Adaptive Data-Driven Routing for Edge-to-Cloud Continuum: A Content-Based Publish/Subscribe Approach

Čilić, Ivan; Žarko, Ivana Podnar

doi:10.1007/978-3-031-20936-9_3

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Based on the service information in the subscription, you can obtain a list of target services for the event. The event routing module utilizes the shortest path algorithm [22] to forward events to the target service, and then the target service list is returned through event matching. Based on the status of the target service instance, it wakes up an existing service instance to run or starts a new service instance to actively respond to incoming events.…”

Section: B the Event-driven Dynamic Service Collaboration Mechanismmentioning

confidence: 99%

A Service-Based Cloud-Edge Fusion Approach for Abnormality Detection of Power Generation Equipment

Yang,

Duan,

Song

et al. 2024

IEEE Access

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Abnormality detection of power generation equipment is of great significance in enhancing equipment reliability. In the era of Industry 4.0, with the rapid development and extensive application of IoT and sensor technology, large-scale sensing devices are deployed in power plants, resulting in a vast amount of sensor streaming data. The emergence of edge computing enables the streaming data processing and computation on edge devices. It reduces the latency of streaming data processing and improves throughput. However, those edge nodes are heterogeneous, decentralized, and capacity-constrained edge nodes. Moreover, the conventional cloud-edge model exhibits some characteristics such as tight coupling of data and computing, hard to deal with temporally varying continuity, and lack of flexibility. It poses significant challenges to detect normality and provide anomaly warning of power generation equipment. In order to deal with those challenges, we propose a service-oriented approach for fusing cloud-edge capabilities. Firstly, we encapsulated the streaming data and its processing into suitable granular service, serving as fundamental units for basic streaming data processing. These services can be deployed independently and flexibly scheduled in cloud-edge environment to facilitate the development of IoT application systems by developers. The services help to decouple the streaming data and its computing. Secondly, we proposed an event-driven mechanism to enable dynamic collaboration among services, allowing proactive response to events and adaptive adjustment of logic for streaming data processing to cope with the dynamics of IoT and time-varying logic of streaming data processing. This enhances service deployment flexibility, reduces latency in streaming data processing and improved the efficiency. Finally, based on the actual scenario of the fire power plants, we validated the feasibility and effectiveness in detecting equipment abnormalities by using our proposed cloud-edge fusion approach. We compared our proposed approach with three typical streaming data processing architectures including Cloud, iFogSim and PureEdgeSim from processing latency and system throughput. It reduced the latency remarkably with an average reduction of about 78%, 22%, and 16%. It improved system throughput of about 57%, 17%, and 14%.

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Section: B the Event-driven Dynamic Service Collaboration Mechanismmentioning

confidence: 99%

A Service-Based Cloud-Edge Fusion Approach for Abnormality Detection of Power Generation Equipment

Yang,

Duan,

Song

et al. 2024

IEEE Access

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“…In the ECC, devices are organized hierarchically into layers, as shown in Figure 1. A more detailed description of entities in each layer of the depicted architecture is given in our previous work [1]. It is important to note that each layer offloads the upper layer and executes a certain amount of its functionalities.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Container Orchestration Tools in Edge Computing Environments

Čilić

Krivic

Žarko

et al. 2023

Sensors

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Edge computing is a viable approach to improve service delivery and performance parameters by extending the cloud with resources placed closer to a given service environment. Numerous research papers in the literature have already identified the key benefits of this architectural approach. However, most results are based on simulations performed in closed network environments. This paper aims to analyze the existing implementations of processing environments containing edge resources, taking into account the targeted quality of service (QoS) parameters and the utilized orchestration platforms. Based on this analysis, the most popular edge orchestration platforms are evaluated in terms of their workflow that allows the inclusion of remote devices in the processing environment and their ability to adapt the logic of the scheduling algorithms to improve the targeted QoS attributes. The experimental results compare the performance of the platforms and show the current state of their readiness for edge computing in real network and execution environments. These findings suggest that Kubernetes and its distributions have the potential to provide effective scheduling across the resources on the network’s edge. However, some challenges still have to be addressed to completely adapt these tools for such a dynamic and distributed execution environment as edge computing implies.

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PPS: A Publish-Process-Subscribe Middleware for Predictive Supply Chains

Jabbari,

Ramachandran,

Malik

et al. 2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Adaptive Data-Driven Routing for Edge-to-Cloud Continuum: A Content-Based Publish/Subscribe Approach

Cited by 5 publications

References 14 publications

A Service-Based Cloud-Edge Fusion Approach for Abnormality Detection of Power Generation Equipment

A Service-Based Cloud-Edge Fusion Approach for Abnormality Detection of Power Generation Equipment

Performance Evaluation of Container Orchestration Tools in Edge Computing Environments

PPS: A Publish-Process-Subscribe Middleware for Predictive Supply Chains

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