Transparent AR Processing Acceleration at the Edge

Trinelli, Marco; Gallo, Massimo; Rifai, Myriana; Pianese, Fabio

doi:10.1145/3301418.3313942

Cited by 6 publications

(15 citation statements)

References 13 publications

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“…adding models into Docker images [45]). Serving was added to 'Data transformation and serving'-FA based on an another prototype, where a web proxy served end users with augmented streaming video [32]. Processing was added to comprise any general processing (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…When the location of architectural elements is analysed in detail, it can be seen that Data transformation and Serving functionality was executed in the edge server (transformation and serving of end users with augmented video streaming content [32]). Models were trained mainly in private/public clouds [36,37,39] or in edge devices [35,46] while the results were inferred from models in all environments [27,28,30,31,45].…”

Section: Discussionmentioning

confidence: 99%

“…An exception was clustering a subset of images on client devices (machine learning in-device) [41]. Streaming data was analysed and inferred in edge [29,32,34] and in-device [29,34] environments. Models were experimented in in-device environments [28].…”

Section: Discussionmentioning

confidence: 99%

“…Subsequently, models were distributed either from edge [27], from private cloud [31], or from end user devices [28]. Finally, models were loaded to memory in mobile devices [27] or in edge servers [28,32]. Figure 5 provides a vision how development and deployment of models may affect AIOps [25] and application SW development processes at the business layer.…”

Section: Discussionmentioning

confidence: 99%

“…Sometimes models need to be designed or compressed for execution on resource-constrained devices [8]. For example, experiments with augmented reality enabling video processing framework [32] indicated that Raspberry Pi couldn't achieve a satisfying performance without optimisation, as only ~ 1 frame per second (FPS) could be processed. Smart execution of big Yolo [33] in an edge device and tiny Yolo in a mobile device led to improved performance in object recognition of video streams [34].…”

Section: Related Workmentioning

confidence: 99%

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Extending reference architecture of big data systems towards machine learning in edge computing environments

Pääkkönen

Pakkala

2020

J Big Data

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IntroductionMany big data systems have been developed and realised to provide end user services (Netflix, Facebook, Twitter, LinkedIn etc.). Also, underlying architectures and technologies of the enabling systems have been published [1-3], and RAs have been designed and proposed [4][5][6]. Edge/5G computing is an emerging technological field [7], and the first products are being shipped to the markets. However, the utilisation of machine learning (ML) as part of the edge computing infrastructure is still an area for further research [8]. Particularly, it should be understood, how data is collected, and how models are Abstract Background: Augmented reality, computer vision and other (e.g. network functions, Internet-of-Things (IoT)) use cases can be realised in edge computing environments with machine learning (ML) techniques. For realisation of the use cases, it has to be understood how data is collected, stored, processed, analysed, and visualised in big data systems. In order to provide services with low latency for end users, often utilisation of ML techniques has to be optimized. Also, software/service developers have to understand, how to develop and deploy ML models in edge computing environments. Therefore, architecture design of big data systems to edge computing environments may be challenging. Findings:The contribution of this paper is reference architecture (RA) design of a big data system utilising ML techniques in edge computing environments. An earlier version of the RA has been extended based on 16 realised implementation architectures, which have been developed to edge/distributed computing environments. Also, deployment of architectural elements in different environments is described. Finally, a system view is provided of the software engineering aspects of ML model development and deployment. Conclusions:The presented RA may facilitate concrete architecture design of use cases in edge computing environments. The value of RAs is reduction of development and maintenance costs of systems, reduction of risks, and facilitation of communication between different stakeholders.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Extending reference architecture of big data systems towards machine learning in edge computing environments

Pääkkönen

Pakkala

2020

J Big Data

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What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

et al. 2019

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Edge computing brings computing and storage resources closer to (mobile) end users and data sources, thus bypassing expensive and slow links to distant cloud computing infrastructures. Often leveraged opportunistically, these heterogeneous resources can be used to offload data and computations, enabling upcoming demanding applications such as augmented reality and autonomous driving. Research in this direction has addressed various challenges, from architectural concerns to runtime optimizations. As of today, however, we lack a widespread availability of edge computing-partly because it remains unclear which of the promised benefits of edge computing are relevant for what types of applications. This article provides a comprehensive snapshot of the current edge computing landscape, with a focus on the application perspective. We outline the characteristics of edge computing and its postulated benefits and drawbacks. To understand the functional composition of applications, we first define common application components that are relevant w.r.t. edge computing. We then present a classification of proposed use cases and analyze them according to their expected benefits from edge computing and which components they use. Furthermore, we illustrate existing products and industry solutions that have recently surfaced and outline future research challenges. INDEX TERMS Edge computing, heterogeneous networks, next generation networking mobile applications, Internet of Things, ubiquitous computing.

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Fog Computing for Augmented Reality: Trends, Challenges and Opportunities

Salman

Sitompul

Papadopoulos

et al. 2020

2020 IEEE International Conference on Fog Computing (ICFC)

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Augmented reality applications are computationally intensive and have latency requirements in the range of 15-20 milliseconds. Fog computing addresses these requirements by providing on-demand computing capacity and lower latency by bringing the computational resources closer to the augmented reality devices. In this paper, we reviewed papers providing custom solutions for augmented reality using the fog architecture and identified that the ongoing research trends towards balancing quality-of-experience, energy, and latency for both single and collaborative multi-device augmented reality applications. Furthermore, some works also focus on providing architectures for fog-based augmented reality systems and also on the training of machine learning algorithms in the fog layers to improve user experience. Based on these findings, we provide some challenges and research directions that can facilitate the adoption of fog-based augmented reality systems.

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Transparent AR Processing Acceleration at the Edge

Cited by 6 publications

References 13 publications

Extending reference architecture of big data systems towards machine learning in edge computing environments

Extending reference architecture of big data systems towards machine learning in edge computing environments

What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

Fog Computing for Augmented Reality: Trends, Challenges and Opportunities

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