Time-Space Trade-offs in Population Protocols

TL;DR

This paper investigates the fundamental trade-off between time and space complexity in population protocols, establishing new bounds and algorithms for leader election and majority tasks, highlighting that faster stabilization requires more memory per agent.

Contribution

It provides the first unified lower bound relating space and time in population protocols and presents algorithms achieving poly-logarithmic stabilization time with feasible space complexity.

Findings

Any protocol with O(log log n) states takes at least n/polylog n expected time.

Algorithms exist that stabilize in O(log^2 n) time using O(log^2 n) space.

There is a clear separation between O(log log n) and O(log^2 n) space complexities for fast protocols.

Abstract

Population protocols are a popular model of distributed computing, in which randomly-interacting agents with little computational power cooperate to jointly perform computational tasks. Inspired by developments in molecular computation, and in particular DNA computing, recent algorithmic work has focused on the complexity of solving simple yet fundamental tasks in the population model, such as leader election (which requires stabilization to a single agent in a special "leader" state), and majority (in which agents must stabilize to a decision as to which of two possible initial states had higher initial count). Known results point towards an inherent trade-off between the time complexity of such algorithms, and the space complexity, i.e. size of the memory available to each agent. In this paper, we explore this trade-off and provide new upper and lower bounds for majority and leader election. First, we prove a unified lower bound, which relates the space available per node with the time complexity achievable by a protocol: for instance, our result implies that any protocol solving either of these tasks for $n$ agents using $O( \log \log n )$ states must take $\Omega( n / \rm{polylog} n )$ expected time. This is the first result to characterize time complexity for protocols which employ super-constant number of states per node, and proves that fast, poly-logarithmic running times require protocols to have relatively large space costs. On the positive side, we give algorithms showing that fast, poly-logarithmic stabilization time can be achieved using $O( \log^2 n )$ space per node, in the case of both tasks. Overall, our results highlight a time complexity separation between $O(\log \log n)$ and $\Theta( \log^2 n )$ state space size for both majority and leader election in population protocols, and introduce new techniques, which should be applicable more broadly.