An Extension to iFogSim to Enable the Design of Data Placement Strategies

Naas, Mohammed; Boukhobza, Jalil; Parvédy, Philippe Raïpin; Lemarchand, Laurent

doi:10.1109/cfec.2018.8358724

Cited by 52 publications

(28 citation statements)

References 13 publications

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“…Two application module placement strategies are packaged to support multiple deployment scenarios, namely, (a) cloud-only placement, where all applications modules run in data centres and (b) edge-ward placement, where application modules run on fog nodes close to edge devices [59]. Furthermore, extensions are available to support the design of data placement strategies according to specific objectives such as minimisation of service latency, network congestion, and energy consumption [64]. It is also worth noting that as the fog computing paradigm has many similarities to cloud computing, CloudSim can also be used as a standalone application to implement many features of fog computing.…”

Section: Fog and Edge Modelling And Simulation Toolsmentioning

confidence: 99%

Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges

et al. 2019

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The fourth industrial revolution heralds a paradigm shift in how people, processes, things, data and networks communicate and connect with each other. Conventional computing infrastructures are struggling to satisfy dramatic growth in demand from a deluge of connected heterogeneous end points located at the edge of networks while, at the same time, meeting quality of service levels. The complexity of computing at the edge makes it increasingly difficult for infrastructure providers to plan for and provision resources to meet this demand. While simulation frameworks are used extensively in the modelling of cloud computing environments in order to test and validate technical solutions, they are at a nascent stage of development and adoption for fog and edge computing. This paper provides an overview of challenges posed by fog and edge computing in relation to simulation.

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Section: Fog and Edge Modelling And Simulation Toolsmentioning

confidence: 99%

Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges

et al. 2019

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“…Myifogsim [17] extends iFogSim to support mobility by migrating virtual machines between cloudlets. In [18], Naas extends iFogSim to implement data deployment strategy in fog and IoT.…”

Section: Related Workmentioning

confidence: 99%

Support Mobile Fog Computing Test in piFogBedII

Zhang

Togookhuu

2020

Sensors

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IoT and 5G technologies are making smart devices, medical devices, cameras and various types of sensors become parts of the Internet, which provides feasibility to the realization of infrastructure and services such as smart homes, smart cities, smart medical technology and smart transportation. Fog computing (edge computing) is a new research field and can accelerate the analysis speed and decision-making for these delay-sensitive applications. It is very important to test functions and performances of various applications and services before they are deployed to the production environment, and current evaluations are more based on various simulation tools; however, the fidelity of the experimental results is a problem for most of network simulation tools. PiFogBed is a fog computing testbed built with real devices, but it does not support the testing of mobile end devices and mobile fog applications. The paper proposes the piFogBedII to support the testing of mobile fog applications by modifying some components in the piFogBed, such as extending the range of end devices, adding the mobile and migration management strategy and inserting a container agent to implement the transparent transmission between end devices and containers. The evaluation results show that it is effective and the delay resulting from the migration strategy and container agent is acceptable.

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“…Such simulation tool must provide results in terms of performance, such as job queue times and energy consumption. Several simulation tools were analysed, including: (a) edge computing simulators, such as iFogSim [56] and extensions [57,58]; and (b) cloud computing simulators, such as CloudSim [59], CloudSched [60], GreenCloud [60], and SCORE [61].…”

Section: Infrastructure-level Behaviour Of Large-scale Collaboratimentioning

confidence: 99%

Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things

Fernández-Cerero

Fernández-Rodríguez

Álvarez-García

et al. 2019

Sensors

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The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.

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An Extension to iFogSim to Enable the Design of Data Placement Strategies

Cited by 52 publications

References 13 publications

Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges

Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges

Support Mobile Fog Computing Test in piFogBedII

Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things

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