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Scheduling of Continuous Operators for IoT edge Analytics with Time Constraints

Abstract : Data stream processing and analytics (DSPA) engines are used to extract in (near) real-time valuable information from multiple IoT data streams. Deploying DSPA applications at the IoT network edge through Edge/Fog architectures is currently one of the core challenges for reducing both network delays and network bandwidth usage to reach the Cloud. In this paper, we address the problem of scheduling continuous DSPA operators to Fog-Cloud nodes featuring both computational and network resources. We are paying particular attention to the dynamic workload of these nodes due to variability of IoT data stream rates and the sharing of nodes' resources by multiple DSPA applications. In this respect, we propose TSOO, a resource-aware and time-efficient heuristic algorithm that takes into account the limited Fog computational resources, the real-time response constraints of DSPA applications, as well as, congestion and delay issues on Fog-to-Cloud network resources. Via extensive simulation experiments, we show that TSOO approximates an optimal operators' placement with a low execution cost.
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https://hal.inria.fr/hal-03701939
Contributor : Patient Ntumba Wa Ntumba Connect in order to contact the contributor
Submitted on : Wednesday, June 22, 2022 - 3:43:13 PM
Last modification on : Friday, June 24, 2022 - 3:44:33 PM

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  • HAL Id : hal-03701939, version 1

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Patient Ntumba, Vassilis Christophides, Nikolaos Georgantas. Scheduling of Continuous Operators for IoT edge Analytics with Time Constraints. SMARTCOMP 2022 - International Conference on Smart Computing, Jun 2022, Espoo, Finland. ⟨hal-03701939⟩

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