Foggy: a platform for workload orchestration in a Fog Computing environment

Abstract : In this paper we present Foggy, an architectural framework and software platform based on Open Source technologies. Foggy orchestrates application workload, negotiates resources and supports IoT operations for multi-tier, distributed, heterogeneous and decentralized Cloud Computing systems. Foggy is tailored for emerging domains such as 5G Networks and IoT, which demand resources and services to be distributed and located close to data sources and users following the Fog Computing paradigm. Foggy provides a platform for infrastructure owners and tenants (i.e., application providers) offering functionality of negotiation, scheduling and workload placement taking into account traditional requirements (e.g. based on RAM, CPU, disk) and non-traditional ones (e.g. based on networking) as well as diversified constraints on location and access rights. Economics and pricing of resources can also be considered by the Foggy model in a near future. The ability of Foggy to find a trade-off between infrastructure owners' and tenants' needs, in terms of efficient and optimized use of the infrastructure while satisfying the application requirements, is demonstrated through three use cases in the video surveillance and vehicle tracking contexts.
Complete list of metadatas

Cited literature [12 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02073503
Contributor : Francesco de Pellegrini <>
Submitted on : Wednesday, April 10, 2019 - 1:16:45 PM
Last modification on : Thursday, April 11, 2019 - 1:17:14 AM

File

Foggy_cloudcom_HAL.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02073503, version 1

Collections

Citation

Daniele Santoro, Daniel Zozin, Daniele Pizzolli, Francesco de Pellegrini, Silvio Cretti. Foggy: a platform for workload orchestration in a Fog Computing environment. 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Dec 2017, Hong Kong, China. ⟨hal-02073503⟩

Share

Metrics

Record views

38

Files downloads

32