In this paper, we present the design of a performance isolation benchmark that quantifies the degree to which a virtualization system limits the impact of a misbehaving virtual machine on other well-behaving virtual machines running on the same physical machine. Our test suite includes six different stress tests -a CPU intensive test, a memory intensive test, a disk intensive test, two network intensive tests (send and receive) and a fork bomb. We describe the design of our benchmark suite and present results of testing three flavors of virtualization systems -an example of full virtualization (VMware Workstation), an example of paravirtualization (Xen) and two examples of operating system level virtualization (Solaris Containers and OpenVZ). We find that the full virtualization system offers complete isolation in all cases and that the paravirtualization system offers nearly the same benefits -no degradation in many cases with at most 1.7% degradation in the disk intensive test. The results for operating system level virtualization systems are varied -illustrating the complexity of achieving isolation of all resources in a tightly coupled system. Our results highlight the difference between these classes of virtualization systems as well as the importance of considering multiple categories of resource consumption when evaluating the performance isolation properties of a virtualization system.

No abstract

The promise of automatic data backup into the cloud is alluring. Off-site backup offers protection against a whole class of catastrophic risks (fire, flood, etc.) that on-site backup solutions cannot. Data can be backed up into the cloud automatically with little or no user involvement. Incremental backup software running detects the latest changes, encrypts the data, and sends it into the cloud. Files can be restored on demand and some services allow copies of files to be downloaded through a web interface to other machines , providing a form of file sharing. With costs dropping to ~$60-$100 per year for unlimited storage, it is not surprising that many home and small business users are signing up. In this paper, we evaluate four popular consumer cloud storage offerings -Mozy, Carbonite, Dropbox, and CrashPlan -to determine if they live up to the benefits users expect. We document wide variations in backup and restore performance, the type of data that is backed-up, no liability for data loss, and problems with data privacy. From our experiments, we derive a set of lessons and recommendations for consumer cloud storage that if followed more uniformly, could substantially improve the cloud storage experience for many consumers.

No abstract

File system designers today face a dilemma. A log-structured file system (LFS) can offer superior performance for many common workloads such as those with frequent small writes, read traffic that is predominantly absorbed by the cache, and sufficient idle time to clean the log. However, an LFS has poor performance for other workloads, such as random updates to a full disk with little idle time to clean. In this paper, we show how adaptive algorithms can be used to enable LFS to provide high performance across a wider range of workloads. First, we show how to improve LFS write performance in three ways: by choosing the segment 'size to match disk and workload characteristics, by modifying the LFS cleaning policy to adapt to changes in disk utilization, and by using cached data to lower cleaning costs. Second, we show how to improve LFS read performance by reorganizing data to match read patterns. Using trace-driven simulations on a combination of synthetic and measured workloads, we demonstrate that these extensions to LFS can significantly improve its performance.