MockFog: Emulating Fog Computing Infrastructure in the Cloud

Hasenburg, Jonathan; Grambow, Martin; Grünewald, Elias; Huk, Sascha; Bermbach, David

doi:10.1109/icfc.2019.00026

Cited by 41 publications

(24 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this simulator, the impact of mobility on service migration is studied using an experiment-based approach. MockFog [39] is a freely available simulator where users can simulate small scale fog networks, and test the performance by setting up the network characteristics. Héctor [40] is a simulator for automatic testing of IoT devices.…”

Section: B Fog Simulatorsmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: B Fog Simulatorsmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…One option is to measure processor capacity and load or the number of cores or virtual cores. 53 Alternatively, generic and traditional metrics such as FLOPS (FLoating-point Operations Per Second) and MIPS (Million Instructions Per Second) can be used. 54 Several variants and derivative metrics have been defined and a good overview is given by Wang et al 55 iFogSim defines hardware units with the following indicators:…”

Section: Hardware and Connectivitymentioning

confidence: 99%

“…Modeling the computational performance is not as trivial and several different approaches and metrics exist. One option is to measure processor capacity and load or the number of cores or virtual cores 53 . Alternatively, generic and traditional metrics such as FLOPS (FLoating‐point Operations Per Second) and MIPS (Million Instructions Per Second) can be used 54 .…”

Section: Modeling Of Software and Hardware Systemsmentioning

confidence: 99%

Evaluating system architectures for driving range estimation and charge planning for electric vehicles

Thorgeirsson

Vaillant²,

Scheubner

et al. 2020

Softw Pract Exp

View full text Add to dashboard Cite

Due to sparse charging infrastructure and short driving ranges, drivers of battery electric vehicles (BEVs) can experience range anxiety, which is the fear of stranding with an empty battery. To help eliminate range anxiety and make BEVs more attractive for customers, accurate range estimation methods need to be developed. In recent years, many publications have suggested machine learning algorithms as a fitting method to achieve accurate range estimations. However, these algorithms use a large amount of data and have high computational requirements. A traditional placement of the software within a vehicle's electronic control unit could lead to high latencies and thus detrimental to user experience. But since modern vehicles are connected to a backend, where software modules can be implemented, high latencies can be prevented with intelligent distribution of the algorithm parts. On the other hand, communication between vehicle and backend can be slow or expensive. In this article, an intelligent deployment of a range estimation software based on ML is analyzed. We model hardware and software to enable performance evaluation in early stages of the development process. Based on simulations, different system architectures and module placements are then analyzed in terms of latency, network usage, energy usage, and cost. We show that a distributed system with cloud-based module placement reduces the end-to-end latency significantly, when compared with a traditional vehicle-based placement. Furthermore, we show that network usage is significantly reduced. This intelligent system enables the application of complex, but accurate range estimation with low latencies, resulting in an improved user experience, which enhances the practicality and acceptance of BEVs.

show abstract

“…Popular Fog and Edge computing based simulators include EdgeCloudSim [25], iFogSim [8], MyiFogSim [16] and FogExplorer 29 . Recently, emulation is also used to evaluate the performance of Fog applications [9].…”

Section: Related Workmentioning

confidence: 99%

DeFog

McChesney

Wang

Tanwer

et al. 2019

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

View full text Add to dashboard Cite

Fog computing envisions that deploying services of an application across resources in the cloud and those located at the edge of the network may improve the overall performance of the application when compared to running the application on the cloud. However, there are currently no benchmarks that can directly compare the performance of the application across the cloud-only, edgeonly and cloud-edge deployment platform to obtain any insight on performance improvement. This paper proposes DeFog, a first Fog benchmarking suite to: (i) alleviate the burden of Fog benchmarking by using a standard methodology, and (ii) facilitate the understanding of the target platform by collecting a catalogue of relevant metrics for a set of benchmarks. The current portfolio of DeFog benchmarks comprises six relevant applications conducive to using the edge. Experimental studies are carried out on multiple target platforms to demonstrate the use of DeFog for collecting metrics related to application latencies (communication and computation), for understanding the impact of stress and concurrent users on application latencies, and for understanding the performance of deploying different combination of services of an application across the cloud and edge. DeFog is available for public download (https://github.com/qub-blesson/DeFog). CCS CONCEPTS • Computing methodologies → Distributed computing methodologies; • Computer systems organization → Embedded and cyber-physical systems; • Software and its engineering → Software notations and tools.

show abstract

MockFog: Emulating Fog Computing Infrastructure in the Cloud

Cited by 41 publications

References 18 publications

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

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

Evaluating system architectures for driving range estimation and charge planning for electric vehicles

DeFog

Contact Info

Product

Resources

About