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Hybrid deep learning and optimized clustering mechanism for load balancing and fault tolerance in cloud computing
Vahini Siruvoru1,2, Shivampeta Aparna1
1Research Scholar, GITAM-School of Technology, Hyderabad, India.
Network (Bristol, England)
|June 27, 2024
Summary
This study introduces a hybrid Deep Learning algorithm for efficient cloud load balancing. The novel approach optimizes resource allocation, enhancing energy efficiency and network reliability in cloud services.
Area of Science:
- Computer Science
- Artificial Intelligence
- Cloud Computing
Background:
- Load balancing is critical for energy efficiency in developing cloud services.
- Fault tolerance is essential for network reliability and accessibility.
Purpose of the Study:
- To develop a hybrid Deep Learning-based load balancing algorithm.
- To improve energy efficiency and reliability in cloud environments.
Main Methods:
- Tasks initially allocated via round-robin.
- Deep Embedding Cluster (DEC) identifies overloaded/underloaded VMs using CPU, memory, and bandwidth.
- Deep Q Recurrent Neural Network (DQRNN) balances load considering supply, demand, capacity, and fault tolerance.
Main Results:
- The hybrid model achieved ideal values for load (0.147), capacity (0.726), resource consumption (0.527), and success rate (0.895).
- Demonstrated effectiveness in load balancing and resource utilization.
Conclusions:
- The proposed Deep Learning algorithm effectively balances load in cloud services.
- The hybrid approach enhances energy efficiency and network reliability.
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