Load Balancing Algorithms for Efficient Resource Utilization in Cloud Data Centers
DOI:
https://doi.org/10.15662/IJEETR.2023.0506002Keywords:
Load balancing, Cloud data centers, Resource utilization, Dynamic algorithms, Hybrid load balancing CloudSim simulation, Energy efficiency, Cloud orchestrationAbstract
Efficient resource utilization in cloud data centers is critical for optimizing operational costs, improving system performance, and ensuring high availability of cloud services. Load balancing algorithms play a vital role in distributing workloads evenly across available resources, preventing bottlenecks, and minimizing response time. This paper presents an in-depth study of various load balancing algorithms designed for cloud data centers, emphasizing their impact on resource utilization and system efficiency. We analyze traditional static algorithms alongside dynamic and adaptive techniques that respond to real-time changes in workload and resource availability. A comparative framework is proposed to evaluate algorithms based on metrics such as throughput, response time, resource utilization, and energy consumption. Additionally, we introduce a hybrid load balancing algorithm combining heuristic and predictive modeling to enhance decision-making under dynamic cloud conditions. Extensive simulations using CloudSim demonstrate that the proposed hybrid approach outperforms conventional methods by achieving better load distribution, reducing response times by 15%, and improving overall resource utilization by 20%. The study also discusses challenges related to scalability, heterogeneity of resources, and the overhead associated with load balancing operations. Our findings highlight the significance of integrating intelligent load balancing techniques with cloud orchestration tools to achieve optimal resource management. This research contributes to the advancement of cloud infrastructure management by providing insights and practical solutions for efficient load balancing, thereby improving the quality of service and reducing operational costs in cloud data centers.
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