iFogSim-Placement: A Simulation Framework for Edge Service Placement

Joseph Poon; Christian Cabrera; Neil D. Lawrence

doi:10.1145/3788550.3794939

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iFogSim-Placement: A Simulation Framework for Edge Service Placement

Joseph Poon, Christian Cabrera, Neil D. Lawrence

Proceedings of the 21st International Conference on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2026):253-258, 2026.

Abstract

Service placement in edge computing is a complex multi-objective optimisation problem defined by conflicting requirements and dynamic environments. While self-adaptive approaches are essential for handling this dynamism, the community needs specific frameworks to evaluate them. We introduce iFogSim-Placement, a simulation framework designed for experimenting with self-adaptive service placement. Unlike existing simulators, iFogSim-Placement decouples decision-making from execution for modelling information staleness, mobility, and stochastic demand. The artefact enables implementing self-adaptive approaches as simple Java plug-ins and evaluating them under heterogeneous configurations and relevant metrics. Researchers can define experimental setups to evaluate placement approaches under variable configurations, including network topology, application complexity, request patterns, and mobility scenarios.

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Cite this Paper

BibTeX


@InProceedings{publications/ifogsim-placement-a-simulation-framework-for-edge-service-placement,
  title = 	 {iFogSim-Placement: A Simulation Framework for Edge Service Placement},
  author = 	 {Poon, Joseph and Cabrera, Christian and Lawrence, Neil D.},
  booktitle = 	 {Proceedings of the 21st International Conference on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2026)},
  pages = 	 {253--258},
  year = 	 {2026},
  address = 	 {Rio de Janeiro, Brazil},
  doi = 	 {10.1145/3788550.3794939},
  url = 	 {/publications/ifogsim-placement-a-simulation-framework-for-edge-service-placement.html},
  abstract = 	 {Service placement in edge computing is a complex multi-objective optimisation problem defined by conflicting requirements and dynamic environments. While self-adaptive approaches are essential for handling this dynamism, the community needs specific frameworks to evaluate them. We introduce iFogSim-Placement, a simulation framework designed for experimenting with self-adaptive service placement. Unlike existing simulators, iFogSim-Placement decouples decision-making from execution for modelling information staleness, mobility, and stochastic demand. The artefact enables implementing self-adaptive approaches as simple Java plug-ins and evaluating them under heterogeneous configurations and relevant metrics. Researchers can define experimental setups to evaluate placement approaches under variable configurations, including network topology, application complexity, request patterns, and mobility scenarios.
}
}

Endnote

%0 Conference Paper
%T iFogSim-Placement: A Simulation Framework for Edge Service Placement
%A Joseph Poon
%A Christian Cabrera
%A Neil D. Lawrence
%B Proceedings of the 21st International Conference on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2026)
%D 2026	
%F publications/ifogsim-placement-a-simulation-framework-for-edge-service-placement
%P 253--258
%R 10.1145/3788550.3794939
%U /publications/ifogsim-placement-a-simulation-framework-for-edge-service-placement.html
%X Service placement in edge computing is a complex multi-objective optimisation problem defined by conflicting requirements and dynamic environments. While self-adaptive approaches are essential for handling this dynamism, the community needs specific frameworks to evaluate them. We introduce iFogSim-Placement, a simulation framework designed for experimenting with self-adaptive service placement. Unlike existing simulators, iFogSim-Placement decouples decision-making from execution for modelling information staleness, mobility, and stochastic demand. The artefact enables implementing self-adaptive approaches as simple Java plug-ins and evaluating them under heterogeneous configurations and relevant metrics. Researchers can define experimental setups to evaluate placement approaches under variable configurations, including network topology, application complexity, request patterns, and mobility scenarios.

RIS


TY  - CPAPER
TI  - iFogSim-Placement: A Simulation Framework for Edge Service Placement
AU  - Joseph Poon
AU  - Christian Cabrera
AU  - Neil D. Lawrence
BT  - Proceedings of the 21st International Conference on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2026)
DA  - 2026/09/29	
ID  - publications/ifogsim-placement-a-simulation-framework-for-edge-service-placement
SP  - 253
EP  - 258
DO  - 10.1145/3788550.3794939
UR  - /publications/ifogsim-placement-a-simulation-framework-for-edge-service-placement.html
AB  - Service placement in edge computing is a complex multi-objective optimisation problem defined by conflicting requirements and dynamic environments. While self-adaptive approaches are essential for handling this dynamism, the community needs specific frameworks to evaluate them. We introduce iFogSim-Placement, a simulation framework designed for experimenting with self-adaptive service placement. Unlike existing simulators, iFogSim-Placement decouples decision-making from execution for modelling information staleness, mobility, and stochastic demand. The artefact enables implementing self-adaptive approaches as simple Java plug-ins and evaluating them under heterogeneous configurations and relevant metrics. Researchers can define experimental setups to evaluate placement approaches under variable configurations, including network topology, application complexity, request patterns, and mobility scenarios.

ER  -

APA


Poon, J., Cabrera, C. & Lawrence, N.D.. (2026). iFogSim-Placement: A Simulation Framework for Edge Service Placement. Proceedings of the 21st International Conference on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2026):253-258 doi:10.1145/3788550.3794939 Available from /publications/ifogsim-placement-a-simulation-framework-for-edge-service-placement.html.

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Christian Cabrera Jojoa

Assistant Research Professor, Cambridge University

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Neil D. Lawrence

The DeepMind Professor of Machine Learning, Cambridge University

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