A Survey of Architecture, Framework and Algorithms for Resource Management in Edge Computing

Premkumar, S.; Sigappi, A. N.

doi:10.4108/eai.23-12-2020.167788

Cited by 6 publications

(7 citation statements)

References 77 publications

(95 reference statements)

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“…Premkumar and Sigappi 26 reviewed the existing works in the literature on the topics of architectures, frameworks, and algorithms for resource management. The study presents a narrower focus on the edge computing paradigm in IoT.…”

Section: A Brief Discussion and Summary Of Related Workmentioning

confidence: 99%

QoS‐aware IoT networks and protocols: A comprehensive survey

Dilek

Irgan

Guzel

et al. 2022

Int J Communication

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Summary The vast number of smart cloud applications that communicate with the “things” over a variety of physical networks and communication protocols contribute to the rise of complexity in Internet of Things (IoT) systems. The diversity of end‐user requirements related to the volume of generated data, its variety, and the velocity of its transmission makes quality of service (QoS) provisioning even more crucial and challenging in IoT. This paper provides a comprehensive and up‐to‐date survey of QoS support in IoT networks and communication protocols. An analysis of the QoS‐aware IoT architectures, layer‐dependent QoS metrics, and network resource optimization methods utilized in IoT systems are given. The limitations of the current state‐of‐the‐art studies for efficient delivery of QoS metrics are discussed. The paper concludes with future research directions on end‐to‐end QoS provisioning in IoT.

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Section: A Brief Discussion and Summary Of Related Workmentioning

confidence: 99%

QoS‐aware IoT networks and protocols: A comprehensive survey

Dilek

Irgan

Guzel

et al. 2022

Int J Communication

View full text Add to dashboard Cite

show abstract

“…In this paper, we focus on clusters of resource-constrained edge devices that are connected through a wireless network. Such networked edge clusters are becoming increasingly common due to their low-cost and scalability at the edge [14].…”

Section: Introductionmentioning

confidence: 99%

DEFER: Distributed Edge Inference for Deep Neural Networks

Parthasarathy¹,

Krishnamachari

2022

2022 14th International Conference on COMmunication Systems &Amp; NETworkS (COMSNETS)

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Edge inference is becoming ever prevalent through its applications from retail to wearable technology. Clusters of networked resource-constrained edge devices are becoming common, yet there is no production-ready orchestration system for deploying deep learning models over such edge networks which adopts the robustness and scalability of the cloud. We present SEIFER, a framework utilizing a standalone Kubernetes cluster to partition a given DNN and place these partitions in a distributed manner across an edge network, with the goal of maximizing inference throughput. The system is node fault-tolerant and automatically updates deployments based on updates to the model's version. We provide a preliminary evaluation of a partitioning and placement algorithm that works within this framework, and show that we can improve the inference pipeline throughput by 200% by utilizing sufficient numbers of resource-constrained nodes. We have implemented SEIFER in open-source software that is publicly available to the research community.

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“…In this paper, we focus on clusters of resourceconstrained edge devices. These edge clusters are becoming increasingly common due to their low-cost and scalability at the edge [38]. Many lessons in high-availability and application portability can be taken from cloud computing [41].…”

Section: Introductionmentioning

confidence: 99%

Partitioning and Placement of Deep Neural Networks on Distributed Edge Devices to Maximize Inference Throughput

Parthasarathy¹,

Krishnamachari

2022

2022 32nd International Telecommunication Networks and Applications Conference (ITNAC)

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Edge inference has become more widespread, as its diverse applications range from retail to wearable technology. Clusters of networked resource-constrained edge devices are becoming common, yet no system exists to split a DNN across these clusters while maximizing the inference throughput of the system. Additionally, no production-ready orchestration system exists for deploying said models over such edge networks which adopts the robustness and scalability of the cloud. We present an algorithm which partitions DNNs and distributes them across a set of edge devices with the goal of minimizing the bottleneck latency and therefore maximizing inference throughput. The system scales well to systems of different node memory capacities and numbers of nodes, while being node fault-tolerant. We find that we can reduce the bottleneck latency by 10x over a random algorithm and 35% over a greedy joint partitioning-placement algorithm, although the joint-partitioning algorithm outperforms our algorithm in most practical use-cases.Furthermore we find empirically that for the set of representative models we tested, the algorithm produces results within 9.2% of the optimal bottleneck latency. We then developed a standalone cluster network emulator on which we tested configurations of up to 20 nodes and found a steady increase in throughput and decrease in end-to-end latency as the cluster size scales. In these tests, we observed that our system has multi-node fault-tolerance as well as network and system IO fault-tolerance. We have implemented our framework in open-source software that is publicly available to the research community at https://github.com/ANRGUSC/SEIFER.

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A Survey of Architecture, Framework and Algorithms for Resource Management in Edge Computing

Cited by 6 publications

References 77 publications

QoS‐aware IoT networks and protocols: A comprehensive survey

QoS‐aware IoT networks and protocols: A comprehensive survey

DEFER: Distributed Edge Inference for Deep Neural Networks

Partitioning and Placement of Deep Neural Networks on Distributed Edge Devices to Maximize Inference Throughput

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