An Evolutionary Multi-objective Optimization Technique to Deploy the IoT Services in Fog-enabled Networks: An Autonomous Approach

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

doi:10.1080/08839514.2021.2008149

Cited by 43 publications

(15 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use some existing benchmark works including Hybrid‐EGAPSO, 20 SPP‐PSO, 18 and GWO‐SPP 30 for comparison work with LPB‐SPP. All these works are similar to the proposed approach and use meta‐heuristic techniques to handle SPP.…”

Section: Resultsmentioning

confidence: 99%

“…In Reference 17, the current trends presented in the fields of service placement and computation offloading on fog environments are surveyed and compared. Today, there are extensive works to address SPP, which try to solve the problem by considering different aspects 18–20 . One of the most important aspects of SPP is the type of model, placement strategy, objectives, mobility and placement policy 18 .…”

Section: Related Workmentioning

confidence: 99%

“…Here, native search returns the first solution based on heuristic techniques, while exhaustive search looks for the solution with the lowest average response time. In Reference 18, an evolutionary multi‐objective optimization technique based on PSO is proposed to solve SPP (as SPP‐PSO). In this method, IoT requests are sent to the nearest fog domain and processed by a cloud‐fog control middleware.…”

Section: Related Workmentioning

confidence: 99%

“…According to this architecture, after independent processing and execution of services on fog computing units, the output results of these services are exchanged and combined to support the IoT application in the end device. IoT services can exchange data by considering the directed acyclic graph (DAG) model 1,18 …”

Section: System Modelmentioning

confidence: 99%

“…Usually, fog nodes have two types, thin and fat, where fat devices have higher energy consumption. Energy consumption in fog domain is calculated based on working and idle energies 18 . Accordingly, Equation (16) shows the energy consumption at time

t

.…”

Section: Proposed Approachmentioning

confidence: 99%

See 4 more Smart Citations

LPB‐SPP: Solving the internet of things service placement problem using the learner performance‐based behavior algorithm

Zhou

et al. 2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Nowadays, fog computing has joined cloud computing as an emerging computing paradigm to provide resources at the network edge. Fog servers process data associated with Internet of Things (IoT) devices independently of cloud computing, thus saving bandwidth, resource reservations, and storage for real‐time applications with lower latency. Besides, cloud computing supports the integration of edge and cloud resources and facilitates the placement of IoT applications at the network edge. Recent researchers focus on how to deploy IoT services as components of IoT applications on fog computing units, where the loss of resources, energy, and bandwidth are minimized. This problem, known as the IoT service placement problem (SPP), is NP‐hard, and meta‐heuristic models are popular to address it. Each IoT service has its own requirements in terms of latency sensitivity, processor, memory, and storage. Meanwhile, fog computing units are heterogeneous and have limited resource capacities. Therefore, SPP should be addressed by considering the features of fog environment, tolerable delay, and network bandwidth. We formulate SPP as a multi‐objective optimization problem with the perspective of throughput, service cost, resource utilization, energy consumption, and service latency. To solve this problem, the learner performance‐based behavior (LPB) algorithm is presented as a meta‐heuristic model that originates from the MAPE‐K autonomous planning model. The proposed approach, LPB‐SPP, considers resource consumption distribution and service deployment prioritization, and also uses the concepts of elitism and balanced resource consumption to improve the placement process. The validation of LPB‐SPP has been done using different performance metrics and the results have been compared against state‐of‐the‐art algorithms. Simulations show that LPB‐SPP performs better in most comparisons.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

t

.…”

Section: Proposed Approachmentioning

confidence: 99%

See 3 more Smart Citations

LPB‐SPP: Solving the internet of things service placement problem using the learner performance‐based behavior algorithm

Zhou

et al. 2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

Hashemi,

Sahafi,

Rahmani

et al. 2023

Softw Pract Exp

View full text Add to dashboard Cite

The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location‐aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study.

show abstract

NSGA-II-MOGWO: A Novel Hybrid Algorithm for IoT-Fog Environment Resources Allocation

Hussein,

Hashem

2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

An Evolutionary Multi-objective Optimization Technique to Deploy the IoT Services in Fog-enabled Networks: An Autonomous Approach

Cited by 43 publications

References 41 publications

LPB‐SPP: Solving the internet of things service placement problem using the learner performance‐based behavior algorithm

LPB‐SPP: Solving the internet of things service placement problem using the learner performance‐based behavior algorithm

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

NSGA-II-MOGWO: A Novel Hybrid Algorithm for IoT-Fog Environment Resources Allocation

Contact Info

Product

Resources

About