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Multiobjective dynamic resource allocation in cloud computing using Harris Hawk Optimization Algorithm (MDLB-HHO).
Varun C M1, Anto Kumar R P2, Paulraj D3
1Department of Computer Science and Business Systems, R.M.K. Engineering College, Kavaraipettai, Tamil Nadu, India.
Plos One
|June 25, 2026
Summary
The Harris Hawks Optimization (HHO) algorithm enhances cloud computing by dynamically balancing workloads across virtual machines. This approach improves resource utilization and reduces response times for cloud services.
Area of Science:
- Cloud Computing
- Artificial Intelligence
- Optimization Algorithms
Background:
- Efficient load balancing and resource distribution are crucial for cloud computing utilization and performance.
- Dynamic load balancing in cloud systems presents significant challenges in optimizing workload and resource allocation.
- Existing methods often struggle to adapt to the fluctuating demands of cloud environments.
Purpose of the Study:
- To introduce and evaluate the Harris Hawks Optimization (HHO) algorithm for dynamic load balancing in cloud systems.
- To optimize workload distribution and resource utilization, thereby enhancing cloud service efficiency and sustainability.
- To adapt resource allocation schemes to changing cloud application requirements.
Main Methods:
- The Harris Hawks Optimization (HHO) algorithm, inspired by hawk hunting behavior, is employed for dynamic workload allocation.
- A multiobjective fitness function is utilized to maximize resource efficiency, minimize response time, and reduce resource usage.
- The algorithm dynamically assigns jobs to virtual machines (VMs) by adapting to changing workloads through iterative interactions and positional updates.
Main Results:
- The HHO algorithm demonstrates efficient and effective control of dynamic load balancing in cloud environments.
- Experimental analysis shows a decrease in response time and resource usage compared to other load-balancing methods.
- The multiobjective fitness function significantly improves overall performance, resource usage, and reaction time.
Conclusions:
- The HHO algorithm offers a cost-effective and efficient solution for dynamic load balancing in cloud computing.
- The proposed method enhances the effectiveness and robustness of cloud-based services in dynamic operational environments.
- The study confirms the HHO algorithm's capability to manage constantly changing task requirements while ensuring load balance and efficient resource use.
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