Energy-aware Load Balancing (EALB) is a technique in fog computing that aims to optimize energy consumption in the system by dynamically distributing workloads among fog nodes based on their energy levels. EALB aims to minimize energy waste and prolong the lifetime of fog nodes while maintaining performance and reliability.
It is achieved by continuously monitoring the energy status of fog nodes and assigning tasks based on the least energy-intensive node. EALB helps reduce operational costs and ensure efficient use of resources in fog computing environments.
An energy-aware load balancing model helps to develop performance in fog computing environments. The task clustering approach is utilized for load balancing to reduce power utilization in cloud data centers and reduce run time with an energy-aware load balancing technique.
Reducing execution time improves system performance and helps reduce energy consumption and implement outlay. It optimizes execution time and cost as all fog nodes are properly utilized.
• Energy Modeling: The energy consumption of fog nodes is modeled to estimate energy usage under different workloads and conditions.
• Energy Monitoring: The energy consumption of fog nodes is continuously monitored and recorded.
• Load Balancing Algorithms: Load balancing algorithms such as Round Robin, Least Load First, and Energy-Aware Scheduling distribute workloads among fog nodes based on their energy levels.
• Feedback Mechanism: A feedback mechanism provides information about the performance of fog nodes, allowing for dynamic adjustment of workload distribution.
• Centralized Load Balancing: A central controller is responsible for monitoring the energy levels of fog nodes and assigning tasks to them. This approach provides a centralized system view and allows for efficient load-balancing decisions.
• Distributed Load Balancing: Each fog node monitors its energy level and communicates with other fog nodes to distribute tasks. This approach allows for improved scalability and fault tolerance, eliminating the need for a central controller.
• Hybrid Load Balancing: In this strategy, a combination of centralized and distributed load balancing techniques is used to achieve the best balance between performance, scalability, and reliability.
• Energy-aware Scheduling: The load-balancing decisions are based on the energy levels of fog nodes and the energy requirements of tasks. Energy-aware scheduling aims to minimize energy waste while ensuring that tasks are completed within a specified deadline.
• Power Management: Power management techniques are used to control the energy consumption of fog nodes and include techniques such as dynamic voltage and frequency scaling, power capping, and sleep modes.
• Unbalanced energy consumption: The lack of energy-aware load balancing can result in unbalanced energy consumption, leading to waste and reduced efficiency.
• Poor system performance: Unbalanced loads and inadequate resource utilization can result in poor system performance, including increased response times and reduced system throughput.
• Balancing trade-offs: Energy-aware load balancing requires balancing trade-offs between energy efficiency and other performance metrics such as response time and system throughput.
• Predictive energy management: Predictive energy management techniques can predict future energy requirements and adjust load balancing accordingly, resulting in more efficient energy usage.
• Collaborative load balancing: Investigating the potential for collaboration between fog nodes to optimize energy consumption and improve the overall efficiency of the system.
• Integration with renewable energy sources: Integrating renewable energy sources, such as solar and wind power, into fog computing to reduce energy consumption and improve sustainability.
• Energy-Aware Next-Generation Mobile Routing Chains with Fog Computing for Emerging Applications.
• A new energy‐aware method for load balance managing in the fog‐based vehicular ad hoc networks (VANET) using a hybrid optimization algorithm.
• An energy-efficient load balancing approach for scientific workflows in fog computing.
