Improving Adobe Experience Cloud Services Dependability with Machine Learning

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Adobe Experience Cloud is a collection of best-in-class solutions for marketing, analytics, advertising, and commerce. All integrated on a cloud platform for a single experience system of record. The Adobe Experience Cloud's SRE team works hand-in-hand with the Product and Engineering teams to build dependable services. In this presentation, you will learn how the team leverage Adobe's artificial intelligence and machine learning engine to build predictive auto-scaling and self-healing services.

© 2018 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Improving Adobe Experience Cloud Services Dependability with Machine
Learning
Nicolas Brousse | Director, SRE

© 2018 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Adobe Experience Cloud — an all-in-one experience solution
Adobe Experience Cloud brings together all of your marketing
tech in a single place, so you can do everything from
managing your content and delivering email campaigns to
automating your ad buying and measuring your success.
One integrated approach for one seamless experience.
2

© 2018 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 3
Dependable Services

© 2018 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 4
Real Time Decisions

© 2018 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 5
Increase In Complexity

© 2018 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 6
Growing Amount Of Data

© 2018 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 7
Core Infrastructure
ML & Data Pipeline
Adobe Advertising Cloud
Enable a Machine Learning Pipeline supported by a
Kubernetes Infrastructure to allow fast iterations and
deployment of new models to production.

© 2018 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 8
Adobe Analytics
Build models to provide traffic
prediction and improve capacity
planning efforts.
Forecasting
&
Capacity Planning

© 2018 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 9
Adobe Experience Platform
Provide anomaly detection on streamed
live timeseries data to enable auto-
remediation rules based of forecasted
dynamic thresholds.
Anomaly Detection
&
Self-Healing

© 2018 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 10
Risk Classification
&
Failure Prediction
Adobe Experience Cloud
Improve change management processes through
dynamic risk classification of changes to reduce
number of incidents due to changes.

© 2018 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 11
Build
&
Deploy
Measure
&
Plan
Verify
&
Test
Operate
&
Monitor

© 2018 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Replacing Human Intuition with Machine Learning
Toward A Better Notion Of Risk During A Release

© 2018 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Adoption of Microservices and
Distributed Systems means:
§ Increased complexity for Developers
§ Increased complexity for Operators
§ Increased of noise in DevOps
communications
13
Complexity
Microservices
&
Distributed Systems
Human Cognitive Limit

© 2018 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Analyze Changes and Likelihood of Leading to An Incident
§ Goal: Reduce Incidents due to Changes With Better Risk Classification
of Changes
§ Exported Data of 123 Incidents due to Change and 40,991 Normal
Changes from January 2017 to June 2018
§ Use of Jupyter Notebooks with Python and scikit-learn, etc.
14

© 2018 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Data Findings
§ Most Changes Leading to an Incident
are self reported as Low Risk
§ Most Changes Leading to an Incident
have a short approval deadline <2H
§ Incident Occurrence Increases
Proportionally to Maintenance
Duration
15

© 2018 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Models Comparison
16
Overall
Accuracy
Incident
Accuracy
Notification
Noise
Logistic Regression 85.79% 76.92% 28%
ANN using MLP 83.09% 94.06% 13.8%
Linear SVM 96.04% 99.26% 4.8%

© 2018 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Future Work
§ Link to Existing Monitoring Systems for real-time feedback loop
§ Link to Tickets System and Git repository for better context analysis
§ Link to Service Discovery for Service Dependency awareness
§ Add checkpoints into CI/CD workflow
17

Improving Adobe Experience Cloud Services Dependability with Machine Learning

Everything starts with the Adobe Cloud Platform. Discover how the platform plays a role in the experience business, helping you identify, understand, and engage customers and deliver consistent, continuous, and compelling experiences that delight them at every turn. Find out exactly what we mean by platform, including what it is, where it’s heading, how you can get value now and in the future, and even how Adobe partners can get involved. Of course, we won’t just talk about it, you’ll see it in action, too.

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It can be easy to come up with a TCO analysis that would challenge any public cloud and make you think, "let's go in-house!" What are the challenges and is it really worth it? The TubeMogul Operation team went thru the technical challenges at building a private cloud. In this presentation you will learn how the team went from a R&D to an automated deployment of a bare-metal servers to finally migrate a large workload from a Public Cloud to its own Private Cloud infrastructure. We will detail how the team dealt with unexpected issues and also how we chose the hardware, estimated capacity, stay cost effective, improve overall performance of the system, and bring better control and visibility. This talk will cover the technical detail of: * Evaluating OpenStack, Building and automating a CI environment for a mix of bare metal and cloud servers. * What are the network limitations of OpenStack and how we creatively leverage VLANs to handle large packet per seconds. * How to efficiently monitor your cloud infrastructure Find quickly your bottlenecks * What we missed and should be consider before moving in house Lesson Learned and Post Cost Analysis

USENIX LISA15: How TubeMogul Handles over One Trillion HTTP Requests a Month

Nicolas Brousse

TubeMogul grew from few servers to over two thousands servers and handling over one trillion http requests a month, processed in less than 50ms each. To keep up with the fast growth, the SRE team had to implement an efficient Continuous Delivery infrastructure that allowed to do over 10,000 puppet deployment and 8,500 application deployment in 2014. In this presentation, we will cover the nuts and bolts of the TubeMogul operations engineering team and how they overcome challenges.

Puppet Camp Silicon Valley 2015: How TubeMogul reached 10,000 Puppet Deployme...

Nicolas Brousse

Improving Operations Efficiency with Puppet

Nicolas Brousse

Scaling Bleeding Edge Technology in a Fast-paced Environment

Nicolas Brousse

The document discusses key principles for scaling bleeding edge technology in a fast-paced environment: be passionate about creating real customer value; be pragmatic and focus on solving problems where innovation is needed most; iterate often to refine solutions; and pay down technical debt through refactoring code as understanding improves. It provides quotes from entrepreneurs like Jeff Bezos and technologists like Paul Graham and Ward Cunningham supporting these principles. The document also briefly describes an automated video advertising software solution and its evolution through iterations and refactoring.

Scaling on EC2 in a fast-paced environment (LISA'11 - Full Paper)

Nicolas Brousse

Managing a server infrastructure in a fastpaced environment like a start-up is challenging. You have little time for provisioning, testing and planning but still you need to prepare for scaling when your product reaches the tipping point. Amazon EC2 is one of the cloud providers that we experimented with while growing our infrastructure from 20 servers to 500 servers. In this paper we will go over the pros and cons of managing EC2 instances with a mix of Bind, LDAP, SimpleDB and Python scripts; how we kept a smooth working process by using NFS, auto-mount and shell-scripting; why we switched from managing our instances based on tailor-made AMI/Shell-scripting to the official Ubuntu AMI, Cloud-init and puppet; and finally, we will go over some rules we had to follow carefully to be able to handle billions of daily non-static http request across multiple Amazon EC2 regions.

Bringing Business Awareness to Your Operation Team (Nagios World Conference 2...

Nicolas Brousse

Managing an infrastructure with a global footprint can be a challenging task for a centralized operation team. Bringing business awareness to your operation is key to success. In this talk you will learn how TubeMogul Operation Team easily manage its on-call rotation, how we centralize monitoring information from multiple datacenter for efficient on-call and how we define our business priority with a business focused dashboard.

Optimizing your Monitoring and Trending tools for the Cloud (Nagios World Con...

Nicolas Brousse

Nowadays most start-up are using cloud solution, while some will go from public cloud to hybrid solution, all have to deal with fast growing infrastructure. In this presentation, I will go over few solutions that we implemented at TubeMogul while growing from 20 servers to over 700 servers in 4 years and dealing with over 10 billions HTTP requests a day. With so many informations and data every day, it's hard to get a good read of what really matter and to alert the right person. You will learn how we integrated Nagios with Google Calendar for easy on-call rotation management, how we centralized our Nagios information from 5 different DC to a common dashboard, how we make daily maintenance report for pro-active action.

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