7–9 November 2016: Conference & Tutorials
9–10 November 2016: Training
Amsterdam, The Netherlands

Schedule: Cloud sessions

11:50–12:30 Monday, 7/11/2016
Average rating: ****.
(4.33, 9 ratings)
We often hear talks on scale and reliability, mostly based on personal experience and lessons learned. Avishai Ish-Shalom asks what mathematics tells us about reliability and scale. Can math help us scale our systems and companies? It turns out that failure models, probability, statistics, and other domains can help our analysis and provide useful insights Read more.
13:45–14:25 Monday, 7/11/2016
Metrics/monitoring Cloud, DevOps Auditorium (Ground + Balcony) Audience level: Intermediate
Tudor Golubenco (Elastic)
Average rating: ***..
(3.67, 12 ratings)
Tudor Golubenco discusses some of the new challenges that logging and monitoring systems are facing in today’s world of containers and microservices and how the open source ELK stack—Elasticsearch, Logstash, and Kibana—is evolving into the Elastic stack—Elasticsearch, Logstash, Kibana, and Beats—to meet these new requirements. Read more.
14:40–15:20 Monday, 7/11/2016
Metrics/monitoring Cloud, Databases Auditorium (Ground + Balcony) Audience level: Intermediate
Radu Gheorghe (Sematext Group), Rafał Kuć (Sematext Group)
Average rating: ***..
(3.00, 13 ratings)
Doing a proof of concept with Elasticsearch and the Elastic stack is easy. Pushing the limits of its performance and scale is quite another thing. Radu Gheorghe and Rafał Kuć concentrate on the latter, discussing both the pitfalls and the best practices of using Elasticsearch for logs and metrics. Read more.
14:40–15:20 Monday, 7/11/2016
Reimaging DevOps, security, and infrastructure Cloud, Continuous delivery Emerald Room & Lounge Audience level: Beginner
Rix Groenboom (Parasoft), Robert Schrijvers (Schrijvers IT Improvement)
Average rating: *....
(1.94, 17 ratings)
Do you want to regain control of testing what you want to test, when you want to? Are your testing efforts blocked by unavailable or restricted dependencies? Rix Groenboom and Robert Schrijvers demonstrate an approach that allows you to create tailor-made test environments on the fly by leveraging service virtualization, containers, and cloud services, enabling you to test anything, anytime. Read more.
17:05–17:45 Monday, 7/11/2016
Reimaging DevOps, security, and infrastructure Cloud, Enterprise Emerald Room & Lounge Audience level: Intermediate
Peter Sbarski (A Cloud Guru)
Average rating: ****.
(4.60, 10 ratings)
With the release of AWS Lambda, there has been a sustained movement toward the adoption of serverless architectures, which allow developers to build rich, scalable, and cost-effective applications without having to maintain traditional multitier backends. Peter Sbarski explains how to create scalable applications using serverless architecture with AWS Lambda, API Gateway, and other services. Read more.
17:05–17:45 Monday, 7/11/2016
Metrics/monitoring Cloud, DevOps Auditorium (Ground + Balcony) Audience level: Beginner
Björn Rabenstein (Grafana Labs)
Average rating: ***..
(3.50, 12 ratings)
Kubernetes and Prometheus are still pretty young, but somehow they immediately fell in love when they first met early last year. And now Prometheus has moved into the shiny new CNCF building that Kubernetes has called home for a while. Björn Rabenstein explores how the first two projects hosted by the Cloud Native Computing Foundation work together. Read more.
17:05–17:45 Monday, 7/11/2016
Amir Chaudhry (Docker)
Average rating: ****.
(4.50, 2 ratings)
The stakes are rising as we connect ever more electronics to the Internet (connected pacemaker, anyone?), and we tend to carry forward legacy technology, assumptions, and problems into these new environments. By rethinking how we deploy and manage cloud software today, we can better program the IoT. Amir Chaudhry explores one approach—unikernels—that can span both the cloud and the IoT. Read more.
14:40–15:20 Tuesday, 8/11/2016
Metrics/monitoring Cloud, DevOps G102/103 Audience level: Intermediate
Arun Kejariwal (Independent)
Average rating: **...
(2.78, 9 ratings)
Data-driven decision making has become a norm in the industry. In light of this—coupled with the high volume and velocity of data streams—large clusters are used to store and analyze data. However, deriving actionable insights from the data chest has been a daunting task. Arun Kejariwal presents approaches for analyzing operations data in the presence of “holes” in the time series. Read more.