A simple parallel algorithm for the maximal independent set problem
SIAM Journal on Computing
A parallel approximation algorithm for positive linear programming
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
A primal-dual parallel approximation technique applied to weighted set and vertex covers
Journal of Algorithms
On the complexity of distributed network decomposition
Journal of Algorithms
A unified approach to approximating resource allocation and scheduling
Journal of the ACM (JACM)
Constant-time distributed dominating set approximation
Proceedings of the twenty-second annual symposium on Principles of distributed computing
On the locality of bounded growth
Proceedings of the twenty-fourth annual ACM symposium on Principles of distributed computing
The price of being near-sighted
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
A Primal-Dual Bicriteria Distributed Algorithm for Capacitated Vertex Cover
SIAM Journal on Computing
Dominating sets of agents in visibility graphs: distributed algorithms for art gallery problems
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Peer-assisted texture streaming in metaverses
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Distributed algorithms for scheduling on line and tree networks
PODC '12 Proceedings of the 2012 ACM symposium on Principles of distributed computing
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In this paper we give an efficient distributed algorithm computing approximate solutions to a very general, and classical, scheduling problem. The approximation guarantee is within a constant factor of the optimum. By "efficient", we mean that the number of communication rounds is poly-logarithmic in the size of the input. In the problem, we have a bipartite graph with computing agents on one side and resources on the other. Agents that share a resource can communicate in one time step. Each agent has a list of jobs, each with its own length and profit, to be executed on a neighbouring resource within a given time-window. Resources can execute non preemptively only one job at a time. The goal is to maximize the profit of the jobs that are scheduled. It is well known that this problem is NP-hard. A very interesting feature of our algorithm is that it is derived in a systematic manner from a primal-dual algorithm.