Streaming Communication Protocols

TL;DR

This paper introduces the Streaming Communication model, blending communication complexity and streaming, and analyzes resource tradeoffs for canonical problems and approximate matching, revealing new complexity insights.

Contribution

It defines a new model combining streaming and communication complexity and provides tight bounds and analysis for canonical problems and approximate matching.

Findings

Established tight tradeoffs between communication and memory resources.

Proved a strong lower bound technique for canonical problems.

Showed the complexity of approximate matching differs from traditional models.

Abstract

We define the Streaming Communication model that combines the main aspects of communication complexity and streaming. We consider two agents that want to compute some function that depends on inputs that are distributed to each agent. The inputs arrive as data streams and each agent has a bounded memory. Agents are allowed to communicate with each other and also update their memory based on the input bit they read and the previous message they received. We provide tight tradeoffs between the necessary resources, i.e. communication and memory, for some of the canonical problems from communication complexity by proving a strong general lower bound technique. Second, we analyze the Approximate Matching problem and show that the complexity of this problem (i.e. the achievable approximation ratio) in the one-way variant of our model is strictly different both from the streaming complexity and the one-way communication complexity thereof.