Proceedings of the 4th ACM/IEEE Symposium on Edge Computing最新文献

Managing edge resources for fully autonomous aerial systems 管理完全自主航空系统的边缘资源

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing Pub Date : 2019-11-07 DOI: 10.1145/3318216.3363306

Jayson G. Boubin, Naveen T. R. Babu, Christopher Stewart, J. Chumley, Shiqi Zhang

{"title":"Managing edge resources for fully autonomous aerial systems","authors":"Jayson G. Boubin, Naveen T. R. Babu, Christopher Stewart, J. Chumley, Shiqi Zhang","doi":"10.1145/3318216.3363306","DOIUrl":"https://doi.org/10.1145/3318216.3363306","url":null,"abstract":"Fully autonomous aerial systems (FAAS) fly complex missions guided wholly by software. If users choose software, compute hardware and aircraft well, FAAS can complete missions faster and safer than unmanned aerial systems piloted by humans. On the other hand, poorly managed edge resources slow down missions, waste energy and inflate costs. This paper presents a model-driven approach to manage FAAS. We fly real FAAS missions, profile compute and aircraft resource usage and model expected demands. Naive profiling approaches use traces from previous flights to infer resource usage. However, edge resources can affect where FAAS fly and which data they sense. Usage profiles can diverge greatly across edge management policies. Instead of using traces, we characterize whole flight areas to accurately model resource usage for any flight path. We combine expected resource demands to model mission throughput, i.e., missions completed per fully charged battery. We validated our model by creating FAAS, measuring mission throughput across many system settings. Our FAAS benchmarks, released through our open source FAAS suite SoftwarePilot, execute realistic missions: autonomous photography, search and rescue, and agricultural scouting using well-known software. Our model predicted throughput with 4% error across mission, software and hardware settings. Competing approaches yielded 10--24% error. We used our SoftwarePilot benchmarks to study (1) GPU acceleration, scale up, and scale out, (2) onboard, edge and cloud computing, (3) energy and monetary budgets, and (4) software driven GPU management. We found that model-driven management can boost mission throughput by 10X and reduce costs by 87%.","PeriodicalId":406118,"journal":{"name":"Proceedings of the 4th ACM/IEEE Symposium on Edge Computing","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124398612","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: 41

Informer

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing Pub Date : 2019-11-07 DOI: 10.1145/3318216.3363375

Jiyu Chen, Heqing Huang, Hao Chen

{"title":"Informer","authors":"Jiyu Chen, Heqing Huang, Hao Chen","doi":"10.1145/3318216.3363375","DOIUrl":"https://doi.org/10.1145/3318216.3363375","url":null,"abstract":"Containerized microservices have been widely deployed in industry. Meanwhile, security issues also arise. Many security enhancement mechanisms for containerized microservices require predefined rules and policies. However, it is challenging when it comes to thousands of microservices and a massive amount of real-time unstructured data. Hence, automatic policy generation becomes indispensable. In this paper, we focus on the automatic solution for the security problem: irregular traffic detection for RPCs. We propose Informer, which is a two-phase machine learning framework to track the traffic of each RPC and report anomalous points automatically. Firstly, we identify RPC chain patterns by density-based clustering techniques and build a graph for each critical pattern. Next, we solve the irregular RPC traffic detection problem as a prediction problem for time-series of attributed graphs by leveraging spatial-temporal graph convolution networks. Since the framework builds multiple models and makes individual predictions for each RPC chain pattern, it can be efficiently updated upon legitimate changes in any of the graphs. In evaluations, we applied Informer to a dataset containing more than 7 billion lines of raw RPC logs sampled from an large Kubernetes system for two weeks. We provide two case studies of detected real-world threats. As a result, our framework found fine-grained RPC chain patterns and accurately captured the anomalies in a dynamic and complicated microservice production scenario, which demonstrates the effectiveness of Informer.","PeriodicalId":406118,"journal":{"name":"Proceedings of the 4th ACM/IEEE Symposium on Edge Computing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125267906","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: 34

A SDN-based network layer for edge computing: poster 基于sdn的边缘计算网络层:海报

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing Pub Date : 2019-11-07 DOI: 10.1145/3318216.3363333

An Wang, Zili Zha, Yang Guo, Songqing Chen

引用次数: 2

An efficient edge-assisted mobile system for video photorealistic style transfer: poster abstract 一个有效的边缘辅助移动系统视频逼真风格转移:海报摘要

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing Pub Date : 2019-11-07 DOI: 10.1145/3318216.3364545

Ang Li, Chunpeng Wu, Yiran Chen, Bin Ni

{"title":"An efficient edge-assisted mobile system for video photorealistic style transfer: poster abstract","authors":"Ang Li, Chunpeng Wu, Yiran Chen, Bin Ni","doi":"10.1145/3318216.3364545","DOIUrl":"https://doi.org/10.1145/3318216.3364545","url":null,"abstract":"In the past decade, convolutional neural networks (CNNs) have achieved great practical success in image transformation tasks, including style transfer, semantic segmentation, etc. CNN-based style transfer, which denotes transforming an image into a desired output image according to a user-specified style image, is one of the most popular techniques in image transformation. It has led to to many successful industrial applications with significant commercial impacts, such as Prisma and DeepArt. Figure 1 shows the general workflow of the CNN-based style transfer. Given a content image and a user-specified style image, the content features and style features can be extracted using a pre-trained CNN, and then be merged to generate the stylized image. The CNN model is trained for generating a stylized image that has similar content features as the content image's and similar style features as the style image's. In this example, we can see the content image is captured at a lake in the daytime, while the style image is another similar scene captured at dusk. After performing style transfer, the content image is successfully transformed to the dusky scene while keeping the content unchanged as the content image.","PeriodicalId":406118,"journal":{"name":"Proceedings of the 4th ACM/IEEE Symposium on Edge Computing","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114335261","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

Collaborative learning between cloud and end devices: an empirical study on location prediction 云和终端设备之间的协同学习:位置预测的实证研究

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing Pub Date : 2019-11-07 DOI: 10.1145/3318216.3363304

Yan Lu, Yuanchao Shu, Xu Tan, Yunxin Liu, Mengyu Zhou, Qi Chen, Dan Pei

{"title":"Collaborative learning between cloud and end devices: an empirical study on location prediction","authors":"Yan Lu, Yuanchao Shu, Xu Tan, Yunxin Liu, Mengyu Zhou, Qi Chen, Dan Pei","doi":"10.1145/3318216.3363304","DOIUrl":"https://doi.org/10.1145/3318216.3363304","url":null,"abstract":"Over the years, numerous learning methods have been put forward to model and predict different user behaviors on end devices (e.g., ads click, location change, app launch). While the learn-then-deploy approaches achieve promising results in many scenarios, data heterogeneity and variability throw impediment in the way of deploying pre-learned models to a large cluster of end devices. On the other hand, learning on devices like smartphones suffers from limited data, computing power and energy budget. This paper proposes Colla, a collaborative learning approach for behavior prediction that allows cloud and devices to learn collectively and continuously. Colla finds a middle ground to build tailored model for each device, leveraging local data and computation resources to update the model, while at the same time exploits cloud to aggregate and transfer device-learned knowledge across the network to solve the cold-start problem and prevent over-fitting. We fully implemented Colla with a multi-feature RNN model on both smartphones and in cloud, and applied it to predict user locations. Evaluation results based on large-scale real data show that compared with training using centralized data, Colla improves prediction accuracy by 21%. Our experiments also validate the efficiency of Colla, showing that one overnight training on a commodity smartphone can process one-year data from a typical smartphone, at the cost of 2000mWh and few hundreds KB communication overhead.","PeriodicalId":406118,"journal":{"name":"Proceedings of the 4th ACM/IEEE Symposium on Edge Computing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121490979","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: 16

Cost-effective microservice scaling at edge: poster 具有成本效益的边缘微服务扩展:海报

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing Pub Date : 2019-11-07 DOI: 10.1145/3318216.3363330

Amit Samanta, Yong Li

引用次数: 3

Cost-aware cloudlet placement in edge computing systems 边缘计算系统中具有成本意识的云布局

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing Pub Date : 2019-11-07 DOI: 10.1145/3318216.3363369

Dixit Bhatta, Lena Mashayekhy

引用次数: 1

Performance evaluation of deception system for deceiving cyber adversaries in adaptive virtualized wireless networks 自适应虚拟化无线网络欺骗系统欺骗对手性能评估

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing Pub Date : 2019-11-07 DOI: 10.1145/3318216.3363377

D. Rawat, Naveen Naik Sapavath, Min Song

引用次数: 4

Linearize, predict and place: minimizing the makespan for edge-based stream processing of directed acyclic graphs 线性化，预测和放置:最小化有向无环图的基于边缘的流处理的最大跨度

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing Pub Date : 2019-11-07 DOI: 10.1145/3318216.3363315

S. Khare, Hongyang Sun, Julien Gascon-Samson, Kaiwen Zhang, A. Gokhale, Yogesh D. Barve, Anirban Bhattacharjee, X. Koutsoukos

{"title":"Linearize, predict and place: minimizing the makespan for edge-based stream processing of directed acyclic graphs","authors":"S. Khare, Hongyang Sun, Julien Gascon-Samson, Kaiwen Zhang, A. Gokhale, Yogesh D. Barve, Anirban Bhattacharjee, X. Koutsoukos","doi":"10.1145/3318216.3363315","DOIUrl":"https://doi.org/10.1145/3318216.3363315","url":null,"abstract":"Many IoT applications found in cyber-physical systems, such as smart grids, must take control actions in response to critical events, such as supply-demand mismatch, which requires low-latency processing of streaming data for rapid event detection and anomaly remediation. These streaming applications generally take the form of directed acyclic graphs (DAGs), where vertices represent operators and edges represent the flow of data between these operators. Edge computing has recently attracted significant attention as a means to readily meet the requirements of latency-critical IoT applications due to its ability to provide low-latency processing near the source of data. To accrue the benefits of edge computing, the constituent operators of these applications must be placed in a manner that intelligently trades-off inter-operator communication costs with the cost of interference incurred due to co-location of operators on the same resource-constrained edge devices. To address these challenges and to substantially simplify the placement problem for DAGs of arbitrary sizes and topologies, we present an algorithm that first transforms any arbitrary stream processing DAG into an approximate set of linear chains. Subsequently, a data-driven latency prediction model for co-located linear chains is used to inform the placement of operators such that the makespan, defined as the maximum latency of all paths in the DAG, is minimized. We empirically evaluate our algorithm using a variety of DAG placement scenarios on a Beagle Bone cluster, which is representative of an edge computing environment.","PeriodicalId":406118,"journal":{"name":"Proceedings of the 4th ACM/IEEE Symposium on Edge Computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122108095","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: 25

Deep learning-based C/U plane separation architecture for automotive edge computing 基于深度学习的汽车边缘计算C/U平面分离架构

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing Pub Date : 2019-11-07 DOI: 10.1145/3318216.3363321

P. Du, A. Nakao, Zhaoxia Sun, L. Zhong, R. Onishi

引用次数: 2