Scheduling on-demand broadcasts: new metrics and algorithms
MobiCom '98 Proceedings of the 4th annual ACM/IEEE international conference on Mobile computing and networking
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
On the Complexity of Computing Minimum Energy Consumption Broadcast Subgraphs
STACS '01 Proceedings of the 18th Annual Symposium on Theoretical Aspects of Computer Science
Scheduling Broadcasts in Wireless Networks
ESA '00 Proceedings of the 8th Annual European Symposium on Algorithms
A Comparison of Multicast Pull Models
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Speed is as powerful as clairvoyance [scheduling problems]
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Using Cooperative Mediation to Solve Distributed Constraint Satisfaction Problems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Energy-Efficient broadcast scheduling for speed-controlled transmission channels
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
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Given a set of requests, we tackle the problem of finding ‘good' broadcast schedules aiming at the minimization of their total flow time. While running at a fixed speed, in the considered model the server is only allowed to use a certain amount of energy to perform these broadcasts. For this task we present optimal and approximation algorithms, respectively, depending on the number of distinct request types and their transmission lengths. The problem is solvable within polynomial time in the offline setting if the transmission lengths of all request types are identical and the number of distinct request types is constant. The presented algorithm can be generalized to obtain an approximation on instances without identical transmission lengths. Regarding the online version, we show lower and upper bounds on the competitive ratio of an optimal algorithm, including randomized algorithms and algorithms using resource augmentation. These lower and corresponding upper bounds match (at least asymptotically).