piFogBed: A Fog Computing Testbed Based on Raspberry Pi

Xu, Qiaozhi; Zhang, Junxing

doi:10.1109/ipccc47392.2019.8958741

Cited by 16 publications

(12 citation statements)

References 18 publications

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“…We have verified the availability and fidelity of piFogBed in [14]. We will evaluate the performances of the migration strategy and container agent in piFogBedII.…”

Section: Discussionmentioning

confidence: 95%

“…A more realistic testing environment can help users find the bottle neck, defect or limitation of their applications. We have designed piFogBed [14] based on raspberry pies, which is a real fog computing testbed that can provide a real fog computing architecture, simulate various network scenarios for users and support users' testing of real applications. The experimental results obtained on piFogBed have better fidelity than simulators, but, the flexibility and scalability of piFogBed were poor in the following aspects:…”

Section: Introductionmentioning

confidence: 99%

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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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“…We have verified the availability and fidelity of piFogBed in [14]. We will evaluate the performances of the migration strategy and container agent in piFogBedII.…”

Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Support Mobile Fog Computing Test in piFogBedII

Zhang

Togookhuu

2020

Sensors

Self Cite

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“…Buyya et al [37] elaborated the concept of iFogSim and its usage in various applications. piFogBed [38] is the first fog computing testbed which is based on Raspberrypi and can easily be configured to use with a realistic fog network. However, for the evaluation, the authors simulated a medical monitoring system.…”

Section: B Fog Simulatorsmentioning

confidence: 99%

Multi-Level Resource Sharing Framework Using Collaborative Fog Environment for Smart Cities

et al. 2021

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Fog computing has proved its importance over legacy cloud architectures for computation, storage, and communication where edge devices are used to facilitate the delay-sensitive applications. The inception of fog nodes has brought computing intelligence close to the end-devices. Many fog computing frameworks have been proposed where edge devices are used for computation. In this paper, we proposed a simulation framework for fog devices that can use end devices to handle the peak computation load to provide better Quality of Services (QoS). The regional fog nodes are deployed at network edge locations which are used as an intelligent agent to handle the computation requests by either scheduling them on local servers, cloud data centers, or at the under-utilized end-user devices. The proposed device-to-device resource sharing model relies on Ant Colony Optimization (ACO) and Earliest Deadline First(EDF) Algorithm to provide a better quality of service using device available at multi-layer design. The concept of using IoT devices as fog nodes has improved the performance of legacy fog based systems. The proposed work is benchmarked in terms of system cost, efficiency, energy, and quality of service. Further, the proposed framework is with xFogSim in terms of task efficiency.

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“…Another Raspberry Pi-based testbed called piFogBed is presented by Xu & Zhang (2019) . Their system has a coordinator deployed on a standard computer that contains user management functionality, a device allocator for service placement of user applications, a container manager to save service images to DockerHub, a network simulator and an application execution controller.…”

Section: Related Workmentioning

confidence: 99%

FogFrame: a framework for IoT application execution in the fog

Skarlat¹,

Schulte²

2021

PeerJ Computer Science

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Recently, a multitude of conceptual architectures and theoretical foundations for fog computing have been proposed. Despite this, there is still a lack of concrete frameworks to setup real-world fog landscapes. In this work, we design and implement the fog computing framework FogFrame—a system able to manage and monitor edge and cloud resources in fog landscapes and to execute Internet of Things (IoT) applications. FogFrame provides communication and interaction as well as application management within a fog landscape, namely, decentralized service placement, deployment and execution. For service placement, we formalize a system model, define an objective function and constraints, and solve the problem implementing a greedy algorithm and a genetic algorithm. The framework is evaluated with regard to Quality of Service parameters of IoT applications and the utilization of fog resources using a real-world operational testbed. The evaluation shows that the service placement is adapted according to the demand and the available resources in the fog landscape. The greedy placement leads to the maximum utilization of edge devices keeping at the edge as many services as possible, while the placement based on the genetic algorithm keeps devices from overloads by balancing between the cloud and edge. When comparing edge and cloud deployment, the service deployment time at the edge takes 14% of the deployment time in the cloud. If fog resources are utilized at maximum capacity, and a new application request arrives with the need of certain sensor equipment, service deployment becomes impossible, and the application needs to be delegated to other fog resources. The genetic algorithm allows to better accommodate new applications and keep the utilization of edge devices at about 50% CPU. During the experiments, the framework successfully reacts to runtime events: (i) services are recovered when devices disappear from the fog landscape; (ii) cloud resources and highly utilized devices are released by migrating services to new devices; (iii) and in case of overloads, services are migrated in order to release resources.

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piFogBed: A Fog Computing Testbed Based on Raspberry Pi

Cited by 16 publications

References 18 publications

Support Mobile Fog Computing Test in piFogBedII

Support Mobile Fog Computing Test in piFogBedII

Multi-Level Resource Sharing Framework Using Collaborative Fog Environment for Smart Cities

FogFrame: a framework for IoT application execution in the fog

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