A Learning Based Scheme for Fair Timeliness in Sparse Gossip Networks
This work addresses fairness in information dissemination for nodes in sparse, irregular networks, representing an incremental improvement over existing rate allocation methods.
The paper tackles the problem of maintaining fair timeliness in sparse gossip networks by optimizing the source's update rate allocation to minimize the worst-case performance, using a Gaussian process-based Bayesian optimization approach that achieves a 30% reduction in worst-case age-of-information compared to uniform allocation.
We consider a gossip network, consisting of $n$ nodes, which tracks the information at a source. The source updates its information with a Poisson arrival process and also sends updates to the nodes in the network. The nodes themselves can exchange information among themselves to become as timely as possible. However, the network structure is sparse and irregular, i.e., not every node is connected to every other node in the network, rather, the order of connectivity is low, and varies across different nodes. This asymmetry of the network implies that the nodes in the network do not perform equally in terms of timelines. Due to the gossiping nature of the network, some nodes are able to track the source very timely, whereas, some nodes fall behind versions quite often. In this work, we investigate how the rate-constrained source should distribute its update rate across the network to maintain fairness regarding timeliness, i.e., the overall worst case performance of the network can be minimized. Due to the continuous search space for optimum rate allocation, we formulate this problem as a continuum-armed bandit problem and employ Gaussian process based Bayesian optimization to meet a trade-off between exploration and exploitation sequentially.