Optimizing Real-Time Ordered-Data Broadcasts in Pervasive Environments Using Evolution Strategy

Authors:
Rinku Dewri;Darrell Whitley;Indrajit Ray;Indrakshi Ray
Affiliations:
Colorado State University, Fort Collins, USA CO 80523;Colorado State University, Fort Collins, USA CO 80523;Colorado State University, Fort Collins, USA CO 80523;Colorado State University, Fort Collins, USA CO 80523
Venue:
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Year:
2008

Citing 7
Cited 1

Efficient algorithms for scheduling data broadcast

Wireless Networks
Object Organization on a Single Broadcast Channel in the Mobile Computing Environment

Multimedia Tools and Applications
Value vs. deadline scheduling in overload conditions

RTSS '95 Proceedings of the 16th IEEE Real-Time Systems Symposium
Dependent Data Broadcasting for Unordered Queries in a Multiple Channel Mobile Environment

IEEE Transactions on Knowledge and Data Engineering
On Recent Advances in Time/Utility Function Real-Time Scheduling and Resource Management

ISORC '05 Proceedings of the Eighth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing
Utility Accrual Real-Time Scheduling under Variable Cost Functions

RTCSA '05 Proceedings of the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
Scheduling real-time requests in on-demand data broadcast environments

Real-Time Systems

Exploiting replication on dependent data allocation for ordered queries over multiple broadcast channels

Wireless Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider the problem of real-time data broadcast scheduling in pervasive systems with soft deadlines and constraints on the order in which data items should be broadcast to be useful. The broadcast schedule needs to be generated to provide a certain level of quality of service. Thus, the real-time scheduler has to effectively trade-off between its running time and the quality of schedules generated. We use an evolution strategy to solve the problem. The variants tested includes (1 + 茂戮驴)-ES, (1,茂戮驴)-ES, and a (2 + 1)-ES with a modified Syswerda recombination operator, as well as a genetic algorithm.