ReTra: Reinforcement based Traffic Load Balancer in Fog based Network

Divya, V.; Sri, R. Leena

doi:10.1109/icccnt45670.2019.8944487

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…However, numerical experimentations have been often used to evaluate these approaches, which do not reflect practical deployment. Divya et al [8] evaluated their Q-Learning LB solution on a testbed with high volume simulated data. They were able to balance the load in isolated Fog clusters (each cluster is assigned to a set of end devices) wasting resources, and hence energy, in other clusters.…”

Section: Related Workmentioning

confidence: 99%

“…In most cases, Fog service providers prefer not to share load and resource information from their Fog nodes; this information can be used by competing service providers to determine competing pricing strategies. However, existing solutions often require Fog resource and/or load information (e.g., [8]- [13]), which requires the agent to retrain in case of dynamic changes in the environment. In this paper, our DDQL agent does not require load and resource information to optimally distribute the load between Fog nodes.…”

Section: Introductionmentioning

confidence: 99%

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Privacy-Aware Load Balancing in Fog Networks: A Reinforcement Learning Approach

Ebrahim¹,

Hafid²

2023

Preprint

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Fog Computing has emerged as a solution to support the growing demands of real-time Internet of Things (IoT) applications, which require high availability of these distributed services. Intelligent workload distribution algorithms are needed to maximize the utilization of such Fog resources while minimizing the time required to process these workloads. These load balancing algorithms are critical in dynamic environments with heterogeneous resources and workload requirements along with unpredictable traffic demands. In this paper, load balancing is provided using a Reinforcement Learning (RL) algorithm, which optimizes the system performance by minimizing the waiting delay of IoT workloads. Unlike previous studies, the proposed solution does not require load and resource information from Fog nodes, which makes the algorithm dynamically adaptable to possible environment changes over time. This also makes the algorithm aware of the privacy requirements of Fog service providers, who might like to hide such information to prevent competing providers from calculating better pricing strategies. The proposed algorithm is interactively evaluated on a Discreteevent Simulator (DES) to mimic a practical deployment of the solution in real environments. In addition, we evaluate the algorithm's generalization ability on simulations longer than what it was trained on, which, to the best of our knowledge, has never been explored before. The results provided in this paper show how our proposed approach outperforms baseline load balancing methods under different workload generation rates.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Privacy-Aware Load Balancing in Fog Networks: A Reinforcement Learning Approach

Ebrahim¹,

Hafid²

2023

Preprint

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“…This method collects the load information for each server, handles the incoming requests, and distributes them between the servers evenly. Divya and Sri [32] proposed a reinforcement learning-based loadbalancing method by combining software-defined networks and fog computing. The proposed method understands the network behavior and balances the loads to provide the maximum possible availability of the resources.…”

Section: Related Workmentioning

confidence: 99%

A Blind Load-Balancing Algorithm (BLBA) for Distributing Tasks in Fog Nodes

Tahmasebi-Pouya

Sarram

Mostafavi

2022

Wireless Communications and Mobile Computing

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In the distributed infrastructure of fog computing, fog nodes (FNs) can process user requests locally. In order to reduce the delay and response time of a user’s requests, incoming requests must be evenly distributed among FNs. For this purpose, in this paper, we propose a blind load-balancing algorithm (BLBA) to improve the load distribution in the fog environment. In the proposed algorithm, the mobile device sends a task to a FN. Then, the FN decides to process that task using the Double- Q -learning algorithm. One of the critical advantages of BLBA is that decision-making on tasks is done without any knowledge of the state of neighbor nodes. The proposed system consists of four layers: (i) IoT layer, (ii) fog layer, (iii) proxy server layer, and (iv) cloud layer. The experimental results show that the proposed algorithm with proper distribution of tasks between nodes significantly reduces the delay and user response time compared to the existing methods.

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