TY - GEN
T1 - Fogify
T2 - 5th IEEE/ACM Symposium on Edge Computing, SEC 2020
AU - Symeonides, Moysis
AU - Georgiou, Zacharias
AU - Trihinas, Demetris
AU - Pallis, George
AU - Dikaiakos, Marios D.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - Fog Computing is emerging as the dominating paradigm bridging the compute and connectivity gap between sensing devices and latency-sensitive services. However, experimenting and evaluating IoT services is a daunting task involving the manual configuration and deployment of a mixture of geodistributed physical and virtual infrastructure with different resource and network requirements. This results in sub-optimal, costly and error-prone deployments due to numerous unexpected overheads not initially envisioned in the design phase and underwhelming testing conditions not resembling the end environment. In this paper, we introduce Fogify, an emulator easing the modeling, deployment and large-scale experimentation of fog and edge testbeds. Fogify provides a toolset to: (i) model complex fog topologies comprised of heterogeneous resources, network capabilities and QoS criteria; (ii) deploy the modelled configuration and services using popular containerized descriptions to a cloud or local environment; (iii) 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. In the evaluation, proof-of-concept IoT services with real-world workloads are introduced to show the wide applicability and benefits of rapid prototyping via Fogify.
AB - Fog Computing is emerging as the dominating paradigm bridging the compute and connectivity gap between sensing devices and latency-sensitive services. However, experimenting and evaluating IoT services is a daunting task involving the manual configuration and deployment of a mixture of geodistributed physical and virtual infrastructure with different resource and network requirements. This results in sub-optimal, costly and error-prone deployments due to numerous unexpected overheads not initially envisioned in the design phase and underwhelming testing conditions not resembling the end environment. In this paper, we introduce Fogify, an emulator easing the modeling, deployment and large-scale experimentation of fog and edge testbeds. Fogify provides a toolset to: (i) model complex fog topologies comprised of heterogeneous resources, network capabilities and QoS criteria; (ii) deploy the modelled configuration and services using popular containerized descriptions to a cloud or local environment; (iii) 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. In the evaluation, proof-of-concept IoT services with real-world workloads are introduced to show the wide applicability and benefits of rapid prototyping via Fogify.
KW - Fog Computing
KW - Internet of Things
UR - http://www.scopus.com/inward/record.url?scp=85102167194&partnerID=8YFLogxK
U2 - 10.1109/SEC50012.2020.00011
DO - 10.1109/SEC50012.2020.00011
M3 - Conference contribution
AN - SCOPUS:85102167194
T3 - Proceedings - 2020 IEEE/ACM Symposium on Edge Computing, SEC 2020
SP - 42
EP - 54
BT - Proceedings - 2020 IEEE/ACM Symposium on Edge Computing, SEC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 November 2020 through 13 November 2020
ER -