One of the key reasons overlay networks are seen as an excellent platform for large scale distributed systems is their resilience in the presence of node failures. This resilience rely on accurate and timely detection of node failures. Despite the prevalent use of keep-alive algorithms in overlay networks to detect node failures, their tradeoffs and the circumstances in which they might best he suited is not well understood. In this paper, we study how the design of various keep-alive approaches affect their performance in node failure detection time, probability of false positive, control overhead, and packet loss rate via analysis, simulation, and implementation. We find that among the class of keep-alive algorithms that share information, the maintenance of backpointer state substantially improves detection time and packet loss rate. The improvement in detection time between baseline and sharing algorithms becomes more pronounced as the size of neighbor set increases. Finally, sharing of information allows a network to tolerate a higher churn rate than baseline.

OceanStore is a utility infrastructure designed to span the globe and provide continuous access to persistent information. Since this infrastructure is comprised of untrusted servers, data is protected through redundancy and cryptographic techniques. To improve performance, data is allowed to be cached anywhere, anytime. Additionally, monitoring of usage patterns allows adaptation to regional outages and denial of service attacks; monitoring also enhances performance through pro-active movement of data. A prototype implementation is currently under development.