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Load-Balancing Strategy: Employing a Capsule Algorithm for Cutting Down Energy Consumption in Cloud Data Centers for
Jyoti Singh1, Jingchao Chen1, Santar Pal Singh2
1College of Information Science & Technology, Donghua University, Shanghai, China.
Computational Intelligence and Neuroscience
|March 2, 2023
Summary
Cloud computing data centers can conserve energy using the Capsule Significance Level of Energy Consumption (CSLEC) pattern. This method balances performance and energy use, achieving high accuracy in predictions.
Area of Science:
- Computer Science
- Energy Efficiency
- Cloud Computing
Background:
- Cloud computing leverages virtualization and data centers for remote services.
- Traditionally, cloud data centers prioritize performance over energy efficiency.
- Balancing performance and energy consumption is a key challenge in cloud infrastructure.
Purpose of the Study:
- To introduce a novel pattern for energy conservation in cloud data centers.
- To address the challenge of reducing energy consumption without impacting performance.
- To provide a method for understanding and optimizing cloud energy usage.
Main Methods:
- Utilized the PlanetLab dataset for analysis.
- Developed the Capsule Significance Level of Energy Consumption (CSLEC) pattern.
- Employed optimization criteria and energy consumption models for strategy implementation.
Main Results:
- The CSLEC pattern demonstrates effective energy conservation strategies for cloud data centers.
- Achieved a 96.7% F1-score in the prediction phase of capsule optimization.
- Attained 97% data accuracy, enabling precise future value projections.
Conclusions:
- The CSLEC pattern offers a viable solution for enhancing energy efficiency in cloud data centers.
- Accurate energy consumption modeling is crucial for effective optimization.
- The proposed method successfully balances system performance with reduced energy usage.
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