Load balancing between fog and cloud in fog of things based platforms through software-defined networking

Batista, Ernando; Figueiredo, Gustavo B.; Prazeres, Cássio

doi:10.1016/j.jksuci.2021.10.003

Cited by 22 publications

(35 citation statements)

References 23 publications

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“…is section presents the simulation environment settings of the proposed method and evaluates the efficiency of the proposed optimal load-balanced-based resource allocation method in comparison with other benchmark methods, Dynamic Energy Efficient Resource Allocation (DEERA) [1] and load balancing for FoT-Gateways [6] over different performance metrics using Personal Cloud Dataset obtained Input: Dataset "DS," task "T � T 1 , T 2 , . .…”

Section: Experimental Evaluation and Resultsmentioning

confidence: 99%

“…e results for performance metrics of load balancing efficiency are provided in Table 3. e simulation results demonstrate that DGW-SBDRmethod improves the load balancing efficiency as compared to DEER [1] and load balancing for FoT-Gateways [6].…”

Section: Performance Analysis Of Load Balancing Efficiency Andmentioning

confidence: 99%

“…It displays how different tasks utilize fog resources. DGW-SBDR shows greater load balancing efficiency in the highlighted environment than [1,6]. e improvement was attributed to using Differential Evolution-based Grey Wolf Optimization to balance host use amongst fog nodes.…”

Section: Performance Analysis Of Load Balancing Efficiency Andmentioning

confidence: 99%

“…By employing the Differential Evolution function, this approach determines the genuine global solution (optimal host utilization assuring load balancing) regardless of cloud user request tasks, with quick convergence between under and overutilization. DGW-SBDR enhanced load balancing efficiency by 2% compared to [1] and 6% compared to [6].…”

Section: Performance Analysis Of Load Balancing Efficiency Andmentioning

confidence: 99%

“…However, due to processing congestion, an overload on a network link connected to an IoT gateway may result in inaccessibility in IoT applications. To solve these issues, [6] provided a solution for the requirements to implement load balancing for the Fog of ings via programmability via the use of Software Defined Networks (SDN).…”

Section: Introductionmentioning

confidence: 99%

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Differential Grey Wolf Load-Balanced Stochastic Bellman Deep Reinforced Resource Allocation in Fog Environment

Nethaji

Chidambaram

2022

Applied Computational Intelligence and Soft Computing

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Fog computing is becoming a dynamic and sought-after computing prototype for Internet of Things (IoT) application deployments. It works in conjunction with the cloud computing environment. Load balancing, which is employed by IoT applications when deciding, which fog or cloud computing nodes to use, is one of the most critical components for enhancing resource efficiency and avoiding problems like overloading or underloading. However, for IoT applications, ensuring that all CPU nodes are evenly distributed in terms of latency and energy utilization remains a challenge. To solve these issues, this work introduces Differential Grey Wolf (DGW) load balancing with stochastic Bellman deep reinforced resource optimization (DGW-SBDR) in fog situations. A Differential Evolution-based Grey Wolf Optimization algorithm based on load balancing has been developed for optimal resource management. The Grey Wolf Optimization algorithm, which employs differential evolution, assigns jobs to virtual machines based on user demands (VMs). In the event of an overutilized VM pool, a grey wolf optimization strategy based on differential evolution can detect both under and overutilized VMs, allowing for smooth transit between fogs. This step disables a number of virtual machines in order to reduce latency. In a Stochastic Gradient and Deep Reinforcement Learning-based Resource Allocation Model, a stochastic gradient bellman optimality function and Deep Reinforcement Learning are integrated for optimal resource allocation. According to the proposed method, QoS may be supplied to end-users by reducing energy consumption and better managing cache resources utilizing stochastic gradient bellman optimality.

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Section: Experimental Evaluation and Resultsmentioning

confidence: 99%

Section: Performance Analysis Of Load Balancing Efficiency Andmentioning

confidence: 99%

Section: Performance Analysis Of Load Balancing Efficiency Andmentioning

confidence: 99%

Section: Performance Analysis Of Load Balancing Efficiency Andmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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