Towards Context-Aware and Dynamic Management of Stream Processing Pipelines for Fog Computing

Wiener, Patrick; Zehnder, Philipp; Riemer, Dominik

doi:10.1109/cfec.2019.8733145

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…Wiener et al [20] describe a conceptual architecture for context-aware stream processing in fog computing systems. In this work, the authors propose relocating the applications according to the changes in the context of the IoT data.…”

Section: Related Workmentioning

confidence: 99%

Context-Aware Routing in Fog Computing Systems

Karagiannis¹,

Frangoudis²,

Dustdar³

et al. 2023

IEEE Trans. Cloud Comput.

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Fog computing enables the execution of IoT applications on compute nodes which reside both in the cloud and at the edge of the network. To achieve this, most fog computing systems route the IoT data on a path which starts at the data source, and goes through various edge and cloud nodes. Each node on this path may accept the data if there are available resources to process this data locally. Otherwise, the data is forwarded to the next node on path. Notably, when the data is forwarded (rather than accepted), the communication latency increases by the delay to reach the next node. To avoid this, we propose a routing mechanism which maintains a history of all nodes that have accepted data of each context in the past. By processing this history, our mechanism sends the data directly to the closest node that tends to accept data of the same context. This lowers the forwarding by nodes on path, and can reduce the communication latency. We evaluate this approach using both prototype-and simulation-based experiments which show reduced communication latency (by up to 23%) and lower number of hops traveled (by up to 73%), compared to a state-of-the-art method.

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Section: Related Workmentioning

confidence: 99%

Context-Aware Routing in Fog Computing Systems

Karagiannis¹,

Frangoudis²,

Dustdar³

et al. 2023

IEEE Trans. Cloud Comput.

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“…Thus, fog cluster management must manage adjacent fog nodes, usually of similar capabilities, to form clusters of nodes [49]. A cluster is composed logically of several containers that cooperate to split a task and process it in parallel [50]. To manage the fog cluster, a management mechanism will perform a critical role.…”

Section: Fc Software Featuresmentioning

confidence: 99%

Fog Computing Based on Machine Learning: A Review

Samann

Abdulazeez

Askar

2021

Int. J. Interact. Mob. Technol.

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<p>Internet of Things (IoT) systems usually produce massive amounts of data, while the number of devices connected to the internet might reach billions by now. Sending all this data over the internet will overhead the cloud and consume bandwidth. Fog computing's (FC) promising technology can solve the issue of computing and networking bottlenecks in large-scale IoT applications. This technology complements the cloud computing by providing processing power and storage to the edge of the network. However, it still suffers from performance and security issues. Thus, machine learning (ML) attracts attention for enabling FC to settle its issues. Lately, there has been a growing trend in utilizing ML to improve FC applications, like resource management, security, lessen latency and power usage. Also, intelligent FC was studied to address issues in industry 4.0, bioinformatics, blockchain and vehicular communication system. Due to the ML vital role in the FC paradigm, this work will shed light on recent studies utilized ML in a FC environment. Background knowledge about ML and FC also presented. This paper categorized the surveyed studies into three groups according to the aim of ML implementation. These studies were thoroughly reviewed and compared using sum-up tables. The results showed that not all studies used the same performance metric except those worked on security issues. In conclusion, the simulations of proposed ML models are not sufficient due to the heterogeneous nature of the FC paradigm.</p>

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“…They predominantly focus on the modularization of applications to deal with the resource constraints of Fog nodes [Benamer et al 2018] [Fiandrino et al 2019] [Prabavathy et al 2019]. These strategies also adopt the web service-based communication techniques to simplify the interactions between different components of modular applications hosted on distributed Fog nodes [Mass et al 2018] [Wiener et al 2019. While assigning the applications to the Fog nodes, the existing application management strategies give much emphasis on meeting the service delivery deadline and optimizing the cost and energy consumptions [Luo et al 2019] [Gazori et al 2019].…”

Section: Motivation Of Researchmentioning

confidence: 99%

“…Due to explicit isolation, a micro-service of an application can be easily attached to other applications as per the requirements. [Kayal and Liebeherr 2019], ], [Mohamed et al 2019], [Wiener et al 2019] and highlight the micro-service-based implementation of IoT applications.…”

Section: Distributedmentioning

confidence: 99%

“…In opposition to bare metals, containers isolate processes with required application packages and they are highly portable across multiple Fog nodes. Containers are used in [Fahs and Pierre 2019], [Kim and Chung 2018], [Luo et al 2019], [Santos et al 2019], [Pallewatta et al 2019] and [Wiener et al 2019] for application placement in Fog environments.…”

Section: Containermentioning

confidence: 99%

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Application Management in Fog Computing Environments: A Taxonomy, Review and Future Directions

Mahmud,

Ramamohanarao,

Buyya

2020

Preprint

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The Internet of Things (IoT) paradigm is being rapidly adopted for the creation of smart environments in various domains. The IoT-enabled Cyber-Physical Systems (CPSs) associated with smart city, healthcare, Industry 4.0 and Agtech handle a huge volume of data and require data processing services from different types of applications in real-time. The Cloud-centric execution of IoT applications barely meets such requirements as the Cloud datacentres reside at a multi-hop distance from the IoT devices. Fog computing, an extension of Cloud at the edge network, can execute these applications closer to data sources. Thus, Fog computing can improve application service delivery time and resist network congestion. However, the Fog nodes are highly distributed, heterogeneous and most of them are constrained in resources and spatial sharing. Therefore, efficient management of applications is necessary to fully exploit the capabilities of Fog nodes. In this work, we investigate the existing application management strategies in Fog computing and review them in terms of architecture, placement and maintenance. Additionally, we propose a comprehensive taxonomy and highlight the research gaps in Fog-based application management. We also discuss a perspective model and provide future research directions for further improvement of application management in Fog computing. CCS Concepts: • General and reference → General literature; • Computer systems organization → Distributed architectures.

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Towards Context-Aware and Dynamic Management of Stream Processing Pipelines for Fog Computing

Cited by 6 publications

References 10 publications

Context-Aware Routing in Fog Computing Systems

Context-Aware Routing in Fog Computing Systems

Fog Computing Based on Machine Learning: A Review

Application Management in Fog Computing Environments: A Taxonomy, Review and Future Directions

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