Resource-Competitive Algorithms

Abstract

The point of adversarial analysis is to model the worst-case performance of an algorithm. Unfortunately, this analysis may not always reect performance in practice because the adversarial assumption can be overly pessimistic. In such cases, several techniques have been developed to provide a more refined understanding of how an algorithm performs e.g., competitive analysis, parameterized analysis, and the theory of approximation algorithms. Here, we describe an analogous technique called resource competitiveness, tailored for distributed systems. Often there is an operational cost for adversarial behavior arising from bandwidth usage, computational power, energy limitations, etc. Modeling this cost provides some notion of how much disruption the adversary can inict on the system. In parameterizing by this cost, we can design an algorithm with the following guarantee: if the adversary pays T, then the additional cost of the algorithm is some function of T. Resource competitiveness yields results pertaining to secure, fault tolerant, and efficient distributed computation. We summarize these results and highlight future challenges where we expect this algorithmic tool to provide new insights.