2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)最新文献_第4页

Towards Edge-enabled Distributed Computing Framework for Heterogeneous Android-based Devices 面向异构android设备的边缘分布式计算框架

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00082

Yongtao Yao, B. Liu, Yiwei Zhao, Weisong Shi

引用次数: 0

Quantum-enhanced Regression Analysis Using State-of-the-art QLSAs and QIPMs 使用最先进的qsas和qipm的量子增强回归分析

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00055

Mohammadhossein Mohammadisiahroudi, Zeguan Wu, Brandon Augustino, T. Terlaky, Arielle Carr

引用次数: 0

Poster: Scalable Quantum Convolutional Neural Networks for Edge Computing 海报:用于边缘计算的可扩展量子卷积神经网络

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00041

Jindi Wu, Qun Li

{"title":"Poster: Scalable Quantum Convolutional Neural Networks for Edge Computing","authors":"Jindi Wu, Qun Li","doi":"10.1109/SEC54971.2022.00041","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00041","url":null,"abstract":"The convolutional neural network (CNN) has become a general approach for image processing in machine learning tasks. Quantum CNN (QCNN) is an emerging method to implement CNN using quantum computing. Quantum computing utilizes the properties of quantum mechanics to perform efficient computing. However, current quantum machines do not support large-scale QCNNs due to a lack of qubits. As a consequence, QCNNs are limited in scale and cannot directly process high-dimensional images. These shortcomings result in suboptimal QCNN performance. Meanwhile, building quantum machines with enough qubits is technically difficult and costly. These obstacles motivate us to design a quantum edge computing (QEC) system capable of achieving the scalability of QCNNs. Quantum machines are organized hierarchically in the QEC system. The quantum machines closer to the users collaboratively load and extract quantum features from the high-dimensional input data. Subsequently, the quantum machine in the next layer collects the extracted features and performs further operations to produce the final results. Each quantum machine in the QEC system is equipped with a local small-scale QCNN to capture the data pattern of its input. The local QCNNs could be combined to form a large-scale QCNN capable of learning and processing high-dimensional data, overcoming hardware limitations and improving performance.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133674688","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}

引用次数: 1

Spiking Reservoir Computing for Temporal Edge Intelligence on Loihi Loihi上时间边缘智能的峰值库计算

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00081

Ramashish Gaurav, T. Stewart, Y. Yi

引用次数: 2

Energy-Optimal Sampling for Edge Computing Feedback Systems: Aperiodic Case 边缘计算反馈系统的能量最优采样:非周期情况

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00047

Vishnu Narayanan Moothedath

引用次数: 1

ML-ACE: Machine Learning Admission Control at the Edge ML-ACE:边缘的机器学习准入控制

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00048

Josh Minor

{"title":"ML-ACE: Machine Learning Admission Control at the Edge","authors":"Josh Minor","doi":"10.1109/SEC54971.2022.00048","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00048","url":null,"abstract":"ML inference has become an increasingly important workload for low-power, near-data edge computing platforms. There is a large existing body of work on how to optimize a trained model for inference on a resource-constrained device, however much of the work does not consider optimizations in how the model will be used by clients in the system. In this space, inference servers emerged to provide a client-server paradigm for inference, offering portable, practical client libraries for users of ML systems. These servers handle batching of requests, runtime optimizations, and placement of multiple replicas of models on CPU/GPU to maximize inference efficiency. Unlike the data center, much infrastructure at the edge lacks the ease in ability to recruit new machines to scale out these servers to meet increasing request demand. Because of this, efficient scheduling of models on these edge platforms is critical. This work presents ML-ACE, a system to systematically schedule ML inference on resource-constrained edge computing platforms. ML-ACE extends the existing client-server paradigm for inference serving by providing admission control, preventing user inference requests from over-saturating system resources.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117052123","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}

引用次数: 0

ShapeShifter: Resolving the Hidden Latency Contention Problem in MEC ShapeShifter:解决MEC中的隐藏延迟争用问题

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00026

V. Rakovic, K. Hsu, Ketan Bhardwaj, Ada Gavrilovska, L. Gavrilovska

{"title":"ShapeShifter: Resolving the Hidden Latency Contention Problem in MEC","authors":"V. Rakovic, K. Hsu, Ketan Bhardwaj, Ada Gavrilovska, L. Gavrilovska","doi":"10.1109/SEC54971.2022.00026","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00026","url":null,"abstract":"Mobile Edge Computing (MEC) creates new infrastructure at the edges of the mobile networks, thus providing transformative opportunities for applications seeking latency benefits by operating closer to end-users and devices. However, the reduced network distance between the application endpoints of the MEC flows causes pattern shifts in the packet bursts exchanged at the network edges. The longer and denser bursts create a new source of contention that is not considered by current solutions. As a result, naively collocating applications onto the MEC tier can negatively affect latency-critical workloads, resulting in up to 73% packets experiencing as much as 3.8x increased latency. This makes it impossible to support latency-centric SLOs in MEC, obviating its expected benefits from MEC. This paper is the first to describe this new contention point in mobile networks and its potentially crippling impact on the achievable latency benefit from MEC. We propose ShapeShifter, a new component in the MEC architecture which solves the MEC latency contention problem through adaptive latency-centric burst management of MEC flows. ShapeShifter is effective - it eliminates SLO violations for latency-critical applications and improves application performance in multi-tenant scenarios by up to 3.8 x – and practical – it can be deployed with minimal disruption to the current mobile network ecosystem.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117305763","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}

引用次数: 1

Improving the Quality of Inference for Applications using Chained DNN Models during Edge Server Handover 在边缘服务器切换过程中使用链式DNN模型提高应用程序的推理质量

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00079

Alex Xie, Yang Peng

{"title":"Improving the Quality of Inference for Applications using Chained DNN Models during Edge Server Handover","authors":"Alex Xie, Yang Peng","doi":"10.1109/SEC54971.2022.00079","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00079","url":null,"abstract":"Recent advances in deep neural networks (DNNs) have greatly benefited mobile applications that perform real-time video analytics. However, mobile devices' computing power usually limits them from inferring complex DNN models timely. Edge intelligence has emerged to help mobile apps offload DNN inference tasks to powerful edge servers for accelerated inference services. One major challenge that edge intelligence faces is maintaining a satisfactory quality of service when users move across edge servers. To address this issue, we propose a novel solution to help improve the quality of inference services for real-time video analytics applications that use chained DNN models. This solution includes two schemes: one maximizes the use of mobile devices to improve inference quality during the handover between edge servers, and the other provides offloading decisions to minimize the end-to-end inference delay when edge servers are available. We evaluate the proposed scheme using a DNN-based realtime traffic monitoring application through testbed and simulation experiments. The results show that our solution can improve inference quality by 52% during handover compared to the greedy algorithm-based solution.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115818336","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}

引用次数: 2

Demo: A Multi-Perspective Video Streaming System with Privacy Preservation in Trauma Room 演示:创伤室隐私保护的多视角视频流系统

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00042

Zhengyong Ren, Yuxin Yang, Kambiz Ghazinour, Sara Bayramzadeh, Qiang Guan

{"title":"Demo: A Multi-Perspective Video Streaming System with Privacy Preservation in Trauma Room","authors":"Zhengyong Ren, Yuxin Yang, Kambiz Ghazinour, Sara Bayramzadeh, Qiang Guan","doi":"10.1109/SEC54971.2022.00042","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00042","url":null,"abstract":"More and more hospitals are now deploying mul-tiple cameras in trauma room for a multi-perspective remote observation. but video surveillance system can cause privacy breach by showing and storing sensitive information of patients and staff. We use OpenPose which is a state-of-the-art human body skeletons estimation framework to extract 18 human key skeleton points. For privacy preservation, we can apply the image obfuscation techniques to human heads, we also can use human skeleton to replace the human body in the truth background. we proposed a head detection method based on the 5 key points of each head output from OpenPose. We applied the st-gcn algorithm to recognize human actions, we propose a interactive algorithm for multiple cameras to recognize and trace the same person in different cameras, Based on multi-view action recognition for the same person, we can take action recognition accuracy to a high level. Our experiment results prove that our proposed technique has a high performance in privacy protection applications. Now we focus on the interactive algorithm for multiple cameras.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115417585","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}

引用次数: 0

Quantum Angle Generator for Image Generation 用于图像生成的量子角发生器

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00064

Rehm Florian, Vallecorsa Sofia, Grossi Michele, Borras Kerstin, Krücker Dirk, Schnake Simon, Verney-Provatas Alexis-Harilaos

引用次数: 1