Abstract—Consider a network of, say, sensors, or P2P nodes, or bluetooth-enabled cell-phones, where nodes transmit information to each other and where links and nodes can go up or down. Consider also a ‘datum’, that is, a piece of information, like a report of an emergency condition in a sensor network, a national traditional song, or a mobile phone virus. How often should nodes transmit the datum to each other, so that the datum can survive (or, in the virus case, under what conditions will the virus die out)? Clearly, the link and node fault probabilities are important — what else is needed to ascertain the survivability of the datum? We propose and solve the problem using non-linear dynamical systems and fixed point stability theorems. We provide a closedform formula that, surprisingly, depends on only one additional parameter, the largest eigenvalue of the connectivity matrix. We illustrate the accuracy of our analysis on realistic and real settings, like mote sensor networks from Intel and MIT, as well as Gnutella and P2P networks. I.

Abstract. Gossip protocols have proven to be effective means by which failures can be detected in large, distributed systems in an asynchronous manner without the limitations associated with reliable multicasting for group communications. In this paper, we discuss the development and features of a Gossip-Enabled Monitoring Service (GEMS), a highly responsive and scalable resource monitoring service, to monitor health and performance information in heterogeneous distributed systems. GEMS has many novel and essential features such as detection of network partitions and dynamic insertion of new nodes into the service. Easily extensible, GEMS also incorporates facilities for distributing arbitrary system and application-specific data. We present experiments and analytical projections demonstrating scalability, fast response times and low resource utilization requirements, making GEMS a potent solution for resource monitoring in distributed computing.

Abstract – Gossip protocols provide a scalable means for detecting failures in heterogeneous distributed systems in an asynchronous manner without the limits associated with group communication. In this paper, we discuss the development and features of a hierarchical Gossip-Enabled Monitoring Service (GEMS), which extends the gossip-style failure detection service to support resource monitoring. By dividing the system into groups of nodes and layers of communication, the GEMS paradigm scales well. Easily extensible, GEMS incorporates facilities for distributing arbitrary system and application-specific data. In this paper we present experiments and analytical projections demonstrating fast response times and low resource utilization requirements, making GEMS a superior solution for resource monitoring issues in distributed computing. Also, we demonstrate the utility of GEMS through the development of a simple dynamic load balancing service for which GEMS forms the information base.