Robust Monitoring of Network-wide Aggregates through Gossiping

We examine the use of gossip protocols for continuous monitoring of network-wide aggregates. Aggregates are computed from local management variables using functions such as AVERAGE, MIN, MAX, or SUM. A particular challenge is to develop a gossip-based aggregation protocol that is robust against node failures. In this paper, we present G-GAP, a gossip protocol for continuous monitoring of aggregates, which is robust against discontiguous failures (i.e., under the constraint that neighboring nodes do not fail within a short period of each other). We formally prove this property, and we evaluate the protocol through simulation using real traces. The simulation results suggest that the design goals for this protocol have been met. For instance, the tradeoff between estimation accuracy and protocol overhead can be controlled, and a high estimation accuracy (below some S% error in our measurements) is achieved by the protocol, even for large networks and frequent node failures. Further, we perform a comparative assessment of G-GAP against a tree-based aggregation protocol using simulation. Surprisingly, we find that the tree-based aggregation protocol consistently outperforms the gossip protocol for comparative overhead, both in terms of accuracy and robustness.