SatEdgeSim: A Toolkit for Modeling and Simulation of Performance Evaluation in Satellite Edge Computing Environments

Li, Xiaoxiu; Cao, Suzhi; Pan, Siyan; Han, J. L.; Lei, Yan; Zhang, Lei

doi:10.1109/iccsn49894.2020.9139057

Cited by 22 publications

(20 citation statements)

References 22 publications

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“…Finally, a case study is simulated to evaluate the simulator's performance and valuable results are collected. SatEdgeSim [41] is PureEdgeSim based simulator for modeling satellite edge computing-based networks. The impact of various task deployment strategies is studied.…”

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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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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“…To study the effect of cloud-edge collaboration on our system model, we implemented the same network configuration using PureEdgeSim 5 ; another extension of the CloudSim designed to efficiently handle various resource management strategies in cloud, edge, and mist computing environments [47]. PureEdgeSim allows for a highly scalable design capable of integration thousands of IIoT devices into the setup.…”

Section: B Effects Of Cloud-edge Collaboration (Pureedgesim)mentioning

confidence: 99%

SDN-Enabled Resource Orchestration for Industrial IoT in Collaborative Edge-Cloud Networks

et al. 2021

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Effective, long-lasting Industrial IoT (IIoT) solutions start with short-term gains and progressively mature with added capabilities and value. The heterogeneous nature of IIoT devices and services suggests frequent changes in resource requirements for different services, applications, and use cases. With such unpredictability, resource orchestration can be quite complicated even in basic use cases and almost impossible to handle in some extensively dynamic use cases. In this paper, we propose SDRM; an SDN-enabled Resource Management scheme. This novel orchestration methodology automatically computes the optimal resource allocation for different IIoT network models and dynamically adjust assigned resources based on predefined constraints to ensure Service Level Agreement (SLA). The proposed approach models resource allocation as a Constraint Satisfaction Problem (CSP) where optimality is based on the solution of a predefined Satisfiability (SAT) problem. This model supports centralized management of all resources using a software defined approach. Such resources include memory, power, bandwidth, and edgecloud resources. SDRM aims at accelerating efficient resource orchestration through dynamic workload balancing and edge-cloud resource utilization, thereby reducing the cost of IIoT system deployment and improving the overall ROI for adopting IIoT solutions. We model our resource allocation approach on SAVILE ROW using ESSENSE PRIME modeling language, we then implement the network model on CloudSimSDN and PureEdgeSim. We present a detailed analysis of the system architecture and the key technologies of the model. We finally demonstrate the efficiency of the model by presenting experimental results from a prototype system. Our test results show an extremely low solver time ranging from 0.47 ms to 0.5 ms for nodes ranging from 100 to 500 nodes. With edge-cloud collaboration, our results show about 4 percent improvement in overall task success rates.INDEX TERMS Constraint satisfaction problem (CSP), software defined resource management (SDRM), software defined networking (SDN), edge computing, cloud computing, Industry 4.0, Internet of Things (IoT), Industrial IoT (IIoT), resource management.

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“…Although IoTNetSim is highly modular, it lacks benchmark policies for mobilitydriven service management and dynamic cluster formation. Wei et al proposed another simulator named SatEdgeSim for evaluating the performance of service management policies in three-tier satellite edge computing environments [18]. Considering the high mobility of satellite nodes, It supports the dynamic alteration in network topology and imitates the impact of communication distance on service offloading delay.…”

Section: Related Workmentioning

confidence: 99%

“…All nodes but the proxy server within a block act as the gateway for the IoT devices. As a means of notations, proxy servers are specified as the tier-1 nodes [10] FogNetSim++ [11] IoTSim-Edge [12] MobFogSim [13] PureEdgeSim [14] STEP-ONE [15] YAFS [16] IoTNetSim [17] SatEdgeSim [18] ECSNeT++ [19] IoTSim-Osmosis [20] However, while parsing the location information of both Edge/Fog nodes and users/IoT devices, the DataParser class creates separate Location objects for each coordinate. The block-wise information of the respective entities (servers and mobile objects) can also be included in a Location object.…”

Section: Mobilitymentioning

confidence: 99%

“…The calculateIn-Range function is responsible to calculate the distance of the current node to other Edge/Fog nodes. The latency of the current node to each CM will be estimated and stored in mapCMToLatency (lines [17][18]. If the communication latency of CMs is a clustering factor (which is checked by the lf flag), the list of current CMs will be pruned to find the CMs satisfying latency constraint (lines [19][20][21][22][23][24][25].…”

Section: Clusteringmentioning

confidence: 99%

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IFogSim2: An Extended iFogSim Simulator for Mobility, Clustering, and Microservice Management in Edge and Fog Computing Environments

Mahmud¹,

Pallewatta²,

Goudarzi³

et al. 2021

Preprint

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Internet of Things (IoT) has already proven to be the building block for next-generation Cyber-Physical Systems (CPSs). The considerable amount of data generated by the IoT devices needs latency-sensitive processing, which is not feasible by deploying the respective applications in remote Cloud datacentres. Edge/Fog computing, a promising extension of Cloud at the IoT-proximate network, can meet such requirements for smart CPSs. However, the structural and operational differences of Edge/Fog infrastructure resist employing Cloud-based service regulations directly to these environments. As a result, many research works have been recently conducted, focusing on efficient application and resource management in Edge/Fog computing environments. Scalable Edge/Fog infrastructure is a must to validate these policies, which is also challenging to accommodate in the real-world due to high cost and implementation time. Considering simulation as a key to this constraint, various software has been developed that can imitate the physical behaviour of Edge/Fog computing environments. Nevertheless, the existing simulators often fail to support advanced service management features because of their monolithic architecture, lack of actual dataset, and limited scope for a periodic update. To overcome these issues, we have developed multiple simulation models for service migration, dynamic distributed cluster formation, and microservice orchestration for Edge/Fog computing in this work and integrated with the existing iFogSim simulation toolkit for launching it as iFogSim2. The performance of iFogSim2 and its built-in policies are evaluated using three use case scenarios and compared with the contemporary simulators and benchmark policies under different settings. Results indicate that the proposed solution outperform others in service management time, network usage, ram consumption, and simulation time.

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SatEdgeSim: A Toolkit for Modeling and Simulation of Performance Evaluation in Satellite Edge Computing Environments

Cited by 22 publications

References 22 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

SDN-Enabled Resource Orchestration for Industrial IoT in Collaborative Edge-Cloud Networks

IFogSim2: An Extended iFogSim Simulator for Mobility, Clustering, and Microservice Management in Edge and Fog Computing Environments

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