Michael Vrable

Difference engine: harnessing memory redundancy in virtual machines

Virtual machine monitors (VMMs) are a popular platform for Internet hosting centers and cloud-based compute services. By multiplexing hardware resources among virtual machines (VMs) running commodity operating systems, VMMs decrease both the capital outlay and management overhead of hosting centers. Appropriate placement and migration policies can take advantage of statistical multiplexing to effectively utilize available processors. However, main memory is not amenable to such multiplexing and is often the primary bottleneck in achieving higher degrees of consolidation. Previous efforts have shown that content-based page sharing provides modest decreases in the memory footprint of VMs running similar operating systems and applications. Our studies show that significant additional gains can be had by leveraging both subpage level sharing (through page patching) and incore memory compression. We build Difference Engine, an extension to the Xen VMM, to support each of these---in addition to standard copy-on-write full-page sharing---and demonstrate substantial savings across VMs running disparate workloads (up to 65%). In head-to-head memory-savings comparisons, Difference Engine outperforms VMware ESX server by a factor 1.6--2.5 for heterogeneous workloads. In all cases, the performance overhead of Difference Engine is less than 7%.

Scalability, fidelity, and containment in the potemkin virtual honeyfarm

The rapid evolution of large-scale worms, viruses and bot-nets have made Internet malware a pressing concern. Such infections are at the root of modern scourges including DDoS extortion, on-line identity theft, SPAM, phishing, and piracy. However, the most widely used tools for gathering intelligence on new malware -- network honeypots -- have forced investigators to choose between monitoring activity at a large scale or capturing behavior with high fidelity. In this paper, we describe an approach to minimize this tension and improve honeypot scalability by up to six orders of magnitude while still closely emulating the execution behavior of individual Internet hosts. We have built a prototype honeyfarm system, called Potemkin, that exploits virtual machines, aggressive memory sharing, and late binding of resources to achieve this goal. While still an immature implementation, Potemkin has emulated over 64,000 Internet honeypots in live test runs, using only a handful of physical servers.

Cumulus: Filesystem backup to the cloud

Cumulus is a system for efficiently implementing filesystem backups over the Internet, specifically designed under a thin cloud assumption—that the remote datacenter storing the backups does not provide any special backup services, but only a least-common-denominator storage interface. Cumulus aggregates data from small files for storage and uses LFS-inspired segment cleaning to maintain storage efficiency. While Cumulus can use virtually any storage service, we show its efficiency is comparable to integrated approaches.

Brief announcement: the overlay network content distribution problem

Many overlay multicast protocols have been designed and deployed across the Internet to support content distribution. To our knowledge, however, none have provided a rigorous analysis of the problem or the effectiveness of their proposed solutions. We define the Overlay Network Content Distribution (OCD) problem to allow such analyses.