Operating Latency Sensitive Applications on Public Serverless Edge Cloud Platforms

Pelle, István; Czentye, János; Dóka, János; Kern, András; Gero, Balazs; Sonkoly, Balázs

doi:10.1109/jiot.2020.3042428

Cited by 37 publications

(8 citation statements)

References 20 publications

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“…Vertical offloading is usually motivated by seeking a node with higher computational resources (hardware and/or network bandwidth) in a superior tier [57,72]. For example, some microservice architectures that employ neural networks through Deep Learning offload the training model to run in a fog or cloud node seeking a higher processing power, and the inference model stays on the edge node [85]. Vertical offloading usually works in explicit mode, where the microservice moves to another host.…”

Section: Rq4 -How Does the Microservice Offloading Process Work?mentioning

confidence: 99%

Relating Edge Computing and Microservices by means of Architecture Approaches and Features, Orchestration, Choreography, and Offloading: A Systematic Literature Review

Castro¹,

Rigo²

2023

Preprint

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Context: Microservices running and being powered by Edge Computing have been gaining much attention in the industry and academia. Since 2014, when Martin Fowler popularized the Microservice term, many studies have been published relating these subjects to explore how the Edge's low-latency feature could be combined with the high throughput of the distributed paradigm from Microservices. Objective: Identifying how Microservices work together with Edge Computing whereas they take advantage when running on Edge. Method: In order to better understand this relationship, we first identified its key concepts, which are: architecture approaches and features, microservice composition (orchestration/choreography), and offloading. Afterward, we conducted a Systematic Literature Review (SLR) as the survey method. Results: We reviewed 111 selected studies and built a taxonomy of Microservices on Edge Computing demonstrating their current architecture approaches and features, composition, and offloading modes. Moreover, we identify the research gaps and trends. Conclusion: This paper is a step forward to help researchers and professionals get a general overview of how Microservices and Edge have been related in the last years. It also discusses gaps and research trends. This SLR will also be a good introduction for new researchers in Edge and Microservices.

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Section: Rq4 -How Does the Microservice Offloading Process Work?mentioning

confidence: 99%

Relating Edge Computing and Microservices by means of Architecture Approaches and Features, Orchestration, Choreography, and Offloading: A Systematic Literature Review

Castro¹,

Rigo²

2023

Preprint

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“…• We use Euclidean distance as our difference measure because it is the most extensively utilized technique. For example, if we have two points ( 1 α , 1 β ) and [5] Ensemble Learning Methods. It is a recent data driven techniques that makes a set of ML algorithm and then modifies new data points by choosing the best from the previous models.…”

Section: Implementation Stepsmentioning

confidence: 99%

“…To solve these problems it is necessary to compute the data nearer to the IoT device which is implemented by edge computing (5)(6)(7)(8). The benefits of IoT with edge computing are low latency, location awareness, real-time processing, more proficient data management, resilience, and scalability.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Comparison of Machine Learning Algorithms for Attack Detection in Internet of Things Edge

Manokaran¹,

Vairavel²

2022

ECS Trans.

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This research work is aimed to perform a comparative analysis of different machine learning algorithms for attack detection at the Internet of Things (IoT) edge. Due to the rising development of IoT, attack detection has become extremely important in network security, as it protects the IoT network from suspicious activities. The self-configuring and open nature of IoT devices is vulnerable to both internal and external attacks. The statistical method of attack detection is not suitable for fast and accurate detection due to the multi-dimensional nature of attacks. Machine learning-based edge computing can rectify these issues through automated response and shifting the computation physically closer to the device edge where the information is generated. In this paper, we have compared the performances of eight machine learning (ML) algorithms to identify the optimal ML algorithm for attack detection in IoT Edge.

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“…The pricing model usually depends on the memory, duration, and the number of executions of a sequence/workflow of functions. The authors of [203] adapt the cloud native approach and related operating techniques for latency sensitive IoT applications operated on public serverless platforms. They argue that solely adding cloud resources to the edge is not enough and other mechanisms and operation layers are required to achieve the desired level of quality.…”

Section: F Economic Aspects Of Edge Placementmentioning

confidence: 99%

Survey on Placement Methods in the Edge and Beyond

Sonkoly

Czentye

Szalay

et al. 2021

IEEE Commun. Surv. Tutorials

Self Cite

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Edge computing is a (r)evolutionary extension of traditional cloud computing. It expands central cloud infrastructure with execution environments close to the users in terms of latency in order to enable a new generation of cloud applications. This paradigm shift has opened the door for telecommunications operators, mobile and fixed network vendors: they have joined the cloud ecosystem as essential stakeholders considerably influencing the future success of the technology. A key problem in edge computing is the optimal placement of computational units (virtual machines, containers, tasks or functions) of novel distributed applications. These components are deployed to a geographically distributed virtualized infrastructure and heterogeneous networking technologies are invoked to connect them while respecting quality requirements. The optimal hosting environment should be selected based on multiple criteria by novel scheduler algorithms which can cope with the new challenges of distributed cloud architecture where networking aspects cannot be ignored. The research community has dedicated significant efforts to this topic during recent years and a vast number of theoretical results have been published addressing different variants of the related mathematical problems. However, a comprehensive survey focusing on the technical and analytical aspects of the placement problem in various edge architectures is still missing. This survey provides a comprehensive summary and a structured taxonomy of the vast research on placement of computational entities in emerging edge infrastructures. Following the given taxonomy, the research papers are analyzed and categorized according to several dimensions, such as the capabilities of the underlying platforms, the structure of the supported services, the problem formulation, the applied mathematical methods, the objectives and constraints incorporated in the optimization problems, and the complexity of the proposed methods. We summarize the gained insights and important lessons learned, and finally, we reveal some important research gaps in the current literature.

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Operating Latency Sensitive Applications on Public Serverless Edge Cloud Platforms

Cited by 37 publications

References 20 publications

Relating Edge Computing and Microservices by means of Architecture Approaches and Features, Orchestration, Choreography, and Offloading: A Systematic Literature Review

Relating Edge Computing and Microservices by means of Architecture Approaches and Features, Orchestration, Choreography, and Offloading: A Systematic Literature Review

An Empirical Comparison of Machine Learning Algorithms for Attack Detection in Internet of Things Edge

Survey on Placement Methods in the Edge and Beyond

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