Portable Energy-Aware Cluster-Based Edge Computers

Rausch, Thomas; Avasalcai, Cosmin; Dustdar, Schahram

doi:10.1109/sec.2018.00026

Cited by 19 publications

(9 citation statements)

References 31 publications

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“…For the MNIST data set, we use the learning rate of 10 −4 . For training and inferences using the MNIST data set, we have used a machine with 4 cores and 8GB memory to mimic an IoT gateway or a low-end edge server such as [41].…”

Section: Evaluation Resultsmentioning

confidence: 99%

Adaptive Deep Learning for Soft Real-Time Image Classification

Chai

Kang

2021

Technologies

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CNNs (Convolutional Neural Networks) are becoming increasingly important for real-time applications, such as image classification in traffic control, visual surveillance, and smart manufacturing. It is challenging, however, to meet timing constraints of image processing tasks using CNNs due to their complexity. Performing dynamic trade-offs between the inference accuracy and time for image data analysis in CNNs is challenging too, since we observe that more complex CNNs that take longer to run even lead to lower accuracy in many cases by evaluating hundreds of CNN models in terms of time and accuracy using two popular data sets, MNIST and CIFAR-10. To address these challenges, we propose a new approach that (1) generates CNN models and analyzes their average inference time and accuracy for image classification, (2) stores a small subset of the CNNs with monotonic time and accuracy relationships offline, and (3) efficiently selects an effective CNN expected to support the highest possible accuracy among the stored CNNs subject to the remaining time to the deadline at run time. In our extensive evaluation, we verify that the CNNs derived by our approach are more flexible and cost-efficient than two baseline approaches. We verify that our approach can effectively build a compact set of CNNs and efficiently support systematic time vs. accuracy trade-offs, if necessary, to meet the user-specified timing and accuracy requirements. Moreover, the overhead of our approach is little/acceptable in terms of latency and memory consumption.

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Section: Evaluation Resultsmentioning

confidence: 99%

Adaptive Deep Learning for Soft Real-Time Image Classification

Chai

Kang

2021

Technologies

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“…• SBC cluster: we assume the availability of a cluster of small SBCs as in [21], allowing for different components of our architecture to be deployed on dedicated SBC nodes. In particular, in the experiments presented, the full LoRaWAN stack is co-located with the LoRaWAN gateway running on a RPi 3; the frames received are then pushed to a monitor executed on an identical device.…”

Section: End-to-end Latencymentioning

confidence: 99%

Edge-Based Runtime Verification for the Internet of Things

Tsigkanos

Bersani

Frangoudis

et al. 2022

IEEE Trans. Serv. Comput.

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Complex distributed systems such as the ones induced by Internet of Things (IoT) deployments, are expected to operate in compliance to their requirements. This can be checked by inspecting events flowing throughout the system, typically originating from end-devices and reflecting arbitrary actions, changes in state or sensing. Such events typically reflect the behavior of the overall IoT system -they may indicate executions which satisfy or violate its requirements. This paper presents a service-based software architecture and technical framework supporting runtime verification for widely deployed, volatile IoT systems. At the lowest level, systems we consider are comprised of resource-constrained devices connected over wide area networks generating events. In our approach, monitors are deployed on edge components, receiving events originating from end-devices or other edge nodes. Temporal logic properties expressing desired requirements are then evaluated on each edge monitor in a runtime fashion. The system exhibits decentralization since evaluation occurs locally on edge nodes, and verdicts possibly affecting satisfaction of properties on other edge nodes are propagated accordingly. This reduces dependence on cloud infrastructures for IoT data collection and centralized processing. We illustrate how specification and runtime verification can be achieved in practice on a characteristic case study of smart parking. Finally, we demonstrate the feasibility of our design over a testbed instantiation, whereupon we evaluate performance and capacity limits of different hardware classes under monitoring workloads of varying intensity using state-of-the-art LPWAN technology.

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“…An energy aware edge computer architecture is designed to be portable and usable in the fieldwork scenarios in [51]. Based on the architecture, a high-density cluster prototype is built using the compact general-purpose commodity hardware.…”

Section: B At the Middle Edge Server Layermentioning

confidence: 99%

A Survey on Edge Computing Systems and Tools

Liu¹,

Tang²,

Li³

et al. 2019

Proc. IEEE

223

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Driven by the visions of Internet of Things and 5G communications, the edge computing systems integrate computing, storage and network resources at the edge of the network to provide computing infrastructure, enabling developers to quickly develop and deploy edge applications. Nowadays the edge computing systems have received widespread attention in both industry and academia. To explore new research opportunities and assist users in selecting suitable edge computing systems for specific applications, this survey paper provides a comprehensive overview of the existing edge computing systems and introduces representative projects. A comparison of open source tools is presented according to their applicability. Finally, we highlight energy efficiency and deep learning optimization of edge computing systems. Open issues for analyzing and designing an edge computing system are also studied in this survey. F. Liu is with the

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Portable Energy-Aware Cluster-Based Edge Computers

Cited by 19 publications

References 31 publications

Adaptive Deep Learning for Soft Real-Time Image Classification

Adaptive Deep Learning for Soft Real-Time Image Classification

Edge-Based Runtime Verification for the Internet of Things

A Survey on Edge Computing Systems and Tools

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