Improved Load Balancing and Partitioning Model for Cloud Networks

Load balancing techniques are vital for the efficient allocation of workloads across nodes in cloud infrastructures, playing an indispensable role in optimizing overall performance. By ensuring that load factors are managed effectively, these techniques significantly enhance system efficiency. This is particularly relevant in data centers, which serve as fundamental elements within cloud computing frameworks. Effective utilization of data centers can lead to marked improvements in system performance. A key aspect of successful load balancing is the precise computation of workloads among nodes. To address this, numerous load balancing models have been developed, particularly tailored for public cloud environments. These models often employ partitioning strategies to optimize performance. This paper delves into existing partitioning concepts and load balancing models within the realm of public clouds. Furthermore, it introduces a comprehensive generalized model designed to adeptly handle a variety of network load scenarios, thereby advancing the field of load balancing in cloud computing.