Automating IoT Data-Intensive Application Allocation in Clustered Edge Computing

Rustem Dautov

doi:10.1109/TKDE.2019.2923638

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Automating IoT Data-Intensive Application Allocation in Clustered Edge Computing

2021-01-01

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Rustem Dautov *

Distefano, Salvatore

DOI：10.1109/TKDE.2019.2923638

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Abstract

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Enabling data processing at the network edge, as close to the actual source of data as possible, is a challenging, yet realistic goal to be achieved by the Internet of Things (IoT), which still primarily relies on the Cloud for data processing. By further extending the Fog and Edge computing principles, recent research advancements enabled aggregation of computing resources from multiple edge devices to support data-intensive task processing using Big Data clustering middleware. The use of these existing solutions, however, is hindered by the heterogeneous, dynamic, mobile, resource-constrained, and time-critical nature of loT ecosystems. More specifically, a particularly challenging goal is to discover, select, and cluster suitable edge devices - on the one hand, and decompose and allocate data-intensive tasks with respect to discovered resources - on the other. To address this challenge, this paper introduces a novel decentralized architecture for clustering heterogeneous edge devices and executing data-intensive loT workflows. The proposed approach first breaks down a complex workflow into simpler tasks, then discovers and selects suitable edge devices, and finally allocates the tasks to the selected nodes, connecting them to recompose the original workflow. The proposed approach benefits from an intelligent mapping algorithm that takes into account available cluster resources and processing demands to efficiently allocate fine-grained tasks to selected nodes. To support the clusterisation process, the proposed solution relies on a unified semantic knowledge base that provides a common vocabulary of terms for modelling task requirements and edge device properties, as well as enables automated task grouping and match-making for device discovery and selection, using built-in reasoning capabilities.

Keywords:

Edge computing

big data

stream processing

discovery

knowledge base

ontology

OWL

reasoner

optimisation

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