Mobility-Aware Computation Offloading in Edge Computing Using Machine Learning

Maleki, Erfan Farhangi; Mashayekhy, Lena; Nabavinejad, Seyed Morteza

doi:10.1109/tmc.2021.3085527

Cited by 37 publications

(17 citation statements)

References 35 publications

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“…The parallel execution of these applications can be obtained using multi processing approaches [79]. In the context of Fog computing, several works such as [13,20,84,89] have considered monolithic applications.…”

Section: Architectural Designmentioning

confidence: 99%

“…In this resource organization, IoT devices are situated at the bottom-most layer and resource providers are placed at the edge of the network in the proximity of IoT devices (i.e., Edge computing). Several works use two-tier resource organization such as [55,69,84,125].…”

Section: Tiering Modelmentioning

confidence: 99%

“…The type of IoT devices help us understand the amount of resources, capabilities, and constraints of these devices. The IoT devices used in the current literature can be broadly classified into three categories, namely 1) mobile devices (MD) which are mostly considered as smartphones or tablets [25,102,119], 2) Vehicles [137,140,148], and 3) General devices containing a set of IoT devices, ranging from small sensors to drones [39,84,120].…”

Section: Typementioning

confidence: 99%

“…It refers to problems with integer and non-integer variables and non-linear functions in the objective function and/or the constraints. Several works such as [22,84,147,156] have used MINLP to present their optimization problems.…”

Section: Mixed Integer Non-linear Programming (Minlp)mentioning

confidence: 99%

“…1. Direct Optimization: In this category, the optimization problem will be solved using classical optimization tools either using 1) Exact approach to find the optimal solution such as [18,25] or 2) Approximation approach to find a near optimal solution such as [11,84,136].…”

Section: Placement Techniquementioning

confidence: 99%

See 4 more Smart Citations

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Goudarzi¹,

Palaniswami²,

Buyya³

2022

Preprint

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Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the network with low latency and high-access bandwidth to support a diverse range of IoT application scenarios. To fully utilize the potential of this computing paradigm, scalable, adaptive, and accurate scheduling mechanisms and algorithms are required to efficiently capture the dynamics and requirements of users, IoT applications, environmental properties, and optimization targets. This paper presents a taxonomy of recent literature on scheduling IoT applications in Fog computing. Based on our new classification schemes, current works in the literature are analyzed, research gaps of each category are identified, and respective future directions are described. CCS Concepts: • General and reference → General literature; • Computer systems organization → Distributed architectures.

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Section: Architectural Designmentioning

confidence: 99%

Section: Tiering Modelmentioning

confidence: 99%

Section: Typementioning

confidence: 99%

Section: Mixed Integer Non-linear Programming (Minlp)mentioning

confidence: 99%

Section: Placement Techniquementioning

confidence: 99%

See 3 more Smart Citations

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

Goudarzi¹,

Palaniswami²,

Buyya³

2022

Preprint

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The offloading algorithm of mobile edge computing considering mobility in the intelligent inspection scenario

Xie

Sun

et al. 2022

Trans Emerging Tel Tech

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In industrial scenarios, it is very common to replace manual detection with new intelligent applications such as image recognition based on deep learning. However, these applications often have the characteristics of high computational energy consumption and high computational density. It is impossible to deal with these tasks only by relying on intelligent detection equipment. Mobile edge computing (MEC) is an effective method to solve the problems of high energy consumption and insufficient computing power of detection equipment.A reasonable offloading strategy can reduce equipment energy consumption and reduce system maintenance costs. This article studies the intelligent inspection scenarios of multi-user and multi-MEC servers, and considers the resource competition and mobility among users. We divided the entire inspection process into several computing cycles, and designed a mobile migration model based on the traditional computing offloading model. Finally, a distributed algorithm based on game theory is proposed to solve the problem of minimizing energy consumption. The proposed intelligent inspection and offloading migration algorithm is evaluated through simulation experiments. Theoretical and experimental results show that our method can better adapt to the scenario proposed in this article, with low algorithm complexity and can effectively reduce the energy consumption of the whole inspection system. Compared with random and greedy algorithms, the energy consumption is reduced by 60% and 50% respectively.

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