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Recent exploration into the unique security challenges of cloud computing have shown that when virtual machines belonging to different customers share the same physical machine, new forms of cross-VM covert channel communication arise. In this paper, we explore one of these threats, L2 cache covert channels, and demonstrate the limits of these this threat by providing a quantification of the channel bit rates and an assessment of its ability to do harm. Through progressively refining models of cross-VM covert channels from the derived maximums, to implementable channels in the lab, and finally in Amazon EC2 itself we show how a variety of factors impact our ability to create effective channels. While we demonstrate a covert channel with considerably higher bit rate than previously reported, we assess that even at such improved rates, the harm of data exfiltration from these channels is still limited to the sharing of small, if important, secrets such as private keys.

Creating good adaptation policies is critical to building complex autonomic systems since it is such policies that define the system configuration used in any given situation. While online approaches based on control theory and rulebased expert systems are possible solutions, each has its disadvantages. Here, a hybrid approach is described that uses modeling and optimization offline to generate suitable configurations, which are then encoded as policies that are used at runtime. The approach is demonstrated on the problem of providing dynamic management in virtualized consolidated server environments that host multiple multi-tier applications. Contributions include layered queuing models for Xen-based virtual machine environments, a novel optimization technique that uses a combination of bin packing and gradient search, and experimental results that show that automatic offline policy generation is viable and can be accurate even with modest computational effort.

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Abstract. Virtualization-based server consolidation requires runtime resource reconfiguration to ensure adequate application isolation and performance, especially for multitier services that have dynamic, rapidly changing workloads and responsiveness requirements. While virtualization makes reconfiguration easy, indiscriminate use of adaptations such as VM replication, VM migration, and capacity controls has performance implications. This paper demonstrates that ignoring these costs can have significant impacts on the ability to satisfy response-time-based SLAs, and proposes a solution in the form of a cost-sensitive adaptation engine that weighs the potential benefits of runtime reconfiguration decisions against their costs. Extensive experimental results based on live workload traces show that the technique is able to maximize SLA fulfillment under typical time-of-day workload variations as well as flash crowds, and that it exhibits significantly improved transient behavior compared to approaches that do not account for adaptation costs.

Automatic system monitoring and recovery has the potential to provide a low-cost solution for high availability. However, automating recovery is difficult in practice because of the challenge of accurate fault diagnosis in the presence of low coverage, poor localization ability, and false positives that are inherent in many widely used monitoring techniques. In this paper, we present a holistic model-based approach that overcomes these challenges and enables automatic recovery in distributed systems. To do so, it uses theoretically sound techniques including Bayesian estimation and Markov decision theory to provide controllers that choose good, if not optimal, recovery actions according to a user-defined optimization criteria. By combining monitoring and recovery, the approach realizes benefits that could not have been obtained by using them in isolation. In this paper, we present two recovery algorithms with complementary properties and trade-offs, and validate our algorithms (through simulation) by fault injection on a realistic e-commerce system.

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Organizations of all sizes are shifting their IT infrastructures to the cloud because of its cost efficiency and convenience. Because of the on-demand nature of the Infrastructure as a Service (IaaS) clouds, hundreds of thousands of virtual machines (VMs) may be deployed and terminated in a single large cloud data center each day. In this paper, we propose a content-based scheduling algorithm for the placement of VMs in data centers. We take advantage of the fact that it is possible to find identical disk blocks in different VM disk images with similar operating systems by scheduling VMs with high content similarity on the same hosts. That allows us to reduce the amount of data transferred when deploying a VM on a destination host. In this paper, we first present our study of content similarity between different VMs, based on a large set of VMs with different operating systems that represent the majority of popular operating systems in use today. Our analysis shows that content similarity between VMs with the same operating system and close version numbers (e.g., Ubuntu 12.04 vs. Ubuntu 11.10) can be as high as 60%. We also show that there is close to zero content similarity between VMs with different operating systems. Second, based on the above results, we designed a content-based scheduling algorithm that lowers the network traffic associated with transfer of VM disk images inside data centers. Our experimental results show that the amount of data transfer associated with deployment of VMs and transfer of virtual disk images can be lowered by more than 70%, resulting in significant savings in data center network utilization and congestion.