Token-based Function Computation with Memory
This work addresses distributed computing efficiency for networks, offering incremental improvements over existing methods like Coalescing Random Walk.
The paper tackles the problem of distributed function computation by proposing a token-based algorithm (TCM) that accelerates token meetings with a novel chasing mechanism, resulting in time complexity reductions by factors such as at least √(n/log(n)) in Erdös-Renyi and complete graphs and log(n)/log(log(n)) in torus networks, and at least constant factor improvements in message complexity across all considered topologies.
In distributed function computation, each node has an initial value and the goal is to compute a function of these values in a distributed manner. In this paper, we propose a novel token-based approach to compute a wide class of target functions to which we refer as "Token-based function Computation with Memory" (TCM) algorithm. In this approach, node values are attached to tokens and travel across the network. Each pair of travelling tokens would coalesce when they meet, forming a token with a new value as a function of the original token values. In contrast to the Coalescing Random Walk (CRW) algorithm, where token movement is governed by random walk, meeting of tokens in our scheme is accelerated by adopting a novel chasing mechanism. We proved that, compared to the CRW algorithm, the TCM algorithm results in a reduction of time complexity by a factor of at least $\sqrt{n/\log(n)}$ in Erdös-Renyi and complete graphs, and by a factor of $\log(n)/\log(\log(n))$ in torus networks. Simulation results show that there is at least a constant factor improvement in the message complexity of TCM algorithm in all considered topologies. Robustness of the CRW and TCM algorithms in the presence of node failure is analyzed. We show that their robustness can be improved by running multiple instances of the algorithms in parallel.