To optimize the multi accesses download time using scheduling approach in fog computing

Keerthika, M.; Ram, N. Sankar; Rajkumar, M.; Manivannan, Madhavan; Monish, S.

doi:10.1016/j.matpr.2020.07.104

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…Self-Similarity-based clustering is implemented in [24] but fog computing contains heterogeneous systems similarity-based grouping is not suitable. K-means++ clustering-based grouping is used in [25] but the optimization of the algorithm is not verified. The optimized K-means clustering is implemented to make an effective clustering.…”

Section: Optimized K-means Elbow Clusteringmentioning

confidence: 99%

Classification of Request-Based Mobility Load Balancing in Fog Computing

Deepa¹,

Jothi²

2023

Computer Systems Science and Engineering

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Every day, more and more data is being produced by the Internet of Things (IoT) applications. IoT data differ in amount, diversity, veracity, and velocity. Because of latency, various types of data handling in cloud computing are not suitable for many time-sensitive applications. When users move from one site to another, mobility also adds to the latency. By placing computing close to IoT devices with mobility support, fog computing addresses these problems. An efficient Load Balancing Algorithm (LBA) improves user experience and Quality of Service (QoS). Classification of Request (CoR) based Resource Adaptive LBA is suggested in this research. This technique clusters fog nodes using an efficient K-means clustering algorithm and then uses a Decision Tree approach to categorize the request. The decision-making process for time-sensitive and delay-tolerable requests is facilitated by the classification of requests. LBA does the operation based on these classifications. The MobFogSim simulation program is utilized to assess how well the algorithm with mobility features performs. The outcome demonstrates that the LBA algorithm's performance enhances the total system performance, which was attained by (90.8%). Using LBA, several metrics may be examined, including Response Time (RT), delay (d), Energy Consumption (EC), and latency. Through the on-demand provisioning of necessary resources to IoT users, our suggested LBA assures effective resource usage.

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Section: Optimized K-means Elbow Clusteringmentioning

confidence: 99%

Classification of Request-Based Mobility Load Balancing in Fog Computing

Deepa¹,

Jothi²

2023

Computer Systems Science and Engineering

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“…To achieve this goal, a heuristic algorithm was created, which lowered not only the cost of energy but also the time it took to complete the task. [20] suggested a fog scheduling strategy that took into account multiple access download times, lowering the average response time. [21] proposed an integrated iterative optimization on subcarrier that took into account power and trajectory to arrive at the best option.…”

Section: Work That Are Relatedmentioning

confidence: 99%

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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To optimize the multi accesses download time using scheduling approach in fog computing

Cited by 2 publications

References 33 publications

Classification of Request-Based Mobility Load Balancing in Fog Computing

Classification of Request-Based Mobility Load Balancing in Fog Computing

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

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