CREATE-NET

Center for REsearch and Telecommunication Experimentation for NETworked communities

February 08, 2016

Fiware Ops Chapter: a proposal to the OPENSTACK SUMMIT

( Source: CREATE-NET )

FIWARE OPS Chapter submitted a proposal titled "Monitoring a multi-region Cloud based on OpenStack: the FIWARE Lab case study" to the next OpenStack Summit (Austin TX – April 25-29).

Fiware Ops Chapter: a proposal to the OPENSTACK SUMMIT

FIWARE Lab is a multi-region federated Cloud, based on OpenStack. Currently it comprises 15 regions spread all over Europe (but with a presence also in LatAm). It offers a capacity of more than 3000 cores, 10 TB RAM, 500 TB disk and is going to be further expanded with the addition of new regions/nodes.
Being FIWARE Lab the experimentation platform where FIWARE services can be deployed, FIWARE brings on top of FIWARE Lab (and consequently on top of OpenStack) an IoT platform cloud services that offers standards for IoT-enabled smart applications integrating technologies for context information gathering, management, publication, real-time processing and big data analysis.
We developed a distributed and scalable monitoring system for FIWARE Lab able to collect, process, analyse and finally present the relevant data to the interested users. Different users are animating FIWARE Lab with different needs and different profiles in terms of monitoring requirements spawning from accessing single resource metrics to having an aggregated view of the status of all the regions, from keeping data private on a given (geographical) region to sharing data among all the regions, from managing real time data to requesting intensive data analytics. All these challenges (and many more) have been tackled during the development of the FIWARE Lab monitoring system.      
The proposal submitted to the OpenStack Summit will focus on the architecture of the solution in term of: collection of custom resource metrics and sanity check measurements, data confidentiality and data replication, real time data vs historical aggregated data on different dimensions (time, tenant, “space”), data analytics, unified cross-region data visualization.

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