Implementing Atomic Data through Indirect Learning in Dynamic Networks

Abstract

Developing middleware services for dynamic distributed systems, e.g., ad-hoc networks, is a challenging task given that such services deal with dynamically changing membership and asynchronous communication. Algorithms developed for static settings are often not usable in such settings because they rely on (logical) all-to-all node connectivity through routing protocols, which may be unfeasible or prohibitively expensive to implement in highly dynamic settings. This paper explores the indirect learning, via periodic gossip, approach to information dissemination within a dynamic, distributed data service implementing atomic read/write memory service. The indirect learning scheme is used to improve the liveness of the service in the settings with uncertain connectivity. The service is formally proved to guarantee atomicity in all executions. Conditional performance analysis of the new service is presented, where this analysis has the potential of being generalized to other similar dynamic algorithms. Under the assumption that the network is connected, and assuming reasonable timing conditions, the bounds on the duration of read/write operations of the new service are calculated. Finally, the paper proposes a deployment strategy where indirect learning leads to an improvement in communication costs relative to a previous solution that assumes all-to-all connectivity.