Toward an autonomic approach for <scp>Internet of Things</scp> service placement using gray wolf optimization in the fog computing environment

Salimian, Mahboubeh; Ghobaei‐Arani, Mostafa; Shahidinejad, Ali

doi:10.1002/spe.2986

Cited by 21 publications

(20 citation statements)

References 45 publications

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“…Deng et al [38] have proposed different solutions for service placement, including a bat-inspired algorithm and a proximity-based approach utilizing Q-learning and ARIMA algorithms. Salimian et al [39] have examined the issue of deploying IoT applications on fog nodes using the concept of selfmanagement. Due to the heterogeneous resources and constraints of fog nodes, the GWO algorithm has been used to effectively deploy IoT applications on existing fog nodes and remove constraints.…”

Section: Centralized and Decentralized Approachesmentioning

confidence: 99%

A Cost and Energy-Aware Deployment of IoT Applications scheme in Fog Systems

Faraji-Mehmandar

Ghobaei‐Arani

Shakarami

2023

Preprint

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The number of Internet-connected devices is constantly increasing due to the growth of IoT. However, this results in a large volume of data transmission, which can cause issues with cloud-based storage and data processing due to inadequate bandwidth. This could lead to inadequacy of IoT; therefore, managing and storing data in such a way as not to cause the slightest delay in processing has become a major challenge in IoT. Both fog and cloud computing offer storage space, applications, and data for users, but fog computing is more geographically distributed and closer to the end-user, which increases system efficiency and reduces data transmission distance. Various QoS requirements of IoT services, distributed and heterogeneous nature of fog node computational capabilities make the application placement in Fog a challenging task. This paper proposes a solution that utilizes the Harris hawks optimization technique to monitor QoS requirements and available fog node capabilities to determine an efficient service placement plan. The proposed mechanism considers throughput, cost, and energy consumption as objective functions while meeting the QoS requirements of each IoT service. The simulation results obtained demonstrate that the proposed solution increases the resource usage and service acceptance ratio by 4.5% and 3.8%, respectively and reduces the service delay and the energy consumption by 2.95% and 1.62%, respectively compared with other state-of-the-art works.

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Section: Centralized and Decentralized Approachesmentioning

confidence: 99%

A Cost and Energy-Aware Deployment of IoT Applications scheme in Fog Systems

Faraji-Mehmandar

Ghobaei‐Arani

Shakarami

2023

Preprint

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“…Studies reveal that the existing RA works focused more on the development of frameworks that reduced latency and increased the quality of service [10]. The works carried out using feed-forward NN, MINLP, and other optimization techniques also obtained better outcomes [11]. But these techniques were heuristic-based and time-consuming.…”

Section: Monte Carlo (Mc) Mc-exploring Starts (Mc-es) Andmentioning

confidence: 99%

“…Fog's limited resource forms the constraint of the resource allocation problem. The objective of the FogRA system is expressed as a multi-objective optimization problem as given in the equation (11).…”

Section: A System Modelmentioning

confidence: 99%

ACT on Monte Carlo FogRA for Time-Critical Applications of IoT

Gowri¹,

Bala²,

Ramdinthara³

et al. 2022

IJACSA

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The need for instantaneous processing for Internet of Things (IoT) has led to the notion of fog computing where computation is performed at the proximity of the data source. Though fog computing reduces the latency and bandwidth bottlenecks, the scarcity of fog nodes hampers its efficiency. Also, due to the heterogeneity and stochastic behavior of IoT, traditional resource allocation technique does not suffice the timesensitiveness of the applications. Therefore, adopting Artificial Intelligence (AI) based Reinforcement Learning approach that has the ability to self-learn and adapt to the dynamic environment is sought. The purpose of the work is to propose an Auto Centric Threshold (ACT) enabled Monte Carlo FogRA system that maximizes the utilization of Fog's limited resources with minimum termination time for time-critical IoT requests. FogRA is devised as a Reinforcement Learning (RL) problem, that obtains optimal solutions through continuous interaction with the uncertain environment. Experimental results show that the optimal value achieved by the proposed system is increased by 41% more than the baseline adaptive RA model. The efficiency of FogRA is evaluated under different performance metrics.

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“…Salimian M et al [6]proposed an autonomous IoT service placement method based on gray wolf optimization scheme, which improves system performance while considering execution costs. In order to minimize the delay between the switch and the controller, Li B et al [7] proposed a controller placement strategy based on Louvain's algorithm.…”

Section: Reliable Controller Placementmentioning

confidence: 99%

Video foreground and background separation via Gaussian scale mixture and generalized nuclear norm based robust principal component analysis

Yang

2022

Preprint

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With the rapid development of information technology, the video foreground and background separation has attracted much interests in the computer version. Recently, robust principal component analysis (RPCA) has been widely used in the task of video foreground and background separation. However, the current RPCA methods have more challenge since they are difficult to find the optimal surrogate functions which can describe the sparse and low-rank components of videos very well. This paper proposes a novel RPCA method based on Gaussian scale mixture and general nuclear norm (GSMGNN) to facilitate video foreground and background separation efficiently. In this novel method, we use the Gaussian mixture scale and the nonconvex generalized nuclear norm to describe the sparse foreground and the low-rank background, respectively. Moreover, considering that noise is always existed in the practical scene, we extend the proposed GSMGNN method to the robust Gaussian scale mixture and general nuclear norm (RGSMGNN) method by inducing the noise item. And then, we adopt the efficient alternating direction method of multipliers (ADMM) method to solve these two new proposed methods. At last, we apply these two proposed methods to the video foreground and background separation experiments, and observe their significant encouraging results compared to the existing RPCA-based video foreground and background separation methods.

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Toward an autonomic approach for Internet of Things service placement using gray wolf optimization in the fog computing environment

Cited by 21 publications

References 45 publications

A Cost and Energy-Aware Deployment of IoT Applications scheme in Fog Systems

A Cost and Energy-Aware Deployment of IoT Applications scheme in Fog Systems

ACT on Monte Carlo FogRA for Time-Critical Applications of IoT

Video foreground and background separation via Gaussian scale mixture and generalized nuclear norm based robust principal component analysis

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