Several weeks ago, I was taking part in a meetup about Kubernetes, when one of the attendees made a remark that resonated deeply with me… Hey, Horacio, that Kubernetes thing is rather cool, but what I would have loved to see is a Functions-as-a-Service platform. Most of my apps could be easily done with a database and several serverless functions! It wasn’t the first time I’d got that question… Being, above all, a web developer, I can definitely relate. Kubernetes is a wonderful product – you can install complicated web architectures with a click – but what about the database + (...)
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Like all the players from the IT sector, OVH has been informed on May 14, 2019 of security vulnerabilities following the discovery of hardware vulnerabilities on Intel processors. These new vulnerabilities are similar to previous spectrum and meltdown vulnerabilities and affect Intel’s microprocessors, which are part of the components used by OVH. Researchers have shown proof of concept attacks under the names RIDL, Fallout and ZombieLoad, which exploit the following attack vectors : CVE-2018-12126 [microarchitectural store buffer data sampling (MSBDS)] CVE-2018-12130 [microarchitectural fill buffer data sampling (MFBDS)] CVE-2018-12127 [microarchitectural load port data sampling (MLPDS)] CVE-2019-11091 [microarchitectural data sampling uncacheable (...)
Comme l’ensemble des acteurs du secteur IT, OVH a été informé le 14 mai 2019 des failles de sécurité consécutives à la découverte de vulnérabilités hardware sur les processeurs Intel. Ces nouvelles vulnérabilités s’apparentent aux précédentes failles de type spectre et meltdown et affectent les microprocesseurs d’Intel, qui font partie des composants utilisés par OVH. Ces failles sont notamment désignées sous les appellations RIDL, Fallout ou ZombieLoad et regroupent les vecteurs d’attaque suivants : CVE-2018-12126 [microarchitectural store buffer data sampling (MSBDS)] CVE-2018-12130 [microarchitectural fill buffer data sampling (MFBDS)] CVE-2018-12127 [microarchitectural load port data sampling (MLPDS)] CVE-2019-11091 [microarchitectural data sampling uncacheable memory (...)
The problem to solve… How to continuously monitor the health of all OVH servers, without any impact on their performance, and no intrusion on the operating systems running on them – this was the issue to address. The end goal of this data collection is to allow us to detect and forecast potential hardware failure, in order to improve the quality of service delivered to our customers. We began by splitting the problem into four general steps: Data collection Data storage Data analytics Visualisation/actions Data collection How did we collect massive amounts of server health data, in a non-intrusive way, within (...)
When discussing the concepts of DevOps and Infrastructure-as-a-Code, the tools developed by HashiCorp quickly come up. With Terraform, HashiCorp offers a simple way to automate infrastructure provisioning in both public clouds and on-premises. Terraform has a long history of deploying and managing OVH’s Public Cloud resources. For example, you can find a complete guide on GitHub. In this article, we will focus on using Terraform to interact with another OVH solution: Private Cloud. Private Cloud enables customers to benefit from a VMware vSphere infrastructure, hosted and managed by OVH. Terraform lets you automate the creation of resources and their life cycle. (...)
Machine Learning projects are becoming an increasingly important component of today’s search for more efficient and complex industrial processes. OVH Prescience is a Machine Learning platform that aims to ease the conception, deployment and serving of models in an industrial context. The system manages Machine Learning pipelines, from data ingestion to model monitoring. This involves the automation of data preprocessing, model selection, evaluation, and deployment within a scalable platform. Prescience supports various kinds of problems, such as regression, classification, time series forecasting, and soon, anomaly detection. Problem resolution is handled through the use of both traditional ML models and neural (...)