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Autonomous Cloud Operations for AWS
The document discusses the evolution of cloud operations from automated to autonomous systems, emphasizing the need for independent, machine-driven operations that enhance efficiency and decision-making. It highlights the challenges enterprises face, such as significant resource wastage and complexity, while outlining key components for successful autonomous cloud operations like machine learning, standardization, and advanced telemetry. Overall, it encourages organizations to invest in transitioning to autonomous cloud solutions to optimize performance and reduce overhead costs.
Autonomous Cloud Operations for AWS
- 1. Autonomous Cloud Operations AutonomousCloud Operations Asim Razzaq, CEO
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- 3. AUTOMATED VS AUTONOMOUSSYSTEM Automated System: A system that implements a repeatable pre-determined set of rules Human Driven, Machine Assisted Autonomous System: A system that has the ability to act independently Machine Driven, Human Assisted Automobile Assembly Line Boeing 787 Cockpit
- 4. AUTOMATED VS AUTONOMOUSCLOUD OPERATIONS Monitoring & Alerting Reporting & Analytics Continuous Deployment Orchestration Troubleshooting & Tracing Automated System: A system that implements a repeatable pre-determined set of rules Human Driven, Machine Assisted ? Autonomous System: A system that has the ability to act independently Machine Driven, Human Assisted
- 5. Governance Financial and operationalpolicy management Budget enforcement Shutdown untagged resources Operating hours Compliance e.g. GDPR Optimization Consumption to value Right size resources Reserved Instance purchase Lifecycle Management Standardized provisioning & de-provisioning Configure resource size and dependencies Monitoring Troubleshoot & resolve issues Monitor machine & applications Log and analyze machine data Root cause & remediation KEY PILLARS OF MODERN CLOUD OPERATIONS
- 6. 40% Wastage due to OpExCost Model On average enterprises waste 40% cloud infrastructure 30% Time on Manual Cloud Ops On average, enterprises spend 30% of their Cloud Ops time doing manual tasks that should be automated Over 50% of enterprises are looking to decentralize cloud operations 25+ Tools Causing Fatigue On average, enterprises have 25+ tools to manage cost, performance and availability SLAs for cloud workloads >50% Cloud Ops Decentralizing 95% IT Workforce Lacking Expertise Over 95% of IT workforce lacks expertise in concepts of large scale, distributed and fault tolerant public cloud computing domain CLOUD OPERATIONS GETTING SIGNIFICANTLY COMPLEX
- 7. WHAT MAKES ITPOSSIBLE NOW? Autonomous Vehicles ● Scale and variety of data ● Maturity of ML models ● Network bandwidth ● Cloud & local processing power ● Mapping technology ● Vision Detection
- 8. WHAT MAKES ITPOSSIBLE NOW? Autonomous Cloud Operations ● Scale and variety of data ● Compute capacity ● Machine Learning models ● Standardization of Infrastructure as a Service ● Standardization of telemetry, log, machine data ● Programmability and APIs to take action
- 9. AUTONOMOUS CLOUD OPERATIONS Loremipsum porta dolor sit amet nec Lorem ipsum dolor sit amet adipiscing. Donec risus dolor, porta venenatis neque pharetra luctus felis vel tellus nec felis. 45% ● Donec risus dolor porta ● Pharetra luctus felis ● Proin vel tellus in felis ● Molestie nec amet cum Lorem ipsum porta dolor sit amet nec Lorem ipsum dolor sit amet adipiscing. Donec risus dolor, porta venenatis neque pharetra luctus felis vel tellus nec felis. 28% ● Donec risus dolor porta ● Pharetra luctus felis ● Proin vel tellus in felis ● Molestie nec amet cum Lorem ipsum porta dolor sit amet nec Lorem ipsum dolor sit amet adipiscing. Donec risus dolor, porta venenatis neque pharetra luctus felis vel tellus nec felis. 36% ● Donec risus dolor porta ● Pharetra luctus felis ● Proin vel tellus in felis ● Molestie nec amet cum Lorem ipsum porta dolor sit amet nec Lorem ipsum dolor sit amet adipiscing. Donec risus dolor, porta venenatis neque pharetra luctus felis vel tellus nec felis. 17% ● Donec risus dolor porta ● Pharetra luctus felis ● Proin vel tellus in felis ● Molestie nec amet cum Lorem ipsum porta dolor sit amet nec Lorem ipsum dolor sit amet adipiscing. Donec risus dolor, porta venenatis neque pharetra luctus felis vel tellus nec felis. 61% ● Donec risus dolor porta ● Pharetra luctus felis ● Proin vel tellus in felis ● Molestie nec amet cum Deterministic deployments Services ● Jenkins, Travis ● Chef, Ansible, Puppet ● Vagrant ● Spinnaker Level 1 ● 12-factor app ● Fully automated CI/CD ● Testing Automation ● Automated Provisioning ● Automated rollbacks Metrics focused Services ● Prometheus, Data Dog ● Sumologic, Loggly ● Pagerduty, VictorOps ● New Relic, Appdynamics Level 2 ● Fully monitoring, logging and alerting ● Well-defined and measured availability, latency and throughput ● SLOs and SLAs tied to business KPIs Cloud Native Microservices Services ● AWS, Azure, GCP ● Docker, rkt ● Terraform, Cloud Formation, Bosh ● Amazon API Gateway, Envoy, Kong Level 3 ● Manageable loosely coupled systems ● Containerized microservices + service meshes ● Declarative, immutable Infrastructure-as-code Efficient Auto-scaling Services ● Kubernetes ● Chaos Monkey Level 4 ● Container orchestration ● Maximized resource utilization & density ● Provably Resilient Fully self-managed Services ● Autonomous CloudOps Level 5 ● Machine-learning ● System + Business Context
- 10. ASPIRATIONAL USE CASE FaultTolerance: Less than 0.01% failure rate PaymentServ Cluster November 26 - January 2nd Business KPI: At least 10,000 payment transactions per day Performance: 1 sec, 95th percentile latency Cost: At most $2,500 per day
- 11. DETECT THE ANOMALIES YotaScaleConfidential & Proprietary
- 12. IDENTIFY CONTRIBUTING REASONS YotaScaleConfidential & Proprietary
- 13. ROOT CAUSE ANALYSIS YotaScaleConfidential & Proprietary
- 14. PLATFORM CONCEPTS Cloud ProviderData ● Cost ● Utilization ● Inventory ● Logs ● Containers OPTIMIZE Suggestions to remediate issues DIAGNOSE Identify root cause DETECT Discover trends and identify incidents 1 2 3 4 PREDICT Forecast the future Third Party Data ● Performance ● Memory ● Configuration APIs AUTONOMOUS Governance Policies ● Mandatory Tags ● RegEx Formats ● Resource Whitelist ● Purchase Preferences Enterprise Integrations MANUAL
- 16. Proprietary AI ModelsTrained on Cloud Data 50% Accuracy 85% TODAY 10% 502 PB Data Data Ingested Resources Apps Cost Processed 5 PB 4.2 K 1 K $4 M 6 MONTHS Data Ingested Resources Apps Cost Processed 68 PB 81 K 4 K $47 M 1.5 YEARS Data Ingested Resources Apps Cost Processed 502 PB 476 K 21 K $202 M TODAY There is no compression algorithm for experience.
- 17. SUGGESTIONS & RECOMMENDATIONS ●Invest in classic machine learning ● Start with Dev/QA environments ● Pick a use case or a specific type of workload ● Initially focus on autonomous insights and behavior analysis ● Make sure you have scale and variety of data ● In a single customer environment the best you can do is leverage historical data ● Give YotaScale a try!
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