Fogify: A Fog Computing Emulation Framework

Fogify is an emulation Framework easing the modeling, deployment and experimentation of fog testbeds. Fogify provides a toolset to: model complex fog topologies comprised of heterogeneous resources, network capabilities and QoS criteria; deploy the modelled configuration and services using popular containerized infrastructure-as-code descriptions to a cloud or local environment; experiment, measure and evaluate the deployment by injecting faults and adapting the configuration at runtime to test different "what-if" scenarios that reveal the limitations of a service before introduced to the public.


Resources Heterogeneity

Fogify is able to emulate Fog nodes with heterogeneous resources and capabilities.

Network Links Heterogeneity

Controlling the link quality, such as network latency, bandwidth, error rate, etc.,and even reproduce real-world node-to-node and node-to-network connection traces

Controllable Faults and Alterations

Changes on running topology by injecting faults, alter network quality,and inject (varying) workload and compute resources

Any-scale Experimentation

Scalability from topologies with a limited number of nodes, capable to run on a single laptop or PC, tohundreds or thousands nodes, running on a whole cluster.

Monitoring Capabilities

Collect, manage, and process metrics from emulated Fog Nodes, network connections, and application-level information seamlessly

Rapid Application Deployment

Functional prototypes of an applications, written in docker-compose, demand no modifications to its business logic in order run on Fogify.

Who can use the Fogify Framework?

IoT Service Developers

who wish to ease the testing of their services in geo-distributed fog settings. With the proposed stack, developers design fog enabled services by using Fogify to rapidly perform multiple tests and compare both well-functionality and performance.

Academic Researchers

who wish to quickly assess algorithms for fog realms. Such users want to quickly perform their analysis over system prototypes, testing scenarios and fog settings, to discover insights without time and cost overheads of having to deploy prototypes over geo-distributed infrastructure.

Fog Computing Operators

who wish to assess the impact of IoT devices to their infrastructure before purchasing and deploying to production.

What can you do with Fogify?

Topology Editing

A typical workflow starts with the user editing the docker-compose file of an IoT application, and extend it to encapsulate Fogify’s model. The Fogify model is composed of: (i) Fog Templates, allowing the description of Services, Nodes and Networks; and (ii) the Fog Topology, enabling users to specify Blueprints. A Blueprint represents an emulated device and is a combination of a Node, Service, Networks, replicas and a label. Services are inherited from docker-compose while the x-fogify section provides all Fogify primitives. Thus, users still develop their application using familiar docker constructs with the added functionality of Fogify not affecting portability. This means that a Fogify enhanced description will run in any docker runtime environment without any alterations, however users will lose the functionality offered by Fogify.


When the description is ready, the user deploys the application using the FogifySDK through a Jupyter notebook, with the description received by the Fogify Controller. If no error is detected by the Controller, it spawns the emulated devices and creates the overlay mesh networks between them, instantiates the services, and broadcasts (any) network constraints to Fogify Agents. Specifically, the Controller translates the model specification to underlying orchestration primitives and deploys them via the Cluster Orchestrator, ensuring the instantiation of the containerized services on the emulated environment. Located on every cluster node, Fogify Agents expose an API to accept requests from the Controller, apply network QoS primitives, and monitor the emulated devices.


On a running emulated deployment, Fogify enables developers to apply Actions and “what-if” Scenarios (sequences of timestamped actions) on their IoT services, such as ad-hoc faults and topology changes. Actions and Scenarios are written by following the Fogify Runtime Evaluation Model. When an action or a scenario is submitted, the Fogify Controller coordinates its execution with the Cluster Orchestrator and the respective Fogify Agents. Furthermore, Fogify captures performance and app-level metrics via the Fogify Agent monitoring module. All metrics are stored at the Agent’s local storage and can be retrieved from the FogifySDK.


To create an end-to-end interactive analytic tool for emulated deployments, we exploit the FogifySDK capabilities in Jupyter Notebook stack. Specifically, we pre-installed the FogifySDK library on Jupyter thus the user can (un-) deploy a Fog Topology, apply ad-hoc changes and scenarios, and, especially, retrieve runtime performance metrics. For the latter, FogifySDK stores metrics to an in-memory data structure, namely panda’s dataframe, providing exploratory analysis methods that produce plots and summary statistics. Except of out-of-the-box plots, provided by Pandas, we extended FogifySDK with tailored functions that provide a set of plots illustrating and explaining the effects of actions and scenarios in application performance. With the wide range of analytic methods provided by FogifySDK, users extract useful insights about QoS, cost, and predictive analytics. Finally, users may integrate other libraries, like scikit-learn, to endrose their analysis with ML and AI models.

Screenshots and Images

Fogify Overview
Fogify integration with Jupyter

Who is using it?


The Team

The creators of the Fogify are members of the Laboratory for Internet Computing (LInC), University of Cyprus. You can find more information about our research activity visit our publications’ page and our on-going projects.


For more details about Fogify and our scientific contributions, you can read the papers of Fogify and a published Demo. If you would like to use Fogify for your research, you should include at least on of the following BibTeX entries.

Fogify’s paper BibTeX citation:

author    = {Moysis, Symeonides and Zacharias, Georgiou and Demetris, Trihinas and George, Pallis and Marios D., Dikaiakos},
title     = {Fogify: A Fog Computing Emulation Framework},
year      = {2020},
publisher = {Association for Computing Machinery},
address   = {New York, NY, USA},
booktitle = {Proceedings of the 5th ACM/IEEE Symposium on Edge Computing},
location  = {San Jose, CA, USA},
series    = {SEC ’20}

Fogify’s demo BibTeX citation:

author    = {Moysis, Symeonides and Zacharias, Georgiou and Demetris, Trihinas and George, Pallis and Marios D., Dikaiakos},
title     = {Demo: Emulating Geo-Distributed Fog Services},
year      = {2020},
publisher = {Association for Computing Machinery},
address   = {New York, NY, USA},
booktitle = {Proceedings of the 5th ACM/IEEE Symposium on Edge Computing},
location  = {San Jose, CA, USA},
series    = {SEC ’20}

A. Michailidou, A. Gounaris, M. Symeonides and D. Trihinas, "EQUALITY: Quality-aware intensive analytics on the edge", *Information Systems, Volume 105, 2022, ISSN 0306-4379*

M. Symeonides, D. Trihinas, G. Pallis, M. D. Dikaiakos, C. Psomas and I. Krikidis “5G-Slicer: An emulator for mobile IoT applications deployed over 5G network slices”, 2022 ACM/IEEE Conference on Internet of Things Design and Implementation (IoTDI), 2022 (more details at the 5G-Slicer documentation)

M. Symeonides, D. Trihinas, G. Pallis and M. D. Dikaiakos “Demo: Emulating 5G-Ready Mobile IoT Services”, 2022 ACM/IEEE Conference on Internet of Things Design and Implementation (IoTDI), 2022


The video presentation of the SEC2020 and the one minute demo presentation of Fogify.

Paper's presentation at SEC
One minute demo presentation

Awards & Acknowledgements

The demo of the Fogify (Emulating Geo-distributed Fog Resources) is awarded with the best demo award in SEC2020. This work is partially supported by the EU Commission through RAINBOW 871403 (ICT-15-2019-2020) project and by the Cyprus Research and Innovation Foundation through COMPLEMENTARY/0916/0916/0171 project.