Analysis of the Response Time Characteristics of the Fog Computing Enabled Real-Time Mobile Applications

Sopin, Eduard; Nikita, Zolotous; Ageev, Kirill A.; Shorgin, Sergey

doi:10.1007/978-3-030-65726-0_9

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…If a task is scheduled, the total response time is the sum of the task transmission time to the fog node through the wireless network, the processing time on the fog node, and the transmission time back to the IoT devices. If the fog node is overloaded, the processing time at the fog is replaced by the transmission time between the fog node and the remote cloud, the processing time on the cloud and the transmission time back to the fog node [65].…”

Section: Response Execution and Completion Timementioning

confidence: 99%

An Analysis of Methods and Metrics for Task Scheduling in Fog Computing

Misirli,

Casalicchio

2023

Future Internet

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The Internet of Things (IoT) uptake brought a paradigm shift in application deployment. Indeed, IoT applications are not centralized in cloud data centers, but the computation and storage are moved close to the consumers, creating a computing continuum between the edge of the network and the cloud. This paradigm shift is called fog computing, a concept introduced by Cisco in 2012. Scheduling applications in this decentralized, heterogeneous, and resource-constrained environment is challenging. The task scheduling problem in fog computing has been widely explored and addressed using many approaches, from traditional operational research to heuristics and machine learning. This paper aims to analyze the literature on task scheduling in fog computing published in the last five years to classify the criteria used for decision-making and the technique used to solve the task scheduling problem. We propose a taxonomy of task scheduling algorithms, and we identify the research gaps and challenges.

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Section: Response Execution and Completion Timementioning

confidence: 99%

An Analysis of Methods and Metrics for Task Scheduling in Fog Computing

Misirli,

Casalicchio

2023

Future Internet

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“…Splitting to "heavy" and "light" tasks are done by the threshold 𝑂 𝑤 on the processing volume and the threshold 𝑂 𝑠 on the data size. Hence, the offloading probability 𝜋 𝑖,𝑂 on the 𝑖-th MD is evaluated [6], [7] according to the following formula…”

Section: Mathematical Modelmentioning

confidence: 99%

“…A task from 𝑖-th MD is processed locally with probability 1 − 𝜋 𝑖,𝑂 on the fog node with probability 𝜋 𝑖,𝑂 (1 − 𝜋 𝐹 ) and on the cloud with probability 𝜋 𝑖,𝑂 𝜋 𝐹 . In [7], we derived the Laplace-Stieltjes Transforms (LST) for all delay components for the case of Gamma distribution of both processing volume and data size, and obtain the LST of the total response time.…”

Section: The Average Response Timementioning

confidence: 99%

“…Then in the paper [6], we analyze the two-parameter offloading mechanism that takes into account both the computing complexity and the data size to be transferred in case of offloading. In [7] we derived the cumulative distribution function of the response time in terms of Laplace-Stieltjes Transform. In the current work, we solve the optimization problem by minimizing energy efficiency, subject to the average time constraint and taking into account the probability that the time exceeds a given threshold.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of mobile device energy consumption in a fog-based mobile computing offloading mechanism

Daraseliya

Sopin

2021

Discrete and Continuous Models

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The offloading of computing tasks to the fog computing system is a promising approach to reduce the response time of resource-greedy real-time mobile applications. Besides the decreasing of the response time, the offloading mechanisms may reduce the energy consumption of mobile devices. In the paper, we focused on the analysis of the energy consumption of mobile devices that use fog computing infrastructure to increase the overall system performance and to improve the battery life. We consider a three-layer computing architecture, which consists of the mobile device itself, a fog node, and a remote cloud. The tasks are processed locally or offloaded according to the threshold-based offloading criterion. We have formulated an optimization problem that minimizes the energy consumption under the constraints on the average response time and the probability that the response time is lower than a certain threshold. We also provide the numerical solution to the optimization problem and discuss the numerical results.

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DD-SPP: Dynamic and Distributed Service Placement Policy for Optimal Scheduling in Fog-Edge Computing

Kaur,

Bhardwaj

2024

SN COMPUT. SCI.

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Analysis of the Response Time Characteristics of the Fog Computing Enabled Real-Time Mobile Applications

Cited by 3 publications

References 10 publications

An Analysis of Methods and Metrics for Task Scheduling in Fog Computing

An Analysis of Methods and Metrics for Task Scheduling in Fog Computing

Optimization of mobile device energy consumption in a fog-based mobile computing offloading mechanism

DD-SPP: Dynamic and Distributed Service Placement Policy for Optimal Scheduling in Fog-Edge Computing

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