Edge Computing Technology Enablers: A Systematic Lecture Study

Douch, Salmane; Abid, Mohamed Riduan; Zine-Dine, Khalid; Bouzidi, Driss; Benhaddou, Driss

doi:10.1109/access.2022.3183634

Cited by 36 publications

(16 citation statements)

References 332 publications

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“…The basic solution is to offload computing tasks directly from TD to a cloud platform with sufficient resources [ 40 ]. However, excessive network transmission delay will be introduced due to the long distance between the cloud platform and TD [ 41 ]. Instead, edge servers are deployed closer to the TD.…”

Section: Background Of Iot Task Offloading In Mecmentioning

confidence: 99%

A comprehensive review on internet of things task offloading in multi-access edge computing

Dayong,

Bin Abu Bakar,

Isyaku

et al. 2024

Heliyon

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Section: Background Of Iot Task Offloading In Mecmentioning

confidence: 99%

A comprehensive review on internet of things task offloading in multi-access edge computing

Dayong,

Bin Abu Bakar,

Isyaku

et al. 2024

Heliyon

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“…Since its advent, edge computing has received significant attention from researchers. More specifically, edge computing has been applied to improve the performance of several technologies, such as voice/vision recognition [16] and 5G [17]. Moreover, several studies have focused on resolving obstacles in deploying applications in an edge computing environment.…”

Section: Related Workmentioning

confidence: 99%

Node-Based Horizontal Pod Autoscaler in KubeEdge-Based Edge Computing Infrastructure

et al. 2022

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KubeEdge (KE) is a container orchestration platform for deploying and managing containerized IoT applications in an edge computing environment based on Kubernetes. It is intended to be hosted at the edge and provides seamless cloud-edge coordination as well as an offline mode that allows the edge to function independently of the cloud. However, there are unreliable communication links between edge nodes in edge computing environments, implying that load balancing in an edge computing environment is not guaranteed while using KE. Furthermore, KE lacks Horizontal Pod Autoscaling (HPA), implying that KE cannot dynamically deploy new resources to efficiently handle increasing requests. Both of the aforementioned issues have a significant impact on the performance of the KE-based edge computing system, particularly when traffic volumes vary over time and geographical location. In this study, a nodebased horizontal pod autoscaler (NHPA) is proposed to provide dynamical adjustment for the number of pods of individual nodes independently from each other in an edge computing environment where the traffic volume fluctuates over time and location, and the communication links between edge nodes are not stable. The proposed NHPA can dynamically adjust the number of pods depending on the incoming traffic at each node, which will improve the overall performance of the KubeEdge-based edge computing environment. In the KubeEdge-based edge computing environment, the experimental findings reveal that NHPA outperforms KE in terms of throughput and response time by a factor of about 3 and 25, respectively.INDEX TERMS Kubernetes, KubeEdge, horizontal pod autoscaler, dynamic resource provisioning, Edge Computing, IoT.

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“…by real-time applications such as automotive vehicles or telesurgery, makes it hard to support the development of latency-sensitive applications. To cope with these constraints, the computer paradigm known as "Edge Computing" (EC) has arisen [2]. EC promises to leverage cloud computing services and bring * sa.douch@aui.ma them closer to the end users.…”

Section: Introductionmentioning

confidence: 99%

Security Robustness Evolution of Split Edge-Cloud Neural Networks

DOUCH,

ABID,

ZINE-DINE

et al. 2023

Preprint

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Cloud computing, with its novel presence at the cloud Edge, proves indispensable to the success of the fifth (5G) and sixth-generation (6G) networks. This due to the myriad set of expected applications and required stringent constraints on latency and bandwidth, e.g., eHealth and Automotive vehicles which fall under the 5G URLLC (Ultra Reliable and Low Latency Communication) set. In order to reduce bandwidth usage at the Cloud core network and allow for faster latency responses, edge computing proposes a new servicing paradigm consisting of bringing computation and intelligence near where the end users reside, i.e., at the edge of the cloud. This is achieved by the edge infrastructure’s ability to support a variety of intelligent and computational jobs at the radio access network (RAN) level. However, many AI tasks, particularly those involving deep learning, pose a challenge as they require significantly more memory and processing capacity, which cannot be provisioned at the edge. Only cloud data centers can do. To address this issue, different edge intelligence methods, such as quantization, pruning, distributed inference, etc., have been proposed in the literature. This research investigates split neural networks (SNN), a neural network architecture with numerous early exits. SNN greatly decreases memory and computing, making it a promising NN architecture for edge devices and applications. Moreover, as the use of these SNNs grows, it becomes increasingly essential to study and validate their safety properties, notably their tolerance to small perturbations, before deploying them in safety-critical applications. This paper presents, as far as we know, the first exploratory work on the robustness assessment of split Edge Cloud neural networks. We evaluated SNN robustness using auto_LiRPA an advance neural network verification tool that is base on bounds calculation. We also compared the relative robustness of SNN to a normal NN, considering the many parameters that influence SNN’s robustness. Our experiment results show that SNN not only reduces the average inference time by 3/4. But it also proved to be four to ten times more resilient against adversarial attacks than normal NN.

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Edge Computing Technology Enablers: A Systematic Lecture Study

Cited by 36 publications

References 332 publications

A comprehensive review on internet of things task offloading in multi-access edge computing

A comprehensive review on internet of things task offloading in multi-access edge computing

Node-Based Horizontal Pod Autoscaler in KubeEdge-Based Edge Computing Infrastructure

Security Robustness Evolution of Split Edge-Cloud Neural Networks

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