Comprehensive Analysis of Resource Allocation and Service Placement in Fog and Cloud Computing

Gowri, A. S.; Bala, P. Shanthi; Ramdinthara, Immanuel Zion

doi:10.14569/ijacsa.2021.0120308

Cited by 3 publications

(3 citation statements)

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“…Sorting them by whether the model is taught or offered is a good generalisation (Hadikhani et al, 2020). In order to decide what to do on a handful of common deep RL benchmark tasks, the model-based approach dubbed MBMF in (Gowri et al, 2021) makes use of a pure planning method known as model predictive control. According to Cappart et al, (2021), the model method is called AZ.…”

Section: Figure2 Model Of Rl Algorithmsmentioning

confidence: 99%

“…PPO focuses on learning a policy that directly maps states to actions while ensuring stability during the learning process (Gowri et al, 2021). The PPO implementation involves defining a policy network, calculating advantages and surrogate loss, and performing optimization using gradient ascent (Hadikhani et al, 2020).…”

Section: Proximal Policy Optimization Implementationmentioning

confidence: 99%

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An Empirical Study of Different Reinforcement Learning Algorithms for Resource Allocation in Cloud Computing

2024

IJFMR

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Regarding the increasing importance of cloud computing in modern IT architecture, it is crucial to create highly efficient resource allocation algorithms. This work conducts an empirical investigation into the utilisation of several reinforcement learning methods for optimising resource allocation in cloud computing environments. Our objective is to assess the efficiency of RL algorithms in a dynamic environment with fluctuating workloads, focusing on resource utilisation, cost effectiveness, and optimality. This research examines the effects of altering cloud settings in order to integrate theoretical reinforcement learning concepts into a practical resource management system. In the literature review, we consider the traditional resource allocation techniques and their inability to accommodate the changing demand. We additionally analyse the available research employing machine learning approaches, paying special attention to RL in cloud computing resource distribution. The methodology describes the research design, detailing the employed RL algorithms Q-learning, Deep Q Networks (DQN), and Proximal Policy Optimization (PPO). We explain the data collection procedure that involves different workloads and also situations to mimic real environments. Performance of each RL algorithm is presented in the experimental results based on the resource utilization, cost efficiency and also system responsiveness. Q-learning, DQN and PPO are being tested which provides a better understanding of their pros and cons. Discussion that follows the Interprets results of these findings bringing to light many challenges along the way as well as possible directions for future inquiry. Therefore, this research fills in the evolving landscape of cloud computing by demonstrating RL algorithms’ adaptability and effectiveness regarding resource allocation challenges under the dynamic environments.

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Section: Figure2 Model Of Rl Algorithmsmentioning

confidence: 99%

Section: Proximal Policy Optimization Implementationmentioning

confidence: 99%

An Empirical Study of Different Reinforcement Learning Algorithms for Resource Allocation in Cloud Computing

2024

IJFMR

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“…9 AoI estimates the time since the latest received status update at a destination was generated. 10 Optimization problems like the joint sensing time, transmission time, unmanned aerial vehicle (UAV) trajectory, and target scheduling have been investigated 11,12 to minimize the system AoI function. 8 At the lowest layer of FC infrastructure, edge devices/IoT devices may perform only the lightest tasks and count on the fog nodes at the upper layer for more complex tasks.…”

Section: Resource Management (Rm) In Fc-challengesmentioning

confidence: 99%

Toward intelligent resource management in dynamic Fog Computing‐based Internet of Things environment with Deep Reinforcement Learning: A survey

Gupta

Singh

2022

Int J Communication

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Fog computing has already started to gain a lot of momentum in the industry for its ability to turn scattered computing resources into a large-scale, virtualized, and elastic computing environment. Resource management (RM) is one of the key challenges in fog computing which is also related to the success of fog computing. Deep learning has been applied to the fog computing field for some time, and it is widely used in large-scale network RM. Reinforcement learning (RL) is a type of machine learning algorithms, and it can be used to learn and make decisions based on reward signals that are obtained from interactions with the environment. We examine current research in this area, comparing RL and deep reinforcement learning (DRL) approaches with traditional algorithmic methods such as graph theory, heuristics, and greedy for managing resources in fog computing environments (published between 2013 and 2022) illustrating how RL and DRL algorithms can be more effective than conventional techniques. Various algorithms based on DRL has been shown to be applicable to RM problem and proved that it has a lot of potential in fog computing. A new microservice model based on the DRL framework is proposed to achieve the goal of efficient fog computing RM. The positive impact of this work is that it can successfully provide a resource manager to efficiently schedule resources and maximize the overall performance. K E Y W O R D S AoI (age of information), deep reinforcement learning (DRL), fog computing (FC), Internet of Things (IoT), Markov decision process, Q-learning

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Efficient Geospatial Data Analysis Framework in Fog Environment

2022

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Comprehensive Analysis of Resource Allocation and Service Placement in Fog and Cloud Computing

Cited by 3 publications

References 0 publications

An Empirical Study of Different Reinforcement Learning Algorithms for Resource Allocation in Cloud Computing

An Empirical Study of Different Reinforcement Learning Algorithms for Resource Allocation in Cloud Computing

Toward intelligent resource management in dynamic Fog Computing‐based Internet of Things environment with Deep Reinforcement Learning: A survey

Efficient Geospatial Data Analysis Framework in Fog Environment

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