Dynamic iFogSim: A Framework for Full-Stack Simulation of Dynamic Resource Management in IoT Systems

Seo, Dongjoo; Shahhosseini, Sina; Mehrabadi, Milad Asgari; Donyanavard, Bryan; Lim, Sung-Soo; Rahmani, Amir M.; Dutt, Nikil

doi:10.1109/coins49042.2020.9191663

Cited by 7 publications

(7 citation statements)

References 21 publications

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“…Fog computing was implemented as a supplement to the cloud infrastructure since IoT system requirements would surpass the cloud resources shortly. Dongjoo Seo [24] it was suggested that Dynamic Internet of Things ( IoT) applications like Healthcare IoT contain a variety of kernel intensities of computation, data, and communication and have various QoS specification intolerances, like latency, throughput, availability, accuracy, respectively. A detailed instrument to conduct an effective fullstack evaluation is needed to ensure QoS specifications for the apps.…”

Section: Features Comparison Of CC and Mccmentioning

confidence: 99%

“…It operates based on a stream processing model, in which sensors continuously generate data that fog devices analyze, enabling people to quickly assess the results of various placement algorithms. Dongjoo Seo [24] suggested that iFogSim is one of CloudSim-based most common simulators. iFogSim can model IoT and fog conditions to measure latency, network congestion, and system energy consumption.…”

Section: A Common Cloud Simulatorsmentioning

confidence: 99%

“…The virtual computer's hardware, network layer, and simulation system specifications are all part of the machine settings. Furthermore, the target programs that should be run on [24] virtual [63] machines are program codes [24]. The Sniper simulator first runs the loaded app codes on the required device hardware configuration to generate statistics for subsequent moves.…”

Section: A Cloudsim Architecturementioning

confidence: 99%

“…In other words, the controller can influence QoS by changing the hardware or platform specifications. For the system configuration specified [24], the Framework records the application's execution time and energy consumption. E. CloudReports Architecture It is an expandable simulation software [58] for energyaware [32] cloud computing [56], and [57] environments that allow researchers to design a variety of complex concepts through an interface.…”

Section: A Cloudsim Architecturementioning

confidence: 99%

“…The simulation starts with settings for the loading system and application codes in the Module Cosimulation Environment.The virtual computer's hardware, network layer, and simulation system specifications are all part of the machine settings. Furthermore, the target programs that should be run on[24] virtual[63] machines are program codes[24]. The Sniper simulator first runs the loaded app codes on the required device hardware configuration to generate statistics for subsequent moves.The iFogSim then uses the stats to simulate the system and network layer according to the device specifications.…”

mentioning

confidence: 99%

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A Systematic Survey of Simulation Tools for Cloud and Mobile Cloud Computing Paradigm

Shahid¹,

Faiz²,

Alam³

et al. 2022

JISR-C

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Cloud computing (CC) provides fast and on-demand access to virtually unlimited computing resources. Mobile cloud computing (MCC) is a new technology that brings CC and mobile devices together. MCC allows mobile devices access to cloud services. Describe the basic architectures for various MCC applications, as well as a brief comparison of cloud and MCC. As CC becomes more common, researchers in this field may need to conduct real-world experiments in their studies. Configuring and running these experiments in real-world cloud environments is costly. As a consequence, modeling and simulation approaches are suitable solutions that can be used to simulate cloud computing environments. Several simulation tools tailored to CC have been developed. The most powerful simulation methods in this field of study are described in this article. Among them are CloudSim, CloudSim Plus, CloudAnalyst, iFogSim, and CloudReports.

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Section: Features Comparison Of CC and Mccmentioning

confidence: 99%

Section: A Common Cloud Simulatorsmentioning

confidence: 99%

Section: A Cloudsim Architecturementioning

confidence: 99%

Section: A Cloudsim Architecturementioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

A Systematic Survey of Simulation Tools for Cloud and Mobile Cloud Computing Paradigm

Shahid¹,

Faiz²,

Alam³

et al. 2022

JISR-C

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A systematic review of task scheduling approaches in fog computing

Bansal

Aggarwal

2022

Trans Emerging Tel Tech

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Due to increased use of IoT devices and data sensors a huge amount of data is being produced for processing in real-time. Fog computing has evolved as a solution for fast processing of data. To complete the data processing as per requirement of user, it is processed at fog nodes which are near the user. To complete work in a specified time with limited resources, task scheduling is performed. With the increased amount of data to be processed, the completion of task within given time in fog computing is a major challenge. So, scheduling of tasks and resources is a very important issue. A lot of research has been undertaken in recent years. In this survey, authors have reviewed various task scheduling algorithms suggested by researchers to meet the user requirements.The focus of this article is on diverse scheduling techniques being deployed in fog computing. An effort has been made to classify existing approaches, research issues and determine significant issues existing in this field. There are four major scheduling categories that are used by researchers namely, static, dynamic, heuristic, and hybrid. As per this study, 17% researchers deployed static, 23% dynamic, 47% heuristic, and 13% hybrid approaches, respectively.Analysis shows that for QoS (Quality of Service) parameters 19% researchers are focused on response time, 18% on cost and energy consumption, and 16% on makespan. For QoS parameter other factors have much smaller contribution in comparison to the above factors. In the tools used, researchers observed that 40% of the researches have used iFogSim, according to literature. Besides, this research article also discusses open issues and future work in the field of fog computing. This article also underlines various open issues and future directions in the field of fog computing.

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