Anzeige
Erschienen in:
2015 | OriginalPaper | Buchkapitel
A Case for Epidemic Fault Detection and Group Membership in HPC Storage Systems
verfasst von : Shane Snyder, Philip Carns, Jonathan Jenkins, Kevin Harms, Robert Ross, Misbah Mubarak, Christopher Carothers
Erschienen in: High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Fault response strategies are crucial to maintaining performance and availability in HPC storage systems, and the first responsibility of a successful fault response strategy is to detect failures and maintain an accurate view of group membership. This is a nontrivial problem given the unreliable nature of communication networks and other system components. As with many engineering problems, trade-offs must be made to account for the competing goals of fault detection efficiency and accuracy.
Today’s production HPC services typically rely on distributed consensus algorithms and heartbeat monitoring for group membership. In this work, we investigate epidemic protocols to determine whether they would be a viable alternative. Epidemic protocols have been proposed in previous work for use in peer-to-peer systems, but they have the potential to increase scalability and decrease fault response time for HPC systems as well. We focus our analysis on the Scalable Weakly-consistent Infection-style Process Group Membership (SWIM) protocol.
We begin by exploring how the semantics of this protocol differ from those of typical HPC group membership protocols, and we discuss how storage systems might need to adapt as a result. We use existing analytical models to choose appropriate SWIM parameters for an HPC use case. We then develop a new, high-resolution parallel discrete event simulation of the protocol to confirm existing analytical models and explore protocol behavior that cannot be readily observed with analytical models. Our preliminary results indicate that the SWIM protocol is a promising alternative for group membership in HPC storage systems, offering rapid convergence, tolerance to transient network failures, and minimal network load.