Pub Date : 2021-12-06DOI: 10.1109/FMEC54266.2021.9732409
Patient Ntumba, N. Georgantas, V. Christophides
Data stream processing and analytics (DSPA) applications are widely used to process the ever increasing amounts of data streams produced by highly geographical distributed data sources such as fixed and mobile IoT devices in order to extract valuable information in a timely manner for real-time actuation. To efficiently handle this ever increasing amount of data streams, the emerging Edge/Fog computing paradigms is used as the middle-tier between the Cloud and the IoT devices to process data streams closer to their sources and to reduce the network resource usage and network delay to reach the Cloud. In this paper, we account for the fact that both network resources and computational resources can be limited and shareable among multiple DSPA applications in the Edge-Fog-Cloud architecture, hence it is necessary to ensure their efficient usage. In this respect, we propose a resource-aware and time-efficient heuristic called SOO that identifies a good DSPA operator placement on the Edge-Fog-Cloud architecture towards optimizing the trade-off between the computational and network resource usage. Via thorough simulation experiments, we show that the solution provided by SOO is very close to the optimal one while the execution time is considerably reduced.
{"title":"Efficient Scheduling of Streaming Operators for IoT Edge Analytics","authors":"Patient Ntumba, N. Georgantas, V. Christophides","doi":"10.1109/FMEC54266.2021.9732409","DOIUrl":"https://doi.org/10.1109/FMEC54266.2021.9732409","url":null,"abstract":"Data stream processing and analytics (DSPA) applications are widely used to process the ever increasing amounts of data streams produced by highly geographical distributed data sources such as fixed and mobile IoT devices in order to extract valuable information in a timely manner for real-time actuation. To efficiently handle this ever increasing amount of data streams, the emerging Edge/Fog computing paradigms is used as the middle-tier between the Cloud and the IoT devices to process data streams closer to their sources and to reduce the network resource usage and network delay to reach the Cloud. In this paper, we account for the fact that both network resources and computational resources can be limited and shareable among multiple DSPA applications in the Edge-Fog-Cloud architecture, hence it is necessary to ensure their efficient usage. In this respect, we propose a resource-aware and time-efficient heuristic called SOO that identifies a good DSPA operator placement on the Edge-Fog-Cloud architecture towards optimizing the trade-off between the computational and network resource usage. Via thorough simulation experiments, we show that the solution provided by SOO is very close to the optimal one while the execution time is considerably reduced.","PeriodicalId":217996,"journal":{"name":"2021 Sixth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124075003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-06DOI: 10.1109/FMEC54266.2021.9732552
Xuan-Thuy Dang, Ricardo Knauer, Sebastian Peters, F. Sivrikaya
Recent advances in mobile communication, mobile cloud, and Internet of Things (IoT) technologies are transforming various vertical application domains. Particularly eHealth ser-vices are expected to take advantage of those enabling technolo-gies to evolve towards remote, patient-centric, and individualized care. In this work, we study the role of the fifth generation mobile technology (5G) as an integrator for various enablers to create a converged infrastructure for future eHealth applications. We first analyze high-impact and industry-driven eHealth use cases and identify challenges. We then propose a reference architecture for a future eHealth infrastructure, which is a fusion of cloud, edge, IoT, and other emerging technologies. Finally, to evalu-ate the proposed architecture, we implement a remote patient monitoring service comprising the architecture components and enablers, i.e., mobile device sensors and distributed intelligent data analysis. The evaluation shows an effective integration of artificial intelligence (AI) components to support novel eHealth applications.
{"title":"A Converged Cloud-Fog Architecture for Future eHealth Applications","authors":"Xuan-Thuy Dang, Ricardo Knauer, Sebastian Peters, F. Sivrikaya","doi":"10.1109/FMEC54266.2021.9732552","DOIUrl":"https://doi.org/10.1109/FMEC54266.2021.9732552","url":null,"abstract":"Recent advances in mobile communication, mobile cloud, and Internet of Things (IoT) technologies are transforming various vertical application domains. Particularly eHealth ser-vices are expected to take advantage of those enabling technolo-gies to evolve towards remote, patient-centric, and individualized care. In this work, we study the role of the fifth generation mobile technology (5G) as an integrator for various enablers to create a converged infrastructure for future eHealth applications. We first analyze high-impact and industry-driven eHealth use cases and identify challenges. We then propose a reference architecture for a future eHealth infrastructure, which is a fusion of cloud, edge, IoT, and other emerging technologies. Finally, to evalu-ate the proposed architecture, we implement a remote patient monitoring service comprising the architecture components and enablers, i.e., mobile device sensors and distributed intelligent data analysis. The evaluation shows an effective integration of artificial intelligence (AI) components to support novel eHealth applications.","PeriodicalId":217996,"journal":{"name":"2021 Sixth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130310840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the growing popularity of the Internet of Things, edge computing concept has been widely studied to relieve the load on the original cloud and networks while improving the service quality for end-users. To simulate such a complex environment involving edge and cloud computing, EdgeCloudSim has been widely adopted. However, it suffers from certain efficiency and scalability issues due to the ignorance of the deficiency in the originally adopted data structures and maintenance strategies. Specifically, it generates all events at beginning of the simulation and stores unnecessary historical information, both result in unnecessarily high complexity for search operations. In this work, by fixing the mismatches on the concept of discrete-event simulation, we propose enhancement of EdgeCloudSim which improves not only the runtime efficiency of simulation, but also the flexibility and scalability. Through extensive experiments with statistical methods, we show that the enhancement does not affect the expressiveness of simulations while obtaining 2 orders of magnitude speedup, especially when the device count is large.
{"title":"Renovation of EdgeCloudSim: An Efficient Discrete-Event Approach","authors":"Raphael Freymann, Junjie Shi, Jian-Jia Chen, Kuan-Hsun Chen","doi":"10.1109/FMEC54266.2021.9732572","DOIUrl":"https://doi.org/10.1109/FMEC54266.2021.9732572","url":null,"abstract":"Due to the growing popularity of the Internet of Things, edge computing concept has been widely studied to relieve the load on the original cloud and networks while improving the service quality for end-users. To simulate such a complex environment involving edge and cloud computing, EdgeCloudSim has been widely adopted. However, it suffers from certain efficiency and scalability issues due to the ignorance of the deficiency in the originally adopted data structures and maintenance strategies. Specifically, it generates all events at beginning of the simulation and stores unnecessary historical information, both result in unnecessarily high complexity for search operations. In this work, by fixing the mismatches on the concept of discrete-event simulation, we propose enhancement of EdgeCloudSim which improves not only the runtime efficiency of simulation, but also the flexibility and scalability. Through extensive experiments with statistical methods, we show that the enhancement does not affect the expressiveness of simulations while obtaining 2 orders of magnitude speedup, especially when the device count is large.","PeriodicalId":217996,"journal":{"name":"2021 Sixth International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134189462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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