Internet-of-things (IoT) technology has been widely adopted in environment control and monitoring applications [2]. Many of these applications actuate in the environment based on results from data analysis that surpass the computational capacities of the IoT devices. Edge computing nodes [5] can be used to provide computational resources to end-devices in their proximity. In complex environments, a large number of devices are deployed simultaneously to support different applications, requiring the availability of several distinct runtime environments at the edge node to support different classes of requests.
{"title":"Container lifecycle management for edge nodes: poster","authors":"Chih-Peng Wu, M. Suresh, D. D. Silva","doi":"10.1145/3132211.3132453","DOIUrl":"https://doi.org/10.1145/3132211.3132453","url":null,"abstract":"Internet-of-things (IoT) technology has been widely adopted in environment control and monitoring applications [2]. Many of these applications actuate in the environment based on results from data analysis that surpass the computational capacities of the IoT devices. Edge computing nodes [5] can be used to provide computational resources to end-devices in their proximity. In complex environments, a large number of devices are deployed simultaneously to support different applications, requiring the availability of several distinct runtime environments at the edge node to support different classes of requests.","PeriodicalId":389022,"journal":{"name":"Proceedings of the Second ACM/IEEE Symposium on Edge Computing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121116605","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}
This work presents an architecture solution for enabling real-time vision processing on the edge next to video cameras. We demonstrate the benefits of proposed solution by constructing and real-time processing of complete vision applications for real-time object detection and tracking. It combines six challenging vision kernels including image smoothing, Mixture of Gaussians (MoG) background subtraction, morphology (dilation and erosion), component labeling, histogram checking and Kalman filter. We prototype the proposed solution on a Xilinx Zynq-based platform processing 1080p frames at 30Hz. It executes 40GOPs at only 1.7Watts of on-chip power, far beyond the processing capabilities of state-of-the-art vision processing platforms.
{"title":"Toward end-to-end object detection and tracking on the edge","authors":"H. Tabkhi","doi":"10.1145/3132211.3132455","DOIUrl":"https://doi.org/10.1145/3132211.3132455","url":null,"abstract":"This work presents an architecture solution for enabling real-time vision processing on the edge next to video cameras. We demonstrate the benefits of proposed solution by constructing and real-time processing of complete vision applications for real-time object detection and tracking. It combines six challenging vision kernels including image smoothing, Mixture of Gaussians (MoG) background subtraction, morphology (dilation and erosion), component labeling, histogram checking and Kalman filter. We prototype the proposed solution on a Xilinx Zynq-based platform processing 1080p frames at 30Hz. It executes 40GOPs at only 1.7Watts of on-chip power, far beyond the processing capabilities of state-of-the-art vision processing platforms.","PeriodicalId":389022,"journal":{"name":"Proceedings of the Second ACM/IEEE Symposium on Edge Computing","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131161078","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}
Ikechukwu Maduako, Hung Cao, Lilian Hernandez, M. Wachowicz
Mobility analytics using data generated from the Internet of Mobile Things (IoMT) is facing many challenges which range from the ingestion of data streams coming from a vast number of fog nodes and IoMT devices to avoiding overflowing the cloud with useless massive data streams that can trigger bottlenecks [1]. Managing data flow is becoming an important part of the IoMT because it will dictate in which platform analytical tasks should run in the future. Data flows are usually a sequence of out-of-order tuples with a high data input rate, and mobility analytics requires a real-time flow of data in both directions, from the edge to the cloud, and vice-versa. Before pulling the data streams to the cloud, edge data stream processing is needed for detecting missing, broken, and duplicated tuples in addition to recognize tuples whose arrival time is out of order. Analytical tasks such as data filtering, data cleaning and low-level data contextualization can be executed at the edge of a network. In contrast, more complex analytical tasks such as graph processing can be deployed in the cloud, and the results of ad-hoc queries and streaming graph analytics can be pushed to the edge as needed by a user application. Graphs are efficient representations used in mobility analytics because they unify knowledge about connectivity, proximity and interaction among moving things.
{"title":"Combining edge and cloud computing for mobility analytics: poster abstract","authors":"Ikechukwu Maduako, Hung Cao, Lilian Hernandez, M. Wachowicz","doi":"10.1145/3132211.3132452","DOIUrl":"https://doi.org/10.1145/3132211.3132452","url":null,"abstract":"Mobility analytics using data generated from the Internet of Mobile Things (IoMT) is facing many challenges which range from the ingestion of data streams coming from a vast number of fog nodes and IoMT devices to avoiding overflowing the cloud with useless massive data streams that can trigger bottlenecks [1]. Managing data flow is becoming an important part of the IoMT because it will dictate in which platform analytical tasks should run in the future. Data flows are usually a sequence of out-of-order tuples with a high data input rate, and mobility analytics requires a real-time flow of data in both directions, from the edge to the cloud, and vice-versa. Before pulling the data streams to the cloud, edge data stream processing is needed for detecting missing, broken, and duplicated tuples in addition to recognize tuples whose arrival time is out of order. Analytical tasks such as data filtering, data cleaning and low-level data contextualization can be executed at the edge of a network. In contrast, more complex analytical tasks such as graph processing can be deployed in the cloud, and the results of ad-hoc queries and streaming graph analytics can be pushed to the edge as needed by a user application. Graphs are efficient representations used in mobility analytics because they unify knowledge about connectivity, proximity and interaction among moving things.","PeriodicalId":389022,"journal":{"name":"Proceedings of the Second ACM/IEEE Symposium on Edge Computing","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131548755","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}
Small cell base stations (SBSs) endowed with cloud-like computing capabilities are considered as a key enabler of edge computing (EC), which provides ultra-low latency and location-awareness for a variety of emerging mobile applications and the Internet of Things. However, due to the limited computation resources of an individual SBS, providing computation services of high quality to its users faces significant challenges when it is overloaded with an excessive amount of computation workload. In this paper, we propose collaborative edge computing among SBSs by forming SBS coalitions to share computation resources with each other, thereby accommodating more computation workload in the edge system and reducing reliance on the remote cloud. A novel SBS coalition formation algorithm is developed based on the coalitional game theory to cope with various new challenges in small-cell-based edge systems, including the co-provisioning of radio access and computing services, cooperation incentives, and potential security risks. To address these challenges, the proposed method (1) allows collaboration at both the user-SBS association stage and the SBS peer offloading stage by exploiting the ultra dense deployment of SBSs, (2) develops a payment-based incentive mechanism that implements proportionally fair utility division to form stable SBS coalitions, and (3) builds a social trust network for managing security risks among SBSs due to collaboration. Systematic simulations in practical scenarios are carried out to evaluate the efficacy and performance of the proposed method, which shows that tremendous edge computing performance improvement can be achieved.
{"title":"Socially trusted collaborative edge computing in ultra dense networks","authors":"Lixing Chen, Jie Xu","doi":"10.1145/3132211.3134451","DOIUrl":"https://doi.org/10.1145/3132211.3134451","url":null,"abstract":"Small cell base stations (SBSs) endowed with cloud-like computing capabilities are considered as a key enabler of edge computing (EC), which provides ultra-low latency and location-awareness for a variety of emerging mobile applications and the Internet of Things. However, due to the limited computation resources of an individual SBS, providing computation services of high quality to its users faces significant challenges when it is overloaded with an excessive amount of computation workload. In this paper, we propose collaborative edge computing among SBSs by forming SBS coalitions to share computation resources with each other, thereby accommodating more computation workload in the edge system and reducing reliance on the remote cloud. A novel SBS coalition formation algorithm is developed based on the coalitional game theory to cope with various new challenges in small-cell-based edge systems, including the co-provisioning of radio access and computing services, cooperation incentives, and potential security risks. To address these challenges, the proposed method (1) allows collaboration at both the user-SBS association stage and the SBS peer offloading stage by exploiting the ultra dense deployment of SBSs, (2) develops a payment-based incentive mechanism that implements proportionally fair utility division to form stable SBS coalitions, and (3) builds a social trust network for managing security risks among SBSs due to collaboration. Systematic simulations in practical scenarios are carried out to evaluate the efficacy and performance of the proposed method, which shows that tremendous edge computing performance improvement can be achieved.","PeriodicalId":389022,"journal":{"name":"Proceedings of the Second ACM/IEEE Symposium on Edge Computing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122893828","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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