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The Application of Fractal Transform and Entropy for Improving Fault Tolerance and Load Balancing in Grid Computing
Murad B Khorsheed1, Qasim M Zainel2, Oday A Hassen3
1College of Administration & Economics, University of Kirkuk, Kirkuk 36001, Iraq.
Entropy (Basel, Switzerland)
|December 18, 2020
Summary
This study enhances computational grids using fractal indexing and R-tree entropy for faster, fault-tolerant, and balanced performance. The new model improves execution time, throughput, and success rates in grid computing.
Area of Science:
- Computer Science
- Data Management
- Distributed Systems
Background:
- Computational grids require efficient indexing and management for reliability.
- Existing fractal indexing methods face challenges with long computing times and fault tolerance.
- Load balancing is crucial for effective grid infrastructure.
Purpose of the Study:
- To improve computational grid performance through an entropy-based fractal indexing scheme.
- To address fault tolerance and load balancing in grid environments.
- To reduce search times and enhance grid path efficiency.
Main Methods:
- Applied an entropy-based fractal indexing scheme combined with R-tree index structures.
- Developed a reduced logical network from the physical grid structure.
- Integrated fractal transform with entropy for balanced infrastructure and minimal faults.
- Utilized an optimization searching technique to determine the optimum number of nodes.
Main Results:
- Achieved faster indexing and querying in the grid environment.
- Demonstrated improved load balancing and fault tolerance.
- Showcased enhanced execution time, throughput, makespan, and latency.
- Reported a higher success rate compared to existing models.
Conclusions:
- The proposed model offers a more effective and reliable computational grid infrastructure.
- Integrating fractal transform with R-tree based entropy significantly boosts grid performance.
- The logical network approach optimizes search paths, reducing latency and improving fault tolerance.
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