An Optimized Framework for Energy-Resource Allocation in A Cloud Environment based on the Whale Optimization...

Area of Science:

• Computer Science
• Cloud Computing
• Optimization Algorithms

Background:

• Cloud computing enables remote data access and manipulation, offering an innovative alternative to physical infrastructure investments.
• Increasingly, cloud infrastructure faces challenges related to high energy consumption and the need for efficient resource scheduling.
• Effective load balancing is crucial for distributing workloads across servers to meet escalating user demands.

Purpose of the Study:

• To investigate and compare the effectiveness of various optimization algorithms for enhancing cloud computing environments.
• To address the critical issues of energy consumption and resource scheduling within cloud infrastructure.
• To evaluate the performance of load balancing techniques for optimizing server effectiveness.

Main Methods:

• The study employed several optimization algorithms: Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO), BAT, Cuckoo Search Algorithm (CSA), and Whale Optimization Algorithm (WOA).
• These algorithms were applied to optimize load balancing, energy efficiency, and resource scheduling in a cloud environment.
• Performance was evaluated using configurations with seven and eight servers.

Main Results:

• The Whale Optimization Algorithm (WOA) demonstrated superior performance compared to other tested algorithms.
• WOA achieved better results in terms of reduced response time and energy consumption.
• The algorithm also showed improvements in execution time and throughput, indicating enhanced overall efficiency.

Conclusions:

• The Whale Optimization Algorithm is highly effective for load balancing and resource scheduling in cloud computing.
• Implementing WOA can lead to significant reductions in energy consumption for cloud infrastructure.
• This optimization approach contributes to creating more efficient and responsive cloud environments.