2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)最新文献_第2页

BigMEC: Scalable Service Migration for Mobile Edge Computing BigMEC:移动边缘计算的可扩展服务迁移

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00018

Florian Brandherm, Julien Gedeon, Osama Abboud, M. Mühlhäuser

{"title":"BigMEC: Scalable Service Migration for Mobile Edge Computing","authors":"Florian Brandherm, Julien Gedeon, Osama Abboud, M. Mühlhäuser","doi":"10.1109/SEC54971.2022.00018","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00018","url":null,"abstract":"The proximity of Mobile Edge Computing offers the potential for offloading low latency closed-loop applications from mobile devices. However, to repair decreases in quality of service (QoS), e.g., resulting from user mobility, the placement of service instances must be continually updated - essential for mission critical applications that cannot tolerate decreased QoS, for example virtual reality or networked control systems. This paper presents BigMEC, a decentralized service placement algorithm that achieves scalable, fast, and high-quality placements by making local service migration decisions immediately when a drop in QoS is detected. The algorithm relies on reinforcement learning to adapt to unknown scenarios and to approximate long-term optimal placement updates by taking future transition costs into account. BigMEC limits each decentralized migration decision to nearby edge sites. Thus, decision computation times are independent of the number of nodes in the network and well below 10ms in our experimental setup. Our ablation study validates that, using its scalable approach to decentralized resource conflict resolution, BigMEC quickly approaches optimal placement with increasing local view size, and that it can reliably learn to approximate long-term optimal migration decisions, given only a black-box optimization objective.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"C-19 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":"126766593","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

Bias Detection and Generalization in AI Algorithms on Edge for Autonomous Driving 自动驾驶边缘AI算法的偏差检测与泛化

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00050

Dewant Katare, N. Kourtellis, Souneil Park, Diego Perino, M. Janssen, A. Ding

{"title":"Bias Detection and Generalization in AI Algorithms on Edge for Autonomous Driving","authors":"Dewant Katare, N. Kourtellis, Souneil Park, Diego Perino, M. Janssen, A. Ding","doi":"10.1109/SEC54971.2022.00050","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00050","url":null,"abstract":"A machine learning model can often produce biased outputs for a familiar group or similar sets of classes during inference over an unknown dataset. The generalization of neural networks have been studied to resolve biases, which has also shown improvement in accuracy and performance metrics, such as precision and recall, and refining the dataset's validation set. Data distribution and instances included in test and validation-set play a significant role in improving the generalization of neural networks. For producing an unbiased AI model, it should not only be trained to achieve high accuracy and minimize false positives. The goal should be to prevent the dominance of one class/feature over the other class/feature while calculating weights. This paper investigates state-of-art object detection/classification on AI models using metrics such as selectivity score and cosine similarity. We focus on perception tasks for vehicular edge scenarios, which generally include collaborative tasks and model updates based on weights. The analysis is performed using cases that include the difference in data diversity, the viewpoint of the input class and combinations. Our results show the potential of using cosine similarity, selectivity score and invariance for measuring the training bias, which sheds light on developing unbiased AI models for future vehicular edge services.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"56 5 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":"116557206","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

Energy-aware Edge Federated Learning for Enhanced Reliability and Sustainability 增强可靠性和可持续性的能源感知边缘联合学习

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00051

Matteo Mendula, P. Bellavista

{"title":"Energy-aware Edge Federated Learning for Enhanced Reliability and Sustainability","authors":"Matteo Mendula, P. Bellavista","doi":"10.1109/SEC54971.2022.00051","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00051","url":null,"abstract":"Federated Learning (FL) has emerged as a value added proposition for use in edge-based infrastructures, distributing the training process among collaborative workers without disclosing raw (user) data. In this context, we argue that, differently from what already present in most current literature, energy consumption of nodes (either workers or the ensembler node) is a central element to consider in FL, e.g., to have a more sustainable FL node selection strategy. To this end, a complete and detailed report about energy consumption at each FL round is required to allow for innovative and greener resource management approaches, taking into account residual energy and learning completion time of participating FL nodes. Filling this gap, we present the design of a novel distributed framework capable of collecting accurate (worker) energy expenditure and learning-centric metrics at each FL round. The frame-work comprises state-of-the-art technological building blocks, purposely integrated to enable advanced and energy-aware FL process orchestration capabilities. To validate the approach, we rely on a heterogeneous experimental testbed, and conduct a distributed learning process employing a realistic dataset. The preliminary evaluation results reported in this paper highlight the potential advantage in terms of overall energy consumption reduction and the suitability of an adaptive learning framework capable of autonomous evaluations of the most proper trade-off to apply between accuracy and energy expenditure.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"37 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":"117247360","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

Learn to Learn on Chip: Hardware-aware Meta-learning for Quantized Few-shot Learning at the Edge 学会在芯片上学习:硬件感知的元学习在边缘量化少量学习

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00009

Nitish Satya Murthy, Peter Vrancx, Nathan Laubeuf, P. Debacker, F. Catthoor, M. Verhelst

{"title":"Learn to Learn on Chip: Hardware-aware Meta-learning for Quantized Few-shot Learning at the Edge","authors":"Nitish Satya Murthy, Peter Vrancx, Nathan Laubeuf, P. Debacker, F. Catthoor, M. Verhelst","doi":"10.1109/SEC54971.2022.00009","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00009","url":null,"abstract":"Recent years have seen a growing trend of deploying deep neural network-based applications on edge devices. Many of these applications, such as biometric identification, activity tracking, user preference learning, etc., require fine-tuning of the trained networks for user personalization. One way to prepare these models to handle new, unseen tasks, is to pre-train them on a distribution of known tasks. This observation has led to increasing research into meta-learning based few-shot learning techniques. However, basic meta-learning approaches do not account for the limited memory and computational resources during on-chip training. We propose a modified meta-learning algorithm that enables quantized fine-tuning to optimally condition the models for on-chip few shot learning. The modification involves the inclusion of target hardware constraints upfront in the meta-learning process. Block floating point datatypes with low precision mantissa bits are utilized in the forward and backward passes, to allow hardware-friendly adaptation. Experiments show that our algorithm provides better initializations than conventional algorithms, more suitable for efficient quantized fine-tuning. This allows the few-shot learner to achieve better convergence, in terms of accuracy and speed. Extensive experiments are also performed to analyze the impact of initialization on quantized fine-tuning and further corroborate the benefits of our method.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"29 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":"123323278","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

Poster: Edge Computing for Deep Learning-based Sensor Multi-Target Detection 海报:基于深度学习的传感器多目标检测的边缘计算

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00033

Alperen Kalay, Alparslan Fisne

引用次数: 1

Preva: Protecting Inference Privacy through Policy-based Video-frame Transformation Preva:基于策略的视频帧变换保护推理隐私

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00021

Rui Lu, Siping Shi, Dan Wang, Chuang Hu, Bihai Zhang

{"title":"Preva: Protecting Inference Privacy through Policy-based Video-frame Transformation","authors":"Rui Lu, Siping Shi, Dan Wang, Chuang Hu, Bihai Zhang","doi":"10.1109/SEC54971.2022.00021","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00021","url":null,"abstract":"Real-time edge-cloud video analytics systems have been widely used to support such applications as traffic counting, surveillance, autonomous driving, Metaverse, etc. In such a system, the edge and the cloud cooperatively conduct model inference of the video frames captured by the camera of the edge, using a trained DNN model of the video analytics application. The edge conducts initial analytics on the video frames to a split layer of the DNN model; and then sends intermediate results to the cloud for follow-up analytics. In this paper, we show that an attacker can perform reconstruction attacks to the intermediate results; and private information of the raw video frames, e.g., a plate number of a car, can be leaked. In this paper, we present Preva, a new Privacy preserving Real-time Edge-cloud Video Analytics system. The core idea of Preva is to conduct image transformation on the video frames, as preprocessing, prior to the video frames starting the edge-cloud video analytics process, so that during edge-cloud video analytics, the intermediate results will not leak private information under attack. We design a policy-based video-frame transformation scheme. Given the resource constraints of the edge, Preva ensures high accuracy in the final video analytics results and minimizes privacy leakage in any split layer. We present a formal privacy analysis and we show that Preva can guarantee privacy leakage under the reconstruction attacks of both outsider attackers and insider attackers. We evaluate Preva through three video analytics applications and we show that Preva outperforms existing systems for 64.4% in analytics accuracy and 59.2% in privacy leakage.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"15 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":"121652685","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: 3

Opportunities for Optimizing the Container Runtime 优化容器运行时的机会

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00028

Adam Hall, U. Ramachandran

{"title":"Opportunities for Optimizing the Container Runtime","authors":"Adam Hall, U. Ramachandran","doi":"10.1109/SEC54971.2022.00028","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00028","url":null,"abstract":"Container-based virtualization provides lightweight mechanisms for process isolation and resource control that are essential for maintaining a high degree of multi-tenancy in Function-as-a-Service (FaaS) platforms, where compute functions are instantiated on-demand and exist only as long as their exe-cution is active. This model is especially advantageous for Edge computing environments, where hardware resources are limited due to physical space constraints. Despite their many advantages, state-of-the-art container runtimes still suffer from startup delays of several hundred milliseconds. This delay adversely impacts user experience for existing human-in-the-loop applications and quickly erodes the low latency response times required by emerging machine-in-the-loop IoT and Edge computing applications utilizing FaaS. In turn, it causes developers of these applications to employ unsanctioned workarounds that artificially extend the lifetime of their functions, resulting in wasted platform resources. In this paper, we provide an exploration of the cause of this startup delay and insight on how container-based virtualization might be made more efficient for FaaS scenarios at the Edge. Our results show that a small number of container startup operations account for the majority of cold start time, that several of these operations have room for improvement, and that startup time is largely bound by the underlying operating system mechanisms that are the building blocks for containers. We draw on our detailed analysis to provide guidance toward developing a container runtime for Edge computing environments and demonstrate how making a few key improvements to the container creation process can lead to a 20 % reduction in cold start time.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"324 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113958684","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

QuYBE - An Algebraic Compiler for Quantum Circuit Compression QuYBE -量子电路压缩的代数编译器

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00060

Sahil Gulania, Zichang He, Bo Peng, N. Govind, Y. Alexeev

引用次数: 4

Characterizing Variability in Heterogeneous Edge Systems: A Methodology & Case Study 表征异质边缘系统的可变性:一种方法和案例研究

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00016

Hazem A. Abdelhafez, Hassan Halawa, Amr Almoallim, Amirhossein Ahmadi, K. Pattabiraman, M. Ripeanu

引用次数: 1

Congestion-free Multiflow Quantum Tree Network with Logarithmic Overhead 对数开销的无拥塞多流量子树网络

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00061

Hyeongrak Choi, M. Davis, D. Englund

引用次数: 0