An official website of the United States government
The rapid growth in technology and wide use of internet has increased smart applications such as intelligent transportation control system, and Internet of Things, which heavily rely on an efficient and reliable connectivity network. To overcome high bandwidth work load on the network, as well as minimize latency for real-time applications, the computation can be moved from the central cloud to a distributed edge cloud. The edge computing benefits various smart applications that uses distributed network for data analytics and services. Different from the existing cloud management solutions, edge computing needs to move cloud management services towards distributed heterogeneous edge nodes for multi-tenant user applications. However, existing cloud management services do not offer remote deployment of multi-tenant user applications on the cloud of edge nodes. In this paper, we propose a practical edge cloud software framework for deploying multi-tenant distributed smart applications. Having multiple distributed end nodes, auto discovery of all active end nodes is required for deploying multi-tenant user applications. However, existing cloud solutions require either private network or fixed IP address, which is not achievable for the distributed edge nodes. Most of the edge nodes connected through the public internet without fixed IP, and some of them even connect through IEEE 802.15 based sensor networks. We propose to build a software platform to manage the distributed edge nodes as well as support services to deploy and launch isolated, multi-tenant user applications through a lightweight container. We propose an architectural solution to remotely access edge cloud management services through intermittent internet connections. We open sourced our whole set of software solutions, and analyzed the major performance metrics of the edge cloud platform.
more »
« less
Distributed Edge Cloud R-CNN for Real Time Object Detection
Cloud computing infrastructures have become the de-facto platform for data driven machine learning applications. However, these centralized models of computing are unqualified for dispersed high volume real-time edge data intensive applications such as real time object detection, where video streams may be captured at multiple geographical locations. While many recent advancements in object detection have been made using Convolutional Neural Networks but these performance improvements only focus on a single contiguous object detection model. In this paper, we propose a distributed Edge-Cloud R-CNN by splitting the model into components and dynamically distributing these components in the cloud for optimal performance for real time object detection. As a proof of concept, we evaluate the performance of the proposed system on a distributed computing platform encompasses cloud servers and edge embedded devices for real-time object detection on video streams.
more »
« less
- Award ID(s):
- 1736209
- PAR ID:
- 10066892
- Date Published:
- Journal Name:
- World Automation Congress proceedings
- ISSN:
- 2154-4824
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Edge and Fog computing paradigms are used to process big data generated by the increasing number of IoT devices. These paradigms have enabled cities to become smarter in various aspects via real-time data-driven applications. While these have addressed some flaws of cloud computing some challenges remain particularly in terms of privacy and security. We create a testbed based on a distributed processing platform called the Information flow of Things (IFoT) middleware. We briefly describe a decentralized traffic speed query and routing service implemented on this framework testbed. We configure the testbed to test counter measure systems that aim to address the security challenges faced by prior paradigms. Using this testbed, we investigate a novel decentralized anomaly detection approach for time-sensitive distributed smart transportation systemsmore » « less
-
The density and complexity of urban environments present significant challenges for autonomous vehicles. Moreover, ensuring pedestrians’ safety and protecting personal privacy are crucial considerations in these environments. Smart city intersections and AI-powered traffic management systems will be essential for addressing these challenges. Therefore, our research focuses on creating an experimental framework for the design of applications that support the secure and efficient management of traffic intersections in urban areas. We integrated two cameras (street-level and bird’s eye view), both viewing an intersection, and a programmable edge computing node, deployed within the COSMOS testbed in New York City, with a central management platform provided by Kentyou. We designed a pipeline to collect and analyze the video streams from both cameras and obtain real-time traffic/pedestrian-related information to support smart city applications. The obtained information from both cameras is merged, and the results are sent to a dedicated dashboard for real-time visualization and further assessment (e.g., accident prevention). The process does not require sending the raw videos in order to avoid violating pedestrians’ privacy. In this demo, we present the designed video analytic pipelines and their integration with Kentyou central management platform. Index Terms—object detection and tracking, camera networks, smart intersection, real-time visualizationmore » « less
-
Recent advances in Visual Language Models (VLMs) have significantly enhanced video analytics. VLMs capture complex visual and textual connections. While Convolutional Neural Networks (CNNs) excel in spatial pattern recognition, VLMs provide a global context, making them ideal for tasks like complex incidents and anomaly detection. However, VLMs are much more computationally intensive, posing challenges for large-scale and real-time applications. This paper introduces EdgeCloudAI, a scalable system integrating VLMs and CNNs through edge-cloud computing. Edge- CloudAI performs initial video processing (e.g., CNN) on edge devices and offloads deeper analysis (e.g., VLM) to the cloud, optimizing resource use and reducing latency. We have deployed EdgeCloudAI on the NSF COSMOS testbed in NYC. In this demo, we will demonstrate EdgeCloudAI’s performance in detecting user-defined incidents in real-time.more » « less
-
Real-time applications such as autonomous and connected cars, surveillance, and online learning applications have to train on streaming data. They require low-latency, high throughput machine learning (ML) functions resident in the network and in the cloud to perform learning and inference. NFV on edge cloud platforms can provide support for these applications by having heterogeneous computing including GPUs and other accelerators to offload ML-related computation. GPUs provide the necessary speedup for performing learning and inference to meet the needs of these latency sensitive real-time applications. Supporting ML inference and learning efficiently for streaming data in NFV platforms has several challenges. In this paper, we present a framework, NetML, that runs existing ML applications on an heterogeneous NFV platform that includes both CPUs and GPUs. NetML efficiently transfers the appropriate packet payload to the GPU, minimizing overheads, avoiding locks, and avoiding CPU-based data copies. Additionally, NetML minimizes latency by maximizing overlap between the data movement and GPU computation. We evaluate the efficiency of our approach for training and inference using popular object detection algorithms on our platform. NetML reduces the latency for inferring images by more than 20% and increases the training throughput by 30% while reducing CPU utilization compared to other state-of-the-art alternatives.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
