Data networks
General purpose parallel architectures
Handbook of theoretical computer science (vol. A)
Introduction to parallel algorithms and architectures: array, trees, hypercubes
Introduction to parallel algorithms and architectures: array, trees, hypercubes
Greedy packet scheduling on shortest paths
Journal of Algorithms
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
An engineering approach to computer networking: ATM networks, the Internet, and the telephone network
Adaptive packet routing for bursty adversarial traffic
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Scheduling time-constrained communication in linear networks
Proceedings of the tenth annual ACM symposium on Parallel algorithms and architectures
Time-constrained scheduling of weighted packets on trees and meshes
Proceedings of the eleventh annual ACM symposium on Parallel algorithms and architectures
Scheduling in Synchronous Networks and the Greedy Algorithm (Extended Abstract)
WDAG '97 Proceedings of the 11th International Workshop on Distributed Algorithms
Fast algorithms for finding O(congestion+dilation) packet routing schedules
HICSS '95 Proceedings of the 28th Hawaii International Conference on System Sciences
Universal stability results for greedy contention-resolution protocols
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
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We study the average number of delays suffered by packets routed using greedy (work conserving) scheduling policies. We obtain tight bounds on the worst-case average number of delays in a few cases as follows. First, we show that the average number of delays is a function of the number of sources of packets, which is interesting in case a node may send many packets. Then, using a new concept we call delay race, we prove a tight bound on the average number of delays in a leveled graph. Finally, using delay races in a more involved way, we prove nearly-tight bounds on the average number of delays in directed acyclic graphs (DAGs). The upper bound for DAGs is expressed in terms of the underlying topology, and as a result it holds for any acyclic set of routes, even if they are not shortest paths. The lower bound for DAGs, on the other hand, holds even for shortest paths routes.