The policy in dynamic load balancing, classification and function are variety based on the focus study for each research. They are different but employing the same strategy to obtain the load balancing. The communication processes between policies are explored within the dynamic load balancing and decentralized approaches. Multi-agent system characteristics and capabilities are explored too. The unique capabilities offered by multi-agent systems can be integrated or combined with the structure of dynamic load balancing to produce a better strategy to produce a better dynamic load balancing algorithm with multi-agent systems.
A grid resource broker seeks to assign the appropriate jobs to the appropriate resources as part of resource management in the multi-grid environment. Multi instances of the broker system provides multiple instances of brokers to simultaneously process jobs between multiple resources in a hierarchical cluster grid environment. In this study, the multi-instance broker is developed using grid resource broker taxonomy properties. The number of broker instances to be used for each processing session is determined by calculating resources, computing power and workload. The Self-Adaptive Multi-Instance Broker Scheduling algorithm SAMiB was tested against iHLBA algorithm through four types of scenarios containing various mixes of background load and CPU speed. The SAMiB algorithm has achieved a decrease of 14.93% in makespan time for 2000 jobs, proving the suitability of the multiinstance broker concept for the hierarchical cluster grid environment.
This paper discusses the impact of background workload for idle re-source items of computing elements in grid computing. Most of the previous re-search did not consider this factor. A resource item may be processing local system operations when the grid perceives them to be idle, thus upsetting grid processing activities. The introduction of the resource items and background load factor in this study will reveal the true computing capability of computing elements. This background load factor, represented in the form of weightage on resource item, is tested to seek overall grid performance. By allocating the right balance of workload weightage of resource item in a computing element, a significant improvement in processing performance is achieved.
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