Efficient parallel computing in distributed workstation environments
Parallel Computing
Exploiting process lifetime distributions for dynamic load balancing
ACM Transactions on Computer Systems (TOCS)
Exploiting local data in parallel array I/O on a practical network of workstations
Proceedings of the fifth workshop on I/O in parallel and distributed systems
Cluster I/O with River: making the fast case common
Proceedings of the sixth workshop on I/O in parallel and distributed systems
File Assignment in Parallel I/O Systems with Minimal Variance of Service Time
IEEE Transactions on Computers
Faster Collective Output through Active Buffering
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Performance comparisons of load balancing algorithms for I/O-intensive workloads on clusters
Journal of Network and Computer Applications
Task graph pre-scheduling, using Nash equilibrium in game theory
The Journal of Supercomputing
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Load balancing and task partitioning are important components of distributed computing. The optimum performance from the distributed computing system is achieved by using effective scheduling and load balancing strategy. Researchers have well explored CPU, memory, and I/O-intensive tasks scheduling, and load balancing techniques. But one of the main obstacles of the load balancing technique leads to the ignorance of applications having a mixed nature of tasks. This is because load balancing strategies developed for one kind of job nature are not effective for the other kind of job nature. We have proposed a load balancing scheme in this paper, which is known as Mixed Task Load Balancing (MTLB) for Cluster of Workstation (CW) systems. In our proposed MTLB strategy, pre-tasks are assigned to each worker by the master to eliminate the worker's idle time. A main feature of MTLB strategy is to eradicate the inevitable selection of workers. Furthermore, the proposed MTLB strategy employs Three Resources Consideration (TRC) for load balancing (CPU, Memory, and I/O). The proposed MTLB strategy has removed the overheads of previously proposed strategies. The measured results show that MTLB strategy has a significant improvement in performance.