An adversarial model for distributed dynamic load balancing
Proceedings of the tenth annual ACM symposium on Parallel algorithms and architectures
Off-line admission control for general scheduling problems
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Convex quadratic and semidefinite programming relaxations in scheduling
Journal of the ACM (JACM)
Convex Quadratic Programming Relaxations for Network Scheduling Problems
ESA '99 Proceedings of the 7th Annual European Symposium on Algorithms
Journal of Parallel and Distributed Computing
Distributed Coordination of Massively Multi-Agent Systems
Massively Multi-Agent Technology
Using relative costs in workflow scheduling to cope with input data uncertainty
Proceedings of the 10th International Workshop on Middleware for Grids, Clouds and e-Science
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Scheduling a set of tasks on a set of machines so as to yield an efficient schedule is a basic problem in computer science and operations research. Most of the research on this problem incorporates the potentially unrealistic assumption that communication between the different machines is instantaneous. In this paper we remove this assumption and study the problem of network scheduling, where each job originates at some node of a network, and in order to be processed at another node must take the time to travel through the network to that node.Our main contribution is to give approximation algorithms and hardness proofs for fully general forms of the fundamental problems in network scheduling. We consider two basic scheduling objectives: minimizing the makespan and minimizing the average completion time. For the makespan, we prove small constant factor hardness-to-approximate and approximation results. For the average completion time, we give a log-squared approximation algorithm for the most general form of the problem. The techniques used in this approximation are fairly general and have several other applications. For example, we give the first nontrivial approximation algorithm to minimize the average weighted completion time of a set of jobs on related or unrelated machines, with or without a network.