Automating IoT Data-Intensive Application Allocation in Clustered Edge Computing
Peer reviewed, Journal article
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Date
2019Metadata
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Original version
IEEE Transactions on Knowledge and Data Engineering. 2019. 10.1109/TKDE.2019.2923638Abstract
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 is hindered by the heterogeneous, dynamic, mobile, resource-constrained and time-critical nature of IoT 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 decentralised architecture for clustering heterogeneous edge devices and executing data-intensive IoT 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. 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.