Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment

Sulimani, Hamza; Sajjad, Akbar Muhammad; Alghamdi, Wael Y.; Kaiwartya, Omprakash; Jan, Tony; Simoff, Simeon; Prasad, Mukesh

doi:10.1002/ett.4650

Cited by 8 publications

(1 citation statement)

References 60 publications

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“…Figure 1. Fog computing architecture (Sulimani, Sajjad et al 2022) All user-sent applications adhere to a standard operational algorithm, as outlined by Mukherjee, Shu et al (2018):…”

Section: Fog Computingmentioning

confidence: 99%

HybOff: A Hybrid Offloading Approach to Improve Load Balancing in Fog Networks

Sulimani,

Ramezani

et al. 2023

Preprint

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Load balancing is crucial in distributed systems like fog computing, where efficiency is paramount. Offloading with different approaches is the key to balancing the load in distributed environments. Static offloading (SOS) falls short in heterogeneous networks, necessitating dynamic offloading to reduce latency in time-sensitive tasks. However, prevalent dynamic offloading (DOS) solutions often come with hidden costs that impact sensitive applications, including decision time, networks congested and distance offloading. This paper introduces the Hybrid Offloading (HybOff) algorithm, which substantially enhances load balancing and resource utilization in fog networks, addressing issues in both static and dynamic approaches while leveraging clustering theory. Its goal is to create a uncomplicated low-cost offloading approach that enhances IoT application performance by eliminating the consequences of hidden costs regardless of network size. Experimental results using the iFogSim simulation tool show that HybOff significantly reduces offloading messages, distance, and decision-offloading consequences. It improves load balancing by 97%, surpassing SOS (64%) and DOS (88%). Additionally, it increases system utilization by an average of 50% and enhances system performance 1.6 times and 1.4 times more than SOS and DOS, respectively. In summary, HybOff substantially contributes to load balancing and offloading research in fog computing.

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“…Figure 1. Fog computing architecture (Sulimani, Sajjad et al 2022) All user-sent applications adhere to a standard operational algorithm, as outlined by Mukherjee, Shu et al (2018):…”

Section: Fog Computingmentioning

confidence: 99%

HybOff: A Hybrid Offloading Approach to Improve Load Balancing in Fog Networks

Sulimani,

Ramezani

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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Improved IoT Application Placement in Fog Computing Through Postponement

Aljohani,

Sakellariou

2024

Lecture Notes in Computer Science

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Machine learning methods for service placement: a systematic review

Keshavarz Haddadha,

Rezvani,

MollaMotalebi

et al. 2024

Artif Intell Rev

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With the growth of real-time and latency-sensitive applications in the Internet of Everything (IoE), service placement cannot rely on cloud computing alone. In response to this need, several computing paradigms, such as Mobile Edge Computing (MEC), Ultra-dense Edge Computing (UDEC), and Fog Computing (FC), have emerged. These paradigms aim to bring computing resources closer to the end user, reducing delay and wasted backhaul bandwidth. One of the major challenges of these new paradigms is the limitation of edge resources and the dependencies between different service parts. Some solutions, such as microservice architecture, allow different parts of an application to be processed simultaneously. However, due to the ever-increasing number of devices and incoming tasks, the problem of service placement cannot be solved today by relying on rule-based deterministic solutions. In such a dynamic and complex environment, many factors can influence the solution. Optimization and Machine Learning (ML) are two well-known tools that have been used most for service placement. Both methods typically use a cost function. Optimization is usually a way to define the difference between the predicted and actual value, while ML aims to minimize the cost function. In simpler terms, ML aims to minimize the gap between prediction and reality based on historical data. Instead of relying on explicit rules, ML uses prediction based on historical data. Due to the NP-hard nature of the service placement problem, classical optimization methods are not sufficient. Instead, metaheuristic and heuristic methods are widely used. In addition, the ever-changing big data in IoE environments requires the use of specific ML methods. In this systematic review, we present a taxonomy of ML methods for the service placement problem. Our findings show that 96% of applications use a distributed microservice architecture. Also, 51% of the studies are based on on-demand resource estimation methods and 81% are multi-objective. This article also outlines open questions and future research trends. Our literature review shows that one of the most important trends in ML is reinforcement learning, with a 56% share of research.

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Reinforcement optimization for decentralized service placement policy in IoT‐centric fog environment

Cited by 8 publications

References 60 publications

HybOff: A Hybrid Offloading Approach to Improve Load Balancing in Fog Networks

HybOff: A Hybrid Offloading Approach to Improve Load Balancing in Fog Networks

Improved IoT Application Placement in Fog Computing Through Postponement

Machine learning methods for service placement: a systematic review

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