With the rapid development of artificial intelligence,face recognition systems are widely used in daily lives.Face recognition applications often need to process large amounts of image data.Maintaining the accuracy an...With the rapid development of artificial intelligence,face recognition systems are widely used in daily lives.Face recognition applications often need to process large amounts of image data.Maintaining the accuracy and low latency is critical to face recognition systems.After analyzing the two-tier architecture“client-cloud”face recognition systems,it is found that these systems have high latency and network congestion when massive recognition requirements are needed to be responded,and it is very inconvenient and inefficient to deploy and manage relevant applications on the edge of the network.This paper proposes a flexible and efficient edge computing accelerated architecture.By offloading part of the computing tasks to the edge server closer to the data source,edge computing resources are used for image preprocessing to reduce the number of images to be transmitted,thus reducing the network transmission overhead.Moreover,the application code does not need to be rewritten and can be easily migrated to the edge server.We evaluate our schemes based on the open source Azure IoT Edge,and the experimental results show that the three-tier architecture“Client-Edge-Cloud”face recognition system outperforms the state-of-art face recognition systems in reducing the average response time.展开更多
A content delivery network(CDN)aims to reduce the content delivery latency to end-users by using distributed cache servers.Nevertheless,deploying and maintaining cache servers on a large scale is very expensive.To sol...A content delivery network(CDN)aims to reduce the content delivery latency to end-users by using distributed cache servers.Nevertheless,deploying and maintaining cache servers on a large scale is very expensive.To solve this problem,CDN providers have developed a new content delivery strategy:allowing end-users’s IoT edge devices to share their storage/bandwidth resources.This new edge CDN platform must address two core questions:(1)how can we incentivize end users to share IoT devices?(2)how can we facilitate a safe and transparent content transaction environment for end users?This paper introduces SmartSharing,a new content delivery network solution to address these questions.In smartSharing,the over-the-top(OTT)IoT devices belonging to end-users are used as mini-cache servers.To motivate end users to share the idle devices and storage/bandwidth resources,SmartSharing designs the content delivery schedule and the pricing scheme based on game theory and machine learning algorithms(specifically,a tailored expectation-maximization(EM)algorithm).To facilitate content trading among end users,SmartSharing creates a secure and transparent transaction platform based on smart contracts in Ethereum.In addition,SmartSharing’s performance evaluation is through trace-driven simulations in the real world and a prototype using content metadata and the achieved pricing schemes.The evaluation results show that CDN providers,end users and content providers can all benefit from our SmartSharing framework.展开更多
基金This work is supported by the National Key Research and Development Program of China under Grant(No.2016YFB1000302)the National Natural Science Foundation of China under Grant(No.61832020).
文摘With the rapid development of artificial intelligence,face recognition systems are widely used in daily lives.Face recognition applications often need to process large amounts of image data.Maintaining the accuracy and low latency is critical to face recognition systems.After analyzing the two-tier architecture“client-cloud”face recognition systems,it is found that these systems have high latency and network congestion when massive recognition requirements are needed to be responded,and it is very inconvenient and inefficient to deploy and manage relevant applications on the edge of the network.This paper proposes a flexible and efficient edge computing accelerated architecture.By offloading part of the computing tasks to the edge server closer to the data source,edge computing resources are used for image preprocessing to reduce the number of images to be transmitted,thus reducing the network transmission overhead.Moreover,the application code does not need to be rewritten and can be easily migrated to the edge server.We evaluate our schemes based on the open source Azure IoT Edge,and the experimental results show that the three-tier architecture“Client-Edge-Cloud”face recognition system outperforms the state-of-art face recognition systems in reducing the average response time.
基金supported by a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada(NSERC).
文摘A content delivery network(CDN)aims to reduce the content delivery latency to end-users by using distributed cache servers.Nevertheless,deploying and maintaining cache servers on a large scale is very expensive.To solve this problem,CDN providers have developed a new content delivery strategy:allowing end-users’s IoT edge devices to share their storage/bandwidth resources.This new edge CDN platform must address two core questions:(1)how can we incentivize end users to share IoT devices?(2)how can we facilitate a safe and transparent content transaction environment for end users?This paper introduces SmartSharing,a new content delivery network solution to address these questions.In smartSharing,the over-the-top(OTT)IoT devices belonging to end-users are used as mini-cache servers.To motivate end users to share the idle devices and storage/bandwidth resources,SmartSharing designs the content delivery schedule and the pricing scheme based on game theory and machine learning algorithms(specifically,a tailored expectation-maximization(EM)algorithm).To facilitate content trading among end users,SmartSharing creates a secure and transparent transaction platform based on smart contracts in Ethereum.In addition,SmartSharing’s performance evaluation is through trace-driven simulations in the real world and a prototype using content metadata and the achieved pricing schemes.The evaluation results show that CDN providers,end users and content providers can all benefit from our SmartSharing framework.
