LAVEA: Latency-Aware Video Analytics on Edge Computing Platform

Yi, Shanhe; Hao, Zijiang; Zhang, Qingyang; Zhang, Quan; Shi, Weisong; Li, Qun

doi:10.1109/icdcs.2017.182

Cited by 106 publications

(137 citation statements)

References 6 publications

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“…Among the multi-levels works, the most common is to use resources located both at the edge and at the cloud level. This is the case in the works by Liu et al [58], Borylo et al [65], Valancius et al [45], Yi et al [76], Wang et al [77], and Singh et al [44]. Specifically, Skarlat et al [50] and Bittencourt et al [72] favor using edge resources over cloud resources.…”

Section: Multi-levelmentioning

confidence: 95%

“…Instead of using VMs, Yi et al [76] adopt lightweight OS-level virtualization and a container technique, arguing that resource isolation can be provided at a much lower cost using OS-level virtualization. They also pinpoint that the creation and destruction of container instances is much faster and thus enable the deployment of an edge computing platform with minimal efforts.…”

Section: Computation and Communicationmentioning

confidence: 99%

“…Gu et al [66] do the same and then use heuristics. Using a different approach, Yi et al [76] first formulate a mixed integer non-linear programming problem but then relax the integer constraints and use sequential quadratic programming for solving.…”

Section: Resource Optimizationmentioning

confidence: 99%

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A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Toczé

Nadjm‐Tehrani

2018

Wireless Communications and Mobile Computing

105

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Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices at the edge of the current network. To achieve higher performance in this new paradigm, one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis are used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource and less extensive towards the estimation, discovery, and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of nonfunctional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.

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Section: Multi-levelmentioning

confidence: 95%

Section: Computation and Communicationmentioning

confidence: 99%

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A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Toczé

Nadjm‐Tehrani

2018

Wireless Communications and Mobile Computing

105

View full text Add to dashboard Cite

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“…To efficiently handle large-scale video datasets and improve the performance of VS systems, researchers have attempted to deploy VS systems in distributed computing, cloud computing, and edge computing environments [12,17,22]. In [12], Kavalionak et al introduced a distributed protocol for a face recognition system, which exploits the computing power of the monitoring devices to perform person recognition.…”

Section: Related Workmentioning

confidence: 99%

“…In [12], Kavalionak et al introduced a distributed protocol for a face recognition system, which exploits the computing power of the monitoring devices to perform person recognition. Yi et al built a video analytics system on an EC platform that offloads computing tasks between monitoring devices and edge nodes and provides low-latency video analysis [17]. In [23], Park et al proposed a scalable architecture for an automatic surveillance system using edge computing to reduce cloud resource consumptions and wireless network limitations.…”

Section: Related Workmentioning

confidence: 99%

Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing

Chen

Deng

et al. 2024

IEEE Trans. Ind. Inf.

184

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In this paper, we propose a Distributed Intelligent Video Surveillance (DIVS) system using Deep Learning (DL) algorithms and deploy it in an edge computing environment. We establish a multi-layer edge computing architecture and a distributed DL training model for the DIVS system. The DIVS system can migrate computing workloads from the network center to network edges to reduce huge network communication overhead and provide low-latency and accurate video analysis solutions. We implement the proposed DIVS system and address the problems of parallel training, model synchronization, and workload balancing. Task-level parallel and model-level parallel training methods are proposed to further accelerate the video analysis process. In addition, we propose a model parameter updating method to achieve model synchronization of the global DL model in a distributed EC environment. Moreover, a dynamic data migration approach is proposed to address the imbalance of workload and computational power of edge nodes. Experimental results showed that the EC architecture can provide elastic and scalable computing power, and the proposed DIVS system can efficiently handle video surveillance and analysis tasks.

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Edge computing in smart health care systems: Review, challenges, and research directions

Hartmann

Hashmi

Imran

2019

Trans Emerging Tel Tech

154

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Today, patients are demanding a newer and more sophisticated healthcare system, one that is more personalized and matches the speed of modern life. For the latency and energy efficiency requirements to be met for a real-time collection and analysis of health data, an edge computing environment is the answer, combined with 5G speeds and modern computing techniques. Previous healthcare surveys have focused on new fog architecture and sensor types, which leaves untouched the aspect of optimal computing techniques, such as encryption, authentication, and classification that are used on the devices deployed in an edge computing architecture. This paper aims first to survey the current and emerging edge computing architectures and techniques for healthcare applications, as well as identify requirements and challenges of devices for various use cases. Edge computing applications primarily focus on the classification of health data involving vital sign monitoring and fall detection. Other low latency applications perform specific symptom monitoring for diseases, such as gait abnormalities in Parkinson's disease patients. We also present our exhaustive review on edge computing data operations that include transmission, encryption, authentication, classification, reduction and prediction. Even with these advantages, edge computing has some associated challenges, including requirements for sophisticated privacy and data reduction methods to allow comparable performance to their Cloud based counterparts, but with lower computational complexity. Future research directions in edge computing for healthcare have been identified to offer a higher quality of life for users if addressed.

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LAVEA: Latency-Aware Video Analytics on Edge Computing Platform

Cited by 106 publications

References 6 publications

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing

Edge computing in smart health care systems: Review, challenges, and research directions

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