的VM分配策略来防止这种共同居住攻击。具体而言，我们通过量化和最小化三个关键因素来将问题建模为优化问题：（1）安全风险，（2）功耗和（3）不同物理服务器之间的不平衡工作量。此外，这项工作通过假设来自随机时间到达的不同用户的随机数量VM来考虑现实的环境环境，这需要优化解决方案不断发展。由于优化问题是NP-HARD，我们建议在时间窗口中首先群集VM，并进一步采用蚂蚁菌落优化（ACO）算法来确定每个时间窗口的最佳分配策略。基于现实世界云痕迹的全面实验结果验证了所提出的方案的有效性。
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 propose to first cluster VMs in time windows, and further adopt the Ant Colony Optimization (ACO) algorithm to identify the optimal allocation strategy for each time window. Comprehensive experimental results based on real world cloud traces validates the effectiveness of the proposed scheme.
