Simulations of three adaptive, decentralized controlled, job scheduling algorithms
Computer Networks and ISDN Systems
Adaptive load sharing in homogeneous distributed systems
IEEE Transactions on Software Engineering
Distributed Scheduling of Tasks with Deadlines and Resource Requirements
IEEE Transactions on Computers
Load Sharing in Distributed Real-Time Systems with State-Change Broadcasts
IEEE Transactions on Computers
Analysis of the Effects of Delays on Load Sharing
IEEE Transactions on Computers
Performance prediction of distributed load balancing on multicomputer systems
Proceedings of the 1991 ACM/IEEE conference on Supercomputing
Processor allocation for a class of hypercube-like supercomputers
Proceedings of the 1992 ACM/IEEE conference on Supercomputing
Fault-tolerant task management and load re-distribution on massively parallel hypercube systems
Proceedings of the 1992 ACM/IEEE conference on Supercomputing
Periodic hierarchical load balancing for large supercomputers
International Journal of High Performance Computing Applications
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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This paper presents a semi distributed approach for task scheduling in large parallel and distributed systems which is different from the conventional centralized and fully distributed approaches. The proposed strategy partitions the system into independent regions (spheres) centered at some control points. The central points called schedulers, optimally schedule tasks within their spheres and maintain state information with low overhead. We consider Hypercube systems for evaluation and using its algebraic characteristics, show that identification of spheres and their scheduling points is an NP-complete problem. An efficient solution for this problem is presented by making an exclusive use of a combinatorial structure known as Hadamard Matrix. Performance of the proposed strategy was evaluated and compared with an efficient fully distributed strategy, through simulation. In addition to yielding high performance in terms of response time, better resource utilization and throughput, the proposed strategy is shown to incur small overhead in terms of network traffic.