Adaptability and the Usefulness of Hints (Extended Abstract)
ESA '98 Proceedings of the 6th Annual European Symposium on Algorithms
Self-tuning reactive diffracting trees
Journal of Parallel and Distributed Computing
Writing-all deterministically and optimally using a nontrivial number of asynchronous processors
ACM Transactions on Algorithms (TALG)
Compositional competitiveness for distributed algorithms
Journal of Algorithms
Asynchronous throughput-optimal routing in malicious networks
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming: Part II
Performing dynamically injected tasks on processes prone to crashes and restarts
DISC'11 Proceedings of the 25th international conference on Distributed computing
On finding better friends in social networks
SSS'12 Proceedings of the 14th international conference on Stabilization, Safety, and Security of Distributed Systems
Online parallel scheduling of non-uniform tasks: trading failures for energy
FCT'13 Proceedings of the 19th international conference on Fundamentals of Computation Theory
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We introduce a theory of competitive analysis for distributed algorithms. The first steps in this direction were made in the seminal papers of Y. Bartal et al. (1992), and of B. Awerbuch et al. (1992), in the context of data management and job scheduling. In these papers, as well as in other subsequent sequent work, the cost of a distributed algorithm is compared to the cost of an optimal global-control algorithm. In this paper we introduce a more refined notion of competitiveness for distributed algorithms, one that reflects the performance of distributed algorithms more accurately. In particular, our theory allows one to compare the cost of a distributed on-line algorithm to the cost of an optimal distributed algorithm. We demonstrate our method by studying the cooperative collect primitive, first abstracted by M. Saks, N. Shavit, and H. Woll (1991). We provide the first algorithms that allow processes to cooperate to finish their work in fewer steps. Specifically, we present two algorithms (with different strengths), and provide a competitive analysis for each one.