A Dynamic Load-Balancing Policy with a Central Job Dispatcher (LBC)
IEEE Transactions on Software Engineering
Communications of the ACM
Customized dynamic load balancing for a network of workstations
Journal of Parallel and Distributed Computing
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Strategies for Dynamic Load Balancing on Highly Parallel Computers
IEEE Transactions on Parallel and Distributed Systems
A Practical Approach to Dynamic Load Balancing
IEEE Transactions on Parallel and Distributed Systems
An Efficient Dynamic Scheduling Algorithm for Multiprocessor Real-Time Systems
IEEE Transactions on Parallel and Distributed Systems
Theory and Practice in Parallel Job Scheduling
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
A Dynamic Load Balancing Algorithm Based on Distributed Database System
HPC '00 Proceedings of the The Fourth International Conference on High-Performance Computing in the Asia-Pacific Region-Volume 2 - Volume 2
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
Scheduling Proxy: Enabling Adaptive-Grained Scheduling for Global Computing System
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
A Real-Time Distributed Scheduling Service For Middleware Systems
WORDS '05 Proceedings of the 10th IEEE International Workshop on Object-Oriented Real-Time Dependable Systems
Hi-index | 0.00 |
Scheduling is an important issue in computational grid. In computational grid, computing resources are connected through networks. So, if we want to take advantage of computational grid, an efficient scheduling algorithm is necessary to assign jobs to the appropriate nodes. Our adaptive load sharing algorithms uses a timer to find a receiver/ sender. If receiver does not find a sender it broadcasts a message to decrease threshold. Similarly if sender does not find receiver within poll limit it broadcasts a message to increase the threshold. We implemented distributed algorithms using a decentralized approach that improves average response time of jobs. The job arrival process and the CPU service times are modeled using MlMIl queuing model. We compared the performance of our algorithms with similar algorithms in the literature. We present some results that verify the effectiveness of our scheme.