Nearest-neighbor mapping of finite element graphs onto processor meshes
IEEE Transactions on Computers
Load Balancing in Distributed Systems: An Approach Using Cooperative Games
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Optimal state-free, size-aware dispatching for heterogeneous M/G/-type systems
Performance Evaluation - Performance 2005
Journal of Parallel and Distributed Computing
Noncooperative load balancing in distributed systems
Journal of Parallel and Distributed Computing
IEEE Transactions on Parallel and Distributed Systems
Load balancing in processor sharing systems
Proceedings of the 3rd International Conference on Performance Evaluation Methodologies and Tools
Price of anarchy in non-cooperative load balancing
INFOCOM'10 Proceedings of the 29th conference on Information communications
Future Generation Computer Systems
Game-theoretic static load balancing for distributed systems
Journal of Parallel and Distributed Computing
Decentralized management of bi-modal network resources in a distributed stream processing platform
Journal of Parallel and Distributed Computing
Price of anarchy in non-cooperative load balancing games
Performance Evaluation
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
A distributed dynamic load balancer for iterative applications
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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A loosely coupled multiprocessor system contains multiple processors which have their own local memories. To balance the load among multiple processors is of fundamental importance in enhancing the performance of such a multiple processor system. Probabilistic load balancing in a heterogeneous multiple processor system with many job classes is considered in this study. The load balancing scheme is formulated as a nonlinear programming problem with linear constraints. An optimal probabilistic load balancing algorithm is proposed to solve this nonlinear programming problem. The proposed load balancing method is proven globally optimum in the sense that it results in a minimum overall average job response time on a probabilistic basis.