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

BumbleBee: Application-aware adaptation for edge-cloud orchestration BumbleBee:边缘云编排的应用程序感知改编

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00017

Hyunjong Lee, S. Noghabi, Brian D. Noble, Matthew Furlong, Landon P. Cox

{"title":"BumbleBee: Application-aware adaptation for edge-cloud orchestration","authors":"Hyunjong Lee, S. Noghabi, Brian D. Noble, Matthew Furlong, Landon P. Cox","doi":"10.1109/SEC54971.2022.00017","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00017","url":null,"abstract":"Modern developers rely on container-orchestration frameworks like Kubernetes to deploy and manage hybrid workloads that span the edge and cloud. When network conditions between the edge and cloud change unexpectedly, a workload must adapt its internal behavior. Unfortunately, container-orchestration frameworks do not offer an easy way to express, deploy, and manage adaptation strategies. As a result, fine-tuning or modifying a workload's adaptive behavior can require modifying containers built from large, complex codebases that may be maintained by separate development teams. This paper presents BumbleBee, a lightweight extension for container-orchestration frameworks that separates the concerns of application logic and adaptation logic. BumbleBee provides a simple in-network programming abstraction for making decisions about network data using application semantics. Experiments with a BumbleBee prototype show that edge ML-workloads can adapt to network variability and survive disconnections, edge stream-processing workloads can improve benchmark results between 37.8% and $boldsymbol{23mathrm{x}}$, and HLS video-streaming can reduce stalled playback by 77%.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"1 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":"125778876","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 Text Encoding for Classification Tasks 分类任务的量子文本编码

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00052

Aaranya Alexander, D. Widdows

引用次数: 1

Shepherd: Seamless Stream Processing on the Edge Shepherd:边缘的无缝流处理

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00011

B. Ramprasad, Pritish Mishra, Myles Thiessen, Hongkai Chen, Alexandre da Silva Veith, Moshe Gabel, Oana Balmau, Abelard Chow, E. de Lara

引用次数: 0

NexusEdge: Leveraging IoT Gateways for a Decentralized Edge Computing Platform NexusEdge:利用物联网网关提供分散式边缘计算平台

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00014

Nabeel Nasir, V. Sobral, Li-Pang Huang, Bradford Campbell

{"title":"NexusEdge: Leveraging IoT Gateways for a Decentralized Edge Computing Platform","authors":"Nabeel Nasir, V. Sobral, Li-Pang Huang, Bradford Campbell","doi":"10.1109/SEC54971.2022.00014","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00014","url":null,"abstract":"Edge computing enables scalability and privacy improvements for Internet of Things (IoT) systems, by shifting applications from the cloud to edge servers closer to IoT devices. Conceptually, IoT devices communicate directly with the edge, but in real-world IoT deployments often IoT gateways are needed to bridge devices and edge servers. Design decisions at this gateway layer directly contribute to the responsiveness of edge applications and scalability of the platform, yet these gateways are often overlooked and under-explored. IoT gateways have a compelling mix of features, including reasonable compute capabilities, low cost, direct contact with devices, and spatial distribution in deployments. We hypothesize that a new management layer that organizes already existing gateways can replace expensive edge servers while enabling the privacy, reliability, and performance benefits of executing IoT applications on the edge. We utilize a decentralized architecture that creates a nexus among disjoint gateways using out-of-band discovery, low-overhead abstraction layers, and runtime application scheduling. This platform supports heterogeneous devices, minimizes configuration overhead, executes applications, and provides resiliency to failure. We develop a prototype of the architecture, NexusEdge, and deploy it across several gateways and hundreds of low-power and energy-harvesting devices. When compared to Amazon's AWS IoT Greengrass, NexusEdge shows a 10x improvement in application latency, and a 2.5x reduction in network traffic, indicating better scalability and responsiveness. We demonstrate how NexusEdge supports applications without cloud support, and envision future extensions of this platform.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"595 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":"132818477","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

MARbLE: Multi-Agent Reinforcement Learning at the Edge for Digital Agriculture 数字农业边缘的多智能体强化学习

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00013

Jayson G. Boubin, Codi Burley, Peida Han, Bowen Li, Barry Porter, Christopher Stewart

{"title":"MARbLE: Multi-Agent Reinforcement Learning at the Edge for Digital Agriculture","authors":"Jayson G. Boubin, Codi Burley, Peida Han, Bowen Li, Barry Porter, Christopher Stewart","doi":"10.1109/SEC54971.2022.00013","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00013","url":null,"abstract":"Digital agriculture, hailed as the fourth great agricultural revolution, employs software-driven autonomous agents for in-field crop management. Edge computing resources deployed near crop fields support autonomous agents with substantial computational needs for tasks such as AI inference. In large fields, using multiple autonomous agents, called swarms, can speed up crop management tasks if sufficient edge resources are provisioned. However, to use swarms today, farmers and software developers craft their own standalone solutions that are either simple and ineffective or complicated and hard-to-reproduce. We present MARbLE, a platform for developing and managing swarms. MARbLE provides an easy-to-use programming paradigm that helps users build swarm workloads using multi-agent reinforcement learning. Developers supply just two functions Map() and Eval(). The platform automatically compiles and deploys swarms and continuously updates the reinforcement learning models that govern their actions. Developers can experiment with multiple swarm and edge resource configurations both in simulation and with actual in-field runs. We studied real UAV swarms conducting digital agriculture missions. We observe that swarms demanded edge computing resources in bursts; the ratio of average to peak demand was 2.9X. MARbLE uses energy-saving load balancing policies to duty cycle machines during workload demand troughs, leveraging workload patterns to save edge energy. Using MARbLE, we found that four-agent swarms with load balancing techniques sped up missions by 2.1X and reduced edge energy usage by up to 2X compared to state of the art autonomous swarms.","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":"133185699","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: 5

Privacy-Preserving and Secure Divide-and-Conquer Learning 隐私保护和安全分而治之学习

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00078

Lewis CL Brown, Qinghua Li

{"title":"Privacy-Preserving and Secure Divide-and-Conquer Learning","authors":"Lewis CL Brown, Qinghua Li","doi":"10.1109/SEC54971.2022.00078","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00078","url":null,"abstract":"The computation need of neural networks has out-paced the capabilities of many individual users whose computers, mobile devices, and other devices are relatively limited in computation power. To solve this problem, currently users need to offload the model training task to the cloud that has many computing resources. On the other hand, many devices on the edge have idling CPU cycles not used. Inspired by the successes of crowdsourcing and decentralized computing platforms such as blockchain and Web3, we propose to outsource an individual's neural network training task to edge devices, such that individuals can train their own neural network models without relying on the centralized cloud. Specifically, we design a divide-and-conquer learning framework in the edge computing environment. A user can divide the training computation of its neural network into neuron-sized computation tasks and distribute them to devices in the edge based on their available resources. The results will be returned to the user and aggregated in an iterative process to obtain the final neural network model. To protect the privacy of the user's data and model, shuffling is done to both the data and the neural network model before the computation task is distributed to edge nodes. Security against misbehaving edge nodes can also be provisioned by redundancy in task assignment.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"12 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":"115101767","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: Reliable On-Ramp Merging via Multimodal Reinforcement Learning 海报:通过多模态强化学习实现可靠的入口匝道合并

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00043

Gaurav R. Bagwe, Jian Li, Xiaoheng Deng, Xiaoyong Yuan, Lan Zhang

引用次数: 0

Poster: Fine-grained Control Plane Container Profiler for MEC 海报:MEC的细粒度控制平面容器分析器

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00037

K. Hsu, Ketan Bhardwaj, Ada Gavrilovska

{"title":"Poster: Fine-grained Control Plane Container Profiler for MEC","authors":"K. Hsu, Ketan Bhardwaj, Ada Gavrilovska","doi":"10.1109/SEC54971.2022.00037","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00037","url":null,"abstract":"Today, the edge computing system stack is built by leveraging the current cloud technologies, such as the containers, Kubernetes, etc., because, like the cloud, the edge is multi-tenant infrastructure. However, edge applications have more latency-critical SLAs and the infrastructure itself resource-constrained. That puts additional burdens on its control plane, which are not addressed by the cloud control plain tools. At the edge, if deployments aren't specified accurately, edge providers will face the dilemma between the waste of resource due to overcommitment vs. SLA violations. However, we observed that it is not feasible to rely on the existing monitoring tools, designed for the cloud, to glean that information from workloads with varying use of resources, at the needed fine granularity. Trying to do that with brute-forcing cloud solutions turns out to be extremely demanding on the resources allocated to the control plane. We present a new control plane tool, Colibri, aimed at addressing those conflicting requirements. Colibri can be dispatched dynamically, when needed, and enables characterization of containers deployed using Kubernetes across CPU, memory and network resource usage patterns at millisecond scale. The preliminary results demonstrate the effectiveness of out approach in reducing SLA violations by up to 98% for representative edge workloads.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"1 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":"129648949","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: Enabling High-Fidelity and Real-Time Mobility Digital Twin with Edge Computing 海报:利用边缘计算实现高保真和实时移动数字孪生

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00031

Yueyang Liu, Haoxin Wang, Zhipeng Cai, Dawei Chen, Kyungtae Han

引用次数: 1

Gemini: a Real-time Video Analytics System with Dual Computing Resource Control 双子:具有双计算资源控制的实时视频分析系统

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-12-01 DOI: 10.1109/SEC54971.2022.00020

Rui Lu, Chuang Hu, Dan Wang, Jin Zhang

{"title":"Gemini: a Real-time Video Analytics System with Dual Computing Resource Control","authors":"Rui Lu, Chuang Hu, Dan Wang, Jin Zhang","doi":"10.1109/SEC54971.2022.00020","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00020","url":null,"abstract":"Edge-side real-time video analytics systems recognize spatial or temporal events (e.g., vehicle counting) in a video stream. To meet the delay requirement, existing systems in smart edge cameras conduct video preprocessing to filter out unnecessary frames and model inference using appropriately selected neural network (NN) models. Video preprocessing is instruction-intensive computing (IIC) and executed by the CPU of the edge camera, and model inference is data-intensive computing (DIC) and executed by the GPU of the edge camera. In this paper, we show that the analytics accuracy of existing systems can largely vary in fields. The root cause is that video analytics applications have different contents, which result in dynamic IIC and DIC workloads. Unfortunately, intelligent cameras in fields have fixed CPU and GPU resources and cannot effectively adapt to workload dynamics. We develop Gemini, a new real-time video analytics system enhanced by a dual-image FPGA. The newly developed dual-image FPGAs can be pre-configured with two FPGA images with a key advantage of negligible image switching time. We thus pre-configure one CPU image and one GPU image and elastically multiplex the dual CPU-GPU resources in the time dimension. The Gemini system design requires both hardware and software revisions. We overcame a challenge that the application development on different dual-image FPGAs is hardware-dependent. We develop a new abstraction of hardware functions to make the Gemini system hardware-agnostic. It is also a challenge to adapt to the dynamic workloads and optimize video analytics accuracy. We develop a bandit learning approach to capture content dynamics and conduct dual computing resource control. We implement Gemini and show that Gemini can improve the analytics accuracy to 90.35 %. We further evaluate Gemini by a case study where we use Gemini to support an intrusion detection application, and Gemini shows consistent high analytics accuracy.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"48 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":"126762653","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