Load Balancing Techniques in Cloud Computing A Comparative Study
1st Perugu Radha
Dept. of Computer Applications Aditya University Surampalem, India
peruguradha46@gmail.com
2nd Vanamatla Padma Pavani Dept. of Computer Applications Aditya University
Surampalem, India pavanivanamatla12053@gmail.com
3rd Yanamala Thanusha Reddy Dept. of Computer Applications Aditya University
Surampalem, India thanushareddyyamala@gmail.com
4th Nallamilli Harshitha Dept. of Computer Applications Aditya University
Surampalem, India hbhargavin7@gmail.com
Abstract—Cloud computing has become a key paradigm of providing the on-demand and scalable computing resources over the internet. Efficient load balancing is one of the key issues in cloud environments as it guarantees the optimal usage of resources, minimizes response time, and enhances the reliability of the system. This paper outlines a comparison of the key load balancing techniques utilized in cloud computing, namely, static, dynamic, and hybrid load balancing techniques. Round Robin, and the Weighted Round Robin are some of the Static algorithms that allocate workloads by fixed rules, being simple but not flexible to changes in real time. Conversely, dynamic algorithms such as Least Connection, Throttled Load Balancing and Ant Colony Optimization modify the allocation of resources according to the prevailing conditions in the systems, resulting in better outcomes in a heterogeneous setting. Moreover, the latest development of combining Artificial Intelligence and Machine Learning, including reinforcement learning-based load balancing, have proven to be much better in forecasting workload patterns and resource allocation patterns.
The research considers such methods through major perfor- mance indicators such as response time, throughput, scalability, fault tolerance, according to the experience of actual cloud platforms like Amazon Web Services (AWS) and Microsoft Azure. The findings by the literature show that dynamic and AI- based solutions are superior to traditional solutions to manage variable workloads and massively distributed systems. They do introduce more complex computations and overheads, however. The paper brings out the trade-offs of efficiency, complexity, and scalability with a broader comprehension of the applicability of each technique in various cloud situations.
The results indicate that the future of cloud infrastructures will have a bright future with the use of hybrid and intelligent load balancing strategies, which can provide a flexible, effective, and efficient way of service provision in more and more complex and data-driven environments.
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