Nexedi: open source convergent edge computing made in Europe

Who is...

Jean-Paul Smets

Jean-Paul Smets

Jean-Paul Smets, CEO of Nexedi

Company profile

Nexedi

The interview

Q: What is your name?

My name is Jean-Paul Smets, I'm CEO of Nexedi, one of Europe's largest free software publisher and one of the inventors of edge computing with our SlapOS solution.

Q: What is your product?

SlapOS, a convergent software for orchestration of software services across a variety of networks - server infrastructures, robots in a factor, swarms of drones and open radio base stations.

Q: Is it SaaS? PaaS? IaaS? Other?

SlapOS is an overlay operating system which can be used to build public or private cloud computing infrastructure (IaaS, PaaS, SaaS, DraaS) hyperconverged infrastructure (virtualisation of compute, storage and network), edge computing infrastructure (CDN, IoT gateway, AI offloading, cache, buffering), 4G/5G network management system (eNodeB, locale core network) ; resilient low latency network as a service (IPv4, IPv6, RINA) ;

Q: Do you provide your technology to cloud operators?

Yes

Q: What are the unique selling propositions of your product? Is your solution different or better than competing solutions?

Contrary to competing solutions, SlapOS does not only cover the technical side of cloud computing such as service orchestration, but it also implements the business side of it such as invoicing, payment and accounting. 

Q: Which free and open source software solutions do you use?

SlapOS is itself free software. It is based on buildout, re6stnet, babeld and erp5 which are all free software.

Q: What components did you develop yourself?

We develop our whole software stack ourselves based on python, babeld, buildout and related open source software libraries.

Q: What are the strong points of your technology?

SlapOS is as a cloud operating system in which "everything is a process". If one has to manage thousands of servers with thousands of processes, hundreds of different applications in multiple releases or versions, SlapOS is useful by making the whole management process well specified, automated and controllable.

Q: Can you name users or clients of your solution? Preferably in the CAC40, DAX30, Fortune500, European governments?

SlapOS powers Teralab, Institut Mines-Télécom's Big Data Platform, but it is also used by Stellantis, Sanef or Airbus to name a few clients.

Q: Do you have a success case among these clients?

Teralab is a good example.

Q: Why did this client choose your solution rather the solution of the market leader or GAFAM?

There was the need for a scalable, industrial grade solution with high security focus that could run a cluster of VMs. While SlapOS did not boast the high-end profile of other providers, our solution was proven to work, as we are using SlapOS ourselves as backbone for our systems and all Nexedi implementations. With Cloudwatt seemlingy being abandoned through a merger, VMWare not providing a sovereign solution and Open-Stack seemingly requiring a too large investment to get running reliably, SlapOS won the bid for implementing a solution with an offer that was cheaper by multiples and also included billing and ticketing on top of cloud orchestration.

Q: What European policies do you suggest to ensure sustainable development of your technology and its adoption?

I am convinced that everything exists in Europe related to Cloud technology, but we (and especially the European government and European institutions) have to use it and buy it from European technology companies, such as us, instead of keep buying GAFAM.

Q: To what extent are the interoperability projects financed by France and Europe likely to guarantee the development of European cloud technologies?

In the current form, projects like GAIAX are probably doing more harm than good for the development of European cloud technologies because they are too much influenced by GAFAM and not use enough the existing European cloud technology.

Q: Tell us about a successful implementation. How did you implement it, why was it a success, where is this implementation today? Plans for the future? How is the relation with the client? Did the client help you get other clients?

Teralab is a very successfull implementation of SlapOS. An initial challenge we had to solve was the fact that Hadoop was running on IPv4 with no easy way to patch or port it to support IPv6. However, as we planned to use our own re6stnet for IPv6 networking within the implementation, we eventually decided to extend re6stnet and also support IPv4 in order to be able to integrate Hadoop. This took about one man-month to develop and can likely be used in other scenarios or with other legacy software. Hence, a useful feature was added to re6stnet as part of this project.

After that, the main task was the extension of our SlapOS's KVM virtual machine profile to automatically provision a cluster of VMs instead of a single machine using SlapOS. To achieve this, we added support for Ansible and Packer to our SlapOS's KVM recipes, giving us both "micro-level" server provisioning as well as "macro-level" cluster orchestration, both of which will also be useful for future implementations.

After three months of development our solution was put into production providing a network of isolated, yet interconnected VMs deployed throughout multiple datacenters. Teralab manages their cluster through an access server with a single point of entry (X509 certified/open VPN/https). Both this server and its backup have also been done using Ansible Playbooks.

The project proved it was possible to develop and setup an automated, customized private infrastructure on existing hardware with operating cost of 1k-2k € per month, fullfilling all security and data access requirements. Moreover, with SlapOS, a single person was able to spend half of his time maintaining a large amount of computers (120 in this case) while doing development the rest of the time, along the way proving that small local French companies can more than compete with large international corporations.

For the future there are plans to extend the implementation by also supporting Big Data software beyond Hadoop (Jupyter, Wendelin, et al) to further strengthen the technical analysis capabilities.