Toggle Main Menu Toggle Search

Open Access padlockePrints

IoTSim-Osmosis: A Framework for Modelling and Simulating IoT Applications over an Edge-Cloud Continuum

Lookup NU author(s): Khaled Alwasel, Devki Jha, Fawzy Mohammad H Habeeb, Umit Demirbaga, Dr Ellis SolaimanORCiD, Professor Raj Ranjan



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


The osmotic computing paradigm sets out the principles and algorithms for simplifying the deployment of Internet of Things (IoT) applications in integrated edge-cloud environments. Various existing simulation frameworks can be used to support integration of cloud and edge computing environments. However, none of these can directly support an osmotic computing environment due to the complexity of IoT applications and heterogeneity of integrated edge-cloud environments. Osmotic computing suggests the migration of workload to/from a cloud data centre to edge devices, based on performance and security trigger events. We propose IoTSim-Osmosis -- a simulation framework to support the testing and validation of osmotic computing applications. In particular, our detailed related work analysis demonstrates that IoTSim-Osmosis is the first simulation framework to enable unified modelling and simulation of complex IoT applications over heterogeneous edge-cloud environments. IoTSim-Osmosis is demonstrated using an electricity management and billing application case study, for benchmarking various run-time QoS parameters, such as IoT battery use, execution time, network transmission time and consumed energy.

Publication metadata

Author(s): Alwasel K, Jha DN, Habeeb F, Demirbaga U, Rana O, Baker T, Dustdar S, Villari M, James P, Solaiman E, Ranjan R

Publication type: Article

Publication status: Published

Journal: Journal of Systems Architecture

Year: 2021

Volume: 116

Print publication date: 01/06/2021

Online publication date: 28/11/2020

Acceptance date: 23/11/2020

Date deposited: 23/11/2020

ISSN (print): 1383-7621

ISSN (electronic): 1873-6165

Publisher: Elsevier


DOI: 10.1016/j.sysarc.2020.101956


Altmetrics provided by Altmetric


Funder referenceFunder name