Cloud computing is exploding in network technology, spurred on by advances in communication technology, the rise of Internet usage, and the ability to solve scaling and global reach issues. Because the consumption of energy accounts for a significant portion of the costs associated with cloud computing—a green cloud arrangement could save energy and fundamentally decrease venture functional expenses, according to researchers published in IEEE Xplore.

Appropriate load balancing is one of the critical factors that help companies, and their customers, meet demands while lowering their capital expenses when dealing with reduced workloads. In a paper presented at the 2023 International Conference on Cloud Computing, Data Science & Engineering (Confluence), researchers compare a wide range of load-balancing techniques in the cloud to improve different performance metrics and help load-balancing. The paper also describes the relationship between green cloud computing and load balancing.

Techniques for Green Cloud Computing

Green cloud computing entails planning, production, and using digital spaces to decrease its effect on the climate. According to the authors, choosing an appropriate load balancer can significantly impact green cloud computing. Because of its adaptability and efficiency in both homogeneous and heterogeneous situations, dynamic balancing algorithms have become a preferred method of balancing systems.


Fig. 1. A Simple Load Balancer


The authors presented a literature review of current research in this field before outlining the paper's primary problem and objectives. The difficulty of scheduling and allocating resources to meet users' needs in the cloud has been referred to as an NP-hard optimization problem (meaning no known algorithm can solve it). The main objectives of this study, according to researchers, was to provide the framework for four major topics:

  • Cloud computing 
  • Algorithms used in modern-day technologies
  • Improvements after moving to green cloud computing
  • Cloud balancing, data centers, and the energy consumption of the algorithms

Load Balancing Algorithms

The adopted methodology used in the study involved a hybrid CloudSim environment for finding out the most appropriate algorithms used for green cloud computing. Using the CloudSim environment, the researchers ran various scenarios for different algorithms for load balancing in a controlled environment.


Fig. 3. 
Flow Diagram for the Proposed Method


The study found that Throughput, Makespan, and Thrashing were the performance metrics that most affected load balancing. Other factors like reliability, accuracy, and scalability also play a role in load balancing.

Out of the various classes of load balancing algorithms, the researchers selected the five most used cloud balancing algorithms: Monte Carlo Tree (MCT), Met, Minmax, MaxMin, and MinMin. The MCT algorithm is the most suitable for handling dynamic workloads—using the minimum resources, thus promoting green cloud computing. The Max-Min algorithm performed the worst, consuming the most energy for running and taking the most time to complete the task. 

The authors concluded that optimizing algorithms for load balancing and green cloud computing go hand in hand. With digitization and globalization, green cloud computing has become necessary for most organizations functioning globally. As noted in the paper, the need for computing on cloud resources is bound to increase in the future, making it necessary to adopt a green model and reduce the burden on our resources.

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