Time and position aware resource search algorithm for the mobile peer-to-peer network using ant colony optimisation

Kumar, Dharmendra; Dubey, Ajay Kumar; Pandey, Mayank

doi:10.1504/ijcnds.2022.126225

Cited by 2 publications

(2 citation statements)

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“…[20] proposed an energy-efficient load-balancing technique that considers energy consumption and workload balancing. The authors used a threshold-based approach to allocate workloads to computing resources based on their power consumption and utilization levels [21].…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach to Green Cloud Computing: QoS-Optimized Load Balancing with Artificial Bee Colony Algorithm

Kumar¹

2023

Preprint

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This paper uses an artificial bee colony algorithm to present a QoS-oriented technique for load balancing in green cloud computing. The proposed technique aims to optimize workload allocation decisions while minimizing energy consumption and carbon emissions and ensuring QoS requirements are met. The technique involves a fitness function that evaluates the fitness of each solution based on a set of QoS and energy consumption metrics. It also involves the ABC algorithm, which generates novel solutions and explores the search space. The algorithm also applies a dynamic threshold-based approach to adjust the threshold values based on the current system load and QoS requirements. Experimental results demonstrate that the proposed technique outperforms other load-balancing techniques regarding QoS and energy consumption metrics. Compared to other load-balancing techniques, the new technique significantly improved response time, throughput, availability, resource utilization, energy consumption, and carbon emissions. The proposed technique is likely to improve energy efficiency, reduce cloud computing's carbon footprint, and meet quality of service requirements.

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Section: Related Workmentioning

confidence: 99%

A Novel Approach to Green Cloud Computing: QoS-Optimized Load Balancing with Artificial Bee Colony Algorithm

Kumar¹

2023

Preprint

Self Cite

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“…The location of the next crow modifies based on where the owner crow is and how long the battle has been going on. Flight length demonstrates how far the master crow can trail the follower crow while still grabbing food [7]. A lower awareness probability value encourages a search in a smaller area, while a higher awareness probability value, which ranges from 0 to 1, encourages a search in a larger area.…”

Section: Introductionmentioning

confidence: 99%

A smart, real-time, secure strategy for allocating and scheduling resources based on fog computing and the multi-objective crow search algorithm

Kumar¹

2023

Preprint

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Fog computing is a new way of using computers that builds on standard cloud technology by taking advantage of the resources at the user's location to improve services. Because of its advantages, which include cheaper operational costs, better security, and lower network latency, it is the most often used option for many real-time applications. However, since each fog device is different, it is hard to figure out how to divide up and schedule resources. This paper proposes a novel approach for resource allocation and scheduling in a fog computing environment using a crow search mechanism and a multi-objective population-based evolutionary mechanism. The proposed work considers two goals: the security hit rate and the success rate. These two aims must be met to the greatest extent possible. A local search method improves how well the crow search algorithm (CSA) works. The suggested work uses the evolutionary method to figure out how to schedule and divide resources in a fog computing environment. We have contrasted the effectiveness of our suggested hybrid CSA with two other approaches, the original CSA and the Genetic Algorithm (GA), based on two metrics: success rate and security hit ratio. We have used seven different scenarios with different values for the parameters. We found that our proposed hybrid CSA outperformed other currently employed methods. The study's outcome also shows how well the suggested algorithm accomplishes its objectives.

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Time and position aware resource search algorithm for the mobile peer-to-peer network using ant colony optimisation

Cited by 2 publications

References 0 publications

A Novel Approach to Green Cloud Computing: QoS-Optimized Load Balancing with Artificial Bee Colony Algorithm

A Novel Approach to Green Cloud Computing: QoS-Optimized Load Balancing with Artificial Bee Colony Algorithm

A smart, real-time, secure strategy for allocating and scheduling resources based on fog computing and the multi-objective crow search algorithm

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