xFogSim: A Distributed Fog Resource Management Framework for Sustainable IoT Services

Malik, Asad Waqar; Qayyum, Tariq; Rahman, Anis Ur; Khattak, Muazzam A. Khan; Khalid, Osman; Khan, Samee U.

doi:10.1109/tsusc.2020.3025021

Cited by 20 publications

(10 citation statements)

References 22 publications

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“…FogNetSim++ [115] focuses on simulating large Fog networks and differs from others mainly by providing features that allow users to incorporate customized mobility models, scheduling algorithms, and manage handover mechanisms. XFogSim [90] extends FogNetSim++ to simulate federated fog computing environments. xFogSim is lightweight, configurable, scalable and introduces the concept of fog federation for resource sharing among fog locations.…”

Section: Simulation Systemsmentioning

confidence: 99%

Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review

Rosendo

Costan

Valduriez

et al. 2022

Journal of Parallel and Distributed Computing

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Section: Simulation Systemsmentioning

confidence: 99%

Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review

Rosendo

Costan

Valduriez

et al. 2022

Journal of Parallel and Distributed Computing

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“…Tariq et al [18] present the FogNetSim++ environment, which covers the network aspects such as latency, transmission range, scheduling, mobility, and heterogeneous mobile devices. Asad et al [19] propose a fog simulation framework named xFogSim to support latency-sensitive applications. It has a very efficient task distribution algorithm that can choose the best computation resource depending on the cost, availability, and latency.…”

Section: Related Workmentioning

confidence: 99%

Task Offloading and Scheduling Strategy for Intelligent Prosthesis in Mobile Edge Computing Environment

2022

Wireless Communications and Mobile Computing

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Traditional intent recognition algorithms of intelligent prosthesis often use deep learning technology. However, deep learning’s high accuracy comes at the expense of high computational and energy consumption requirements. Mobile edge computing is a viable solution to meet the high computation and real-time execution requirements of deep learning algorithm on mobile device. In this paper, we consider the computation offloading problem of multiple heterogeneous edge servers in intelligent prosthesis scenario. Firstly, we present the problem definition and the detail design of MEC-based task offloading model for deep neural network. Then, considering the mobility of amputees, the mobility-aware energy consumption model and latency model are proposed. By deploying the deep learning-based motion intent recognition algorithm on intelligent prosthesis in a real-world MEC environment, the effectiveness of the task offloading and scheduling strategy is demonstrated. The experimental results show that the proposed algorithms can always find the optimal task offloading and scheduling decision.

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“…Rehman et al [30] proposed a trust-based task mapping and resource sharing framework for vehicular fog networks. They applied the concept of 5G and utilized the concept [5] RECAP [6] FogTorch [7] MobFogSim [8] FogDirSim [9] PFogSim [10] Edge-Fog cloud [11] Limited SimpleIoTSimulator [12] OPNET [13] iFogSim [14] EdgeNetworkCloudSim [15] PureEdgeSim [16] MyiFogSim [17] iFogSimWithDataPlacement [18] EdgeCloudSim [19] StormOnEdge/SpanEdge [20] DockerSim [21] EmuFog [22] xFogSim [23] Limited Proposed Framework of trust matrix to form an off-street cluster of vehicles. A task mapping algorithm is also proposed which enhances the efficiency of resource sharing.…”

Section: A Fog Computing Frameworkmentioning

confidence: 99%

“…The results show that the proposed model is efficient in terms of delay, energy consumption, and task success rate. xFogSim [23] is a simulator designed in OMNeT++ 1 which can be used to simulate distributed fog networks. Medium-scale networks are simulated and it has a very efficient task distribution algorithm that chooses the best fog node depending on the cost, availability, and response time.…”

Section: B Fog Simulatorsmentioning

confidence: 99%

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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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xFogSim: A Distributed Fog Resource Management Framework for Sustainable IoT Services

Cited by 20 publications

References 22 publications

Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review

Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review

Task Offloading and Scheduling Strategy for Intelligent Prosthesis in Mobile Edge Computing Environment

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

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