Optimal static load balancing in distributed computer systems
Journal of the ACM (JACM)
An Algorithm for Optimal Static Load Balancing in Distributed Computer Systems
IEEE Transactions on Computers
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Load Balancing Problems for Multiclass Jobs in Distributed/Parallel Computer Systems
IEEE Transactions on Computers
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Optimal balancing of I/O requests to disks
Communications of the ACM
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
A Decomposition Algorithm for Optimal Static Load Balancing in Tree Hierarchy Network Configurations
IEEE Transactions on Parallel and Distributed Systems
Hybrid crossover operators for real-coded genetic algorithms: an experimental study
Soft Computing - A Fusion of Foundations, Methodologies and Applications
An Application of Bayesian Decision Theory to Decentralized Control of Job Scheduling
IEEE Transactions on Computers
Adaptive Routing Using a Virtual Waiting Time Technique
IEEE Transactions on Software Engineering
Optimal Load Balancing in a Multiple Processor System with Many Job Classes
IEEE Transactions on Software Engineering
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
ACSAC'05 Proceedings of the 10th Asia-Pacific conference on Advances in Computer Systems Architecture
Hi-index | 0.00 |
We consider the problem of static load balancing with the objective of minimizing the job response times. The jobs that arrive at a central scheduler are allocated to various processors in the system with certain probabilities. This optimization problem is solved using real-coded genetic algorithms. A comparison of this approach with the standard optimization methods are presented.