2021 - SRE conferences.md

2021 - SRE conferences

SREcon21

• When Linux Memory Accounting Goes Wrong
• Don't Follow Leaders or "All Models Are Wrong (and So Am I)"
• Let the Chaos Begin—SRE Chaos Engineering Meets Cybersecurity
• What's the Cost of a Millisecond?
• What To Do When SRE is Just a New Job Title?
• Capacity Management for Fun & Profit
• A Political Scientist's View on Site Reliability
• Panel: Engineering Onboarding
• Sparking Joy for Engineers with Observability
• Panel: Observability
• 10 Lessons Learned in 10 Years of SRE
• Rethinking the SDLC
• Elephant in the Blameless War Room—Accountability
• How LinkedIn Performs Maintenances at Scale
• Take Me Down to the Paradise City Where the Metric Is Green and Traces Are Pretty
• Need for SPEED: Site Performance Efficiency, Evaluation and Decision
• SLX: An Extended SLO Framework to Expedite Incident Recovery
• Watching the Watchers: Generating Absent Alerts for Prometheus
• A Principled Approach to Monitoring Streaming Data Infrastructure at Scale
• Let's Bring System Dynamics Back to CS!
• From 15,000 Database Connections to under 100—A Tech Debt Tale
• MySQL and InnoDB Performance for the Rest of Us
• Cache Strategies with Best Practices
• Optimizing Cost and Performance with arm64
• Ceci N'est Pas un CPU Load
• Grand National 2021: Managing Extreme Online Demand at William Hill
• Microservices above the Cloud—Designing the International Space Station for Reliability
• Horizontal Data Freshness Monitoring in Complex Pipelines
• How We Built Out Our SRE Department to Support over 100 Million Users for the World's 3rd
• You've Lost That Process Feeling: Some Lessons from Resilience Engineering
• Scaling for a Pandemic: How We Keep Ahead of Demand for Google Meet during COVID-19
• DevOps Ten Years After: Review of a Failure with John Allspaw and Paul Hammond
• What If the Promise of AIOps Was True?
• Model Monitoring: Detecting and Analyzing Data Issues
• Leveraging ML to Detect Application HotSpots [@scale, of Course!]
• Demystifying Machine Learning in Production: Reasoning about a Large-Scale ML Platform
• Designing an Autonomous Workbench for Data Science on AWS
• Panel: OpML
• When Systems Flatline—Enhancing Incident Response with Learnings from the Medical Field
• Evolution of Incident Management at Slack
• Hacking ML into Your Organization
• Automating Performance Tuning with Machine Learning
• Practical TLS Advice for Large Infrastructure
• Learning More from Complex Systems
• Of Mice & Elephants
• User Uptime in Practice
• Nothing to Recommend It: An Interactive ML Outage Fable
• Improving Observability in Your Observability: Simple Tips for SREs
• SRE for ML: The First 10 Years and the Next 10
• Lessons Learned Using the Operator Pattern to Build a Kubernetes Platform
• Nine Questions to Build Great Infrastructure Automation Pipelines
• Hard Problems We Handle in Incidents but Aren't Recognized
• Experiments for SRE
• Reliable Data Processing with Minimal Toil
• SRE "Power Words"—the Lexicon of SRE as an Industry
• How Our SREs Safeguard Nanosecond Performance—at Scale—in an Environment Built to Fail
• Panel: Unsolved Problems in SRE
• Beyond Goldilocks Reliability
• A Retrospective: Five Years Later, Was Chaos Engineering Worth It?
• The Origins of USAA's Postmortem of the Week
• Cache for Cash—Speeding Up Production with Kafka and MySQL binlog
• Taking Control of Metrics Growth and Cardinality: Tips for Maximizing Your Observability
• Games We Play to Improve Incident Response Effectiveness
• Food for Thought: What Restaurants Can Teach Us about Reliability
• Latency Distributions and Micro-Benchmarking to Identify and Characterize Kernel Hotspots
• Trustworthy Graceful Degradation: Fault Tolerance across Service Boundaries
• Spike Detection in Alert Correlation at LinkedIn