Adaptive Scheduling of Continuous Operators for IoT Edge Analytics

In this paper, we address the problem of adaptive scheduling of data stream processing and analytics (DSPA) applications in a shared edge fog cloud (EFC) continuum with response time constraints. The focus is on handling the dynamic workload of DSPA applications caused by the variability of their in...

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Vydáno v:Future generation computer systems Ročník 158; s. 277 - 293
Hlavní autoři: Ntumba, Patient, Georgantas, Nikolaos, Christophides, Vassilis
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.09.2024
Elsevier
Témata:
Cloud
Computer Science
Continuous operator
Data stream
Data Structures and Algorithms
Dynamic system
Edge/Fog
IoT
Networking and Internet Architecture
Operations Research
Optimisation
Queuing theory
Data stream
Dynamic system
Edge/Fog
Optimisation
Cloud
Continuous operator
Queuing theory
IoT
Queueing theory
Optimization
ISSN:0167-739X
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Shrnutí:In this paper, we address the problem of adaptive scheduling of data stream processing and analytics (DSPA) applications in a shared edge fog cloud (EFC) continuum with response time constraints. The focus is on handling the dynamic workload of DSPA applications caused by the variability of their input data stream rates generated by mobile IoT devices, and the dynamically available resource capacity in the EFC continuum. To address these challenges, we characterise the different types of resources in the EFC continuum, as well as the operators that make up a DSPA application. Based on this characterisation, we propose models to evaluate the response time and the cost of using the resources in the always dynamic EFC continuum. We then formulate the problem of adaptive scheduling of a DSPA application in the EFC continuum with the objective of minimising the cost of using the shared resources subject to the constraints of the response time and the available capacity of the EFC resources. We propose a heuristic algorithm that dynamically computes a new scheduling of the DSPA application, taking into account its current deployment state and the current state of the shared resources in the EFC continuum. Experimental results, using simulation, show the effectiveness of our proposed algorithm against algorithms of related work. •Cost Model for evaluating the usage cost of computational and network resources in an Edge–Fog–Cloud (EFC) architecture.•Response Time Model to take into account the constraints related to the time-sensitive nature of data stream processing.•Formulation the problem of adaptive scheduling for DSPA applications across a hierarchical EFC as an optimisation problem.•The objective is to balance the usage of the computational and network resources accross the EFC.•The constraints are related to the available capacities of the two types of resources and the real-time.•Heuristic Algorithm aTSOO-H adaptively schedules DSPA application based on its current deployment state in the EFC architecture.•aTSOO-H algorithm achieves optimal trade-offs between resource usage cost, response time, and execution cost.
ISSN:0167-739X
DOI:10.1016/j.future.2024.04.029