Dennis Geels

OceanStore: an architecture for global-scale persistent storage

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.

Maintenance-free global data storage

Explores mechanisms for storage-level management in OceanStore, a global-scale distributed storage utility infrastructure, designed to scale to billions of users and exabytes of data. OceanStore automatically recovers from server and network failures, incorporates new resources and adjusts to usage patterns. It provides its storage platform through adaptation, fault tolerance and repair. The only role of human administrators in the system is to physically attach or remove server hardware. Of course, an open question is how to scale a research prototype in such a way to demonstrate the basic thesis of this article - that OceanStore is self-maintaining. The allure of connecting millions or billions of components together is the hope that aggregate systems can provide scalability and predictable behavior under a wide variety of failures. The OceanStore architecture is a step towards this goal.

On failure detection algorithms in overlay networks

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.

Replica management should be a game

We believe that large-scale replica management solutions should be based on an economic model. In this paper, we discuss the benefits provided by an economic approach and outline important directions for future research.