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IoTSim-Stream: Modeling stream graph application in cloud simulation

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Barika, M, Garg, S ORCID: 0000-0003-3510-2464, Chan, A ORCID: 0000-0003-0042-8448, Calheiros, RN and Ranjan, R 2019 , 'IoTSim-Stream: Modeling stream graph application in cloud simulation' , Future Generation Computer Systems , doi: 10.1016/j.future.2019.04.004.

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Abstract

In the era of big data, the high velocity of data imposes the demand for processing such data in real-time to gain real-time insights. Various real-time big data platforms/services (i.e. Apache Storm, Amazon Kinesis) allow to develop real-time big data applications to process continuous data to get incremental results. Composing those applications to form a workflow that is designed to accomplish certain goal is the becoming more important nowadays. However, given the current need of composing those applications into data pipelines forming stream workflow applications (aka stream graph applications) to support decision making, a simulation toolkit is required to simulate the behaviour of this graph application in Cloud computing environment. Therefore, in this paper, we propose an IoT Simulator for Stream processing on the big data (named IoTSim-Stream) that offers an environment to model complex stream graph applications in Multicloud environment, where the large-scale simulation-based studies can be conducted to evaluate and analyse these applications. The experimental results show that IoTSim-Stream is effective in modelling and simulating different structures of complex stream graph applications with excellent performance and scalability.

Item Type: Article
Authors/Creators:Barika, M and Garg, S and Chan, A and Calheiros, RN and Ranjan, R
Keywords: Internet of Things (ioT), stream processing, stream graph applications, multicloud environment, simulator, cloud computing, stream computing, workflows
Journal or Publication Title: Future Generation Computer Systems
Publisher: Elsevier Science Bv
ISSN: 0167-739X
DOI / ID Number: 10.1016/j.future.2019.04.004
Copyright Information:

© 2019 Elsevier B.V. All rights reserved.

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