An improved multi-objective genetic algorithm with heuristic initialization for service placement and load distribution in edge computing

Maia, Adyson M.; Ghamri‐Doudane, Yacine; Vieira, Dário; Castro, Miguel

doi:10.1016/j.comnet.2021.108146

Cited by 49 publications

(16 citation statements)

References 39 publications

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“…If the population size is too large, the network will converge slowly, and the training time will be too long. Therefore, the population size needs to be determined according to actual problems and reference experience settings, and the population can also be generated by random generation [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Ideological and Political Education under Deep Learning Neural Network

Wang

2022

Computational Intelligence and Neuroscience

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Under the background of the rapid development of the new generation of information technology, artificial neural networks (ANNs) have made some progress in the evaluation research of various courses in colleges and universities. However, there is little research on the application of ANNs in Ideological and Political Education (IPE) courses. Based on this, this work attempts to introduce the Backpropagation Neural Network (BPNN) into the evaluation system of IPE courses. Firstly, the structure and characteristics of the BPNN are given, and it is optimized by the genetic algorithm based on its characteristics. Secondly, the theoretical framework of IPE course evaluation is established, and the corresponding evaluation model is constructed using the optimized BPNN. Finally, a questionnaire survey is designed to analyze the current situation of IPE evaluation in colleges and universities, and a simulation experiment is set up to test the BPNN evaluation model before and after optimization. The results are as follows. First, there are mainly five evaluation methods for IPE courses in Chinese colleges and universities: 6/4, 3/7, 2/2/6, 5/5, and 3/3/4. Second, the training error value of the BPNN model is in the interval (−2.3, 2.2), and when the number of cycles is 552, the error is infinitely close to zero. Thirdly, the training error value of the optimized BPNN model is in the interval (−0.22, 1.2), and when the number of cycles is 775, the error is infinitely close to zero. Fourthly, the error between the output value of the optimized BPNN model and the expert score value is generally smaller than the error value before optimization. This work aims to provide a theoretical reference for the further application of neural network technology in the evaluation of IPE.

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Section: Methodsmentioning

confidence: 99%

Evaluation of Ideological and Political Education under Deep Learning Neural Network

Wang

2022

Computational Intelligence and Neuroscience

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“…-The Minimum Delay Provider First (MD1): This algorithm selects the provider which offers the minimum delay for each service placement request. -Genetic Algorithm (GA): GA is one of the most used meta-heuristic techniques for the SPP in Fog and Cloud environments, e.g., [39,28,32,43]. Here, we have implemented our custom GA according to the OF provided in eq.…”

Section: Baseline Algorithmsmentioning

confidence: 99%

A Scalable and Flexible Platform for Service Placement inMulti-Fog and Multi-Cloud Environments

Azizi

Farzin

Shojafar

et al. 2023

Preprint

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Provisioning services for Internet of Things (IoT) devices leads to severalchallenges: heterogeneity of IoT devices, varying Quality of Services (QoS)requirements, and increasing availability of both Cloud and Fog resources. Thelast of these is most significant to cope with the limitations of Cloud infrastructureproviders (CIPs) for latency-sensitive services. Many Fog infrastructure providers(FIPs) have recently emerged and their number is increasing continually. FLEX isproposed in this work as a platform for selecting a location for service placementin a multi-Fog and multi-Cloud environment. For each service, FLEX broadcastsservice requirements to the resource managers (RMs) of the available Fog andCloud service providers and then selects the most suitable provider for that service.FLEX is scalable and flexible as it leaves it up to the RMs to have their ownpolicy for the placement of submitted services. Service placement and resource selectionhas been formulated as an optimization problem and an efficient heuristicalgorithm is proposed to solve it. Results show that the proposed algorithm canbe used across both Cloud and Fog-based providers.

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“…In Reference 33, a fog orchestrator node selection model to improve application placement in fog computing is proposed. Maia et al 34 proposed an improved multiobjective genetic algorithm with heuristic initialization for service placement. Faticanti et al 35 proposed a joint partitioning and optimization framework for throughput‐intensive applications.…”

Section: Related Workmentioning

confidence: 99%

Deploying IoT services on the fog infrastructure: A graph partitioning‐based approach

2022

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Internet of Things (IoT) represents a new generation of information and communication technology for anyone, anytime and anywhere. Cloud service‐based IoT applications significantly increase latency and network utilization. The fog environment is closer to the user to perform computing, communication, and storage tasks on network edge devices. Therefore, it can greatly reduce the latency of real‐time applications. It is an essential feature of fog computing and its most important advantage compared to cloud computing. This study proposed a new approach to service placement generated by running applications on IoT devices in the fog computing. IoT devices send applications to the fog environment that each application contains a set of services. The purpose of solving the IoT services placement problem is to efficiently deploy these services on fog cells. For this purpose, it is assumed that the received services from the IoT applications are received as a directed acyclic graph that depicts the communication between the cells within the graph that shows the communication between the services. Then, the imperialist competitive algorithm is used to place and select the destination for IoT services. The simulation results of the iFogSim simulator in different experiments showed that the imperialist competitive algorithm with the proposed graph partitioning approach has improved service placement on the fog infrastructure compared to the genetic algorithm and best‐fit algorithm.

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An improved multi-objective genetic algorithm with heuristic initialization for service placement and load distribution in edge computing

Cited by 49 publications

References 39 publications

Evaluation of Ideological and Political Education under Deep Learning Neural Network

Evaluation of Ideological and Political Education under Deep Learning Neural Network

A Scalable and Flexible Platform for Service Placement inMulti-Fog and Multi-Cloud Environments

Deploying IoT services on the fog infrastructure: A graph partitioning‐based approach

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