OneEdge

Saurez, Enrique; Gupta, Harshit; Daglis, Alexandros; Ramachandran, Umakishore

doi:10.1145/3472883.3487008

Cited by 11 publications

(3 citation statements)

References 18 publications

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“…Saurez et al. introduced a task graph partition offloading algorithm considering device capabilities [17], and Lv et al. applied Q‐learning for efficient microservice container scheduling to lower resource variance and overhead [18].…”

Section: Related Workmentioning

confidence: 99%

Provisioning edge computing services in urban areas through the chaining of unmanned aerial vehicles: A genetic approach

Tang,

Huang,

Chang

2024

Electronics Letters

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Edge computing has been proved an efficient approach to provisioning computation offloading service to vehicles on road through Road‐Side Units (RSUs). However, the traffic volume on road is highly dynamic, while RSU‐based edge servers are static in terms of geographical location and computation capacity. To address this problem, this paper proposes a mobile edge server placement strategy using cruising UAVs along the roads based on the genetic algorithm. A mathematical model is first built to characterize the deployment cost of these UAV‐mounted servers and their routes. Next, a heuristic UAV‐mounted edge server deployment scheme based on K‐medoid clustering and genetic algorithms is designed. Experimental results verify that the proposed UAV deployment scheme satisfies the offloading demand of IoV nodes while reducing the total deployment cost by 17.05–48.94% compared with existing popular approaches.

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Section: Related Workmentioning

confidence: 99%

Provisioning edge computing services in urban areas through the chaining of unmanned aerial vehicles: A genetic approach

Tang,

Huang,

Chang

2024

Electronics Letters

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“…One approach to designing control planes for edge video analytics is to use industry standard distributed systems and adapt it to the edge such as in the open source K3s [51] and KubeEdge [100] projects. In contrast, instead of retrofitting Kubernetes, which is optimized for cloud-based, throughput-focused applications, and has a centralized control plane design, the OneEdge [84] project proposes a control plane that enables autonomous scheduling at individual edge sites without the need for central coordination. This is particularly useful for applications that largely operate independently.…”

Section: Technique: Distributed Hierarchical Architecturementioning

confidence: 99%

Internet-of-Things Edge Computing Systems for Streaming Video Analytics: Trails Behind and the Paths Ahead

Ravindran

2023

IoT

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The falling cost of IoT cameras, the advancement of AI-based computer vision algorithms, and powerful hardware accelerators for deep learning have enabled the widespread deployment of surveillance cameras with the ability to automatically analyze streaming video feeds to detect events of interest. While streaming video analytics is currently largely performed in the cloud, edge computing has emerged as a pivotal component due to its advantages of low latency, reduced bandwidth, and enhanced privacy. However, a distinct gap persists between state-of-the-art computer vision algorithms and the successful practical implementation of edge-based streaming video analytics systems. This paper presents a comprehensive review of more than 30 research papers published over the last 6 years on IoT edge streaming video analytics (IE-SVA) systems. The papers are analyzed across 17 distinct dimensions. Unlike prior reviews, we examine each system holistically, identifying their strengths and weaknesses in diverse implementations. Our findings suggest that certain critical topics necessary for the practical realization of IE-SVA systems are not sufficiently addressed in current research. Based on these observations, we propose research trajectories across short-, medium-, and long-term horizons. Additionally, we explore trending topics in other computing areas that can significantly impact the evolution of IE-SVA systems.

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“…In addition to addressing adaptability issues, various algorithms have been proposed to efficiently schedule tasks and minimize communication costs in dynamic computational resource provisioning scenarios. Saurez et al introduced a task graph partition offloading algorithm that takes into account end device capabilities [17], while Lv et al applied Q-learning techniques to schedule microservice containers, aiming to reduce resource variance and communication overhead [18].…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aerial Vehicle-Mounted Edge Server Deployment for Internet of Vehicles

Tang,

Huang,

Chang

2024

Preprint

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Edge computing has been proved an efficient approach to provisioning computation offloading service to vehicles on road through Road-Side Units (RSUs). However, the traffic volume on road is highly dynamic, while RSU-based edge servers are static in terms of geographical location and computation capacity. To address this problem, this paper proposes a mobile edge server placement strategy using cruising UAVs along the roads based on the genetic algorithm. We first build a mathematical model to characterize the deployment cost of these UAV-mounted servers and their routes. Next, we design a heuristic UAV-mounted edge server deployment scheme based on K-medoid clustering and genetic algorithms. Experimental results verify that our proposed UAV deployment scheme satisfies the offloading demand of IoV nodes while reducing the total deployment cost by 17.05% to 48.94% compared with existing popular approaches.

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OneEdge

Cited by 11 publications

References 18 publications

Provisioning edge computing services in urban areas through the chaining of unmanned aerial vehicles: A genetic approach

Provisioning edge computing services in urban areas through the chaining of unmanned aerial vehicles: A genetic approach

Internet-of-Things Edge Computing Systems for Streaming Video Analytics: Trails Behind and the Paths Ahead

Unmanned Aerial Vehicle-Mounted Edge Server Deployment for Internet of Vehicles

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