A fruitfly-based optimal resource sharing and load balancing for the better cloud services

Gerald, B. Edward; Geetha, P.; Ramaraj, E.

doi:10.1007/s00500-023-07873-y

Cited by 2 publications

(1 citation statement)

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“…For the purpose of implementing dynamic resource allocation in cloud computing, Praveenchandar and Tamilarasi [8] suggest a new load-balancing approach that they name PBMM. By putting an emphasis on the dynamic allocation and scheduling of resources, this algorithm overcomes difficulties with load balancing, which in turn promotes stability and profitability.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Meta-Heuristic Dynamic Load Balancing and Resource Allocation Approach in Cloud Computing Using Harris Hawk Optimization Algorithm (MDLB-HHO)

Dassan¹,

Thirumaaran²,

Monickaraj³

et al. 2023

Preprint

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The ultimate aim of dynamic load balancing in cloud computing systems is to maximise the efficiency with which resources are utilised and workloads are distributed. Given that load balancing is a multi-objective process and that response time is a priority, the Harris hawk optimisation (HHO) algorithm was developed as a unique solution for dynamic load balancing. Based on burden distribution and resource utilisation, the HHO algorithm is responsible for dynamically assigning workloads to virtual machines (VMs). Through iterative interactions and position updates, the hawks investigate the solution space, determine the optimal method for dividing the work, and adapt to the ever-changing conditions of the workload. The HHO algorithm has been demonstrated to be effective and efficient in the management of dynamic load balancing via a series of experimental evaluations and comparisons with other load-balancing approaches. These discoveries have led to quicker response times and more efficient resource utilisation. Utilising the collaborative search behaviour of hawks, this technique provides a solution that is both practicable and effective for addressing load balancing concerns in dynamic scenarios.

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Section: Literature Reviewmentioning

confidence: 99%