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Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
Advanced Topics - Session 3 - Optimizing AWS Applications
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Advanced Topics - Session 3 - Optimizing AWS Applications

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Many customers choose AWS because they need a highly reliable, scalable, and low-cost platform on which to run their applications. Low “pay only for what you use” pricing and frequent price decreases …

Many customers choose AWS because they need a highly reliable, scalable, and low-cost platform on which to run their applications. Low “pay only for what you use” pricing and frequent price decreases are just the beginning of how AWS can help you optimize your usage and achieve lower costs. In this session, you will learn about a few simple tools for monitoring and managing your AWS resource usage that you can start using right away, as well as some innovative features that can help you operate at lower costs programmatically. Cost allocation reporting, detailed usage reports, billing alerts, EC2 Auto Scaling, Spot and Reserved Instances, and idle resource detection are just a few of the tools and features we will cover.

Paul Duffy, Sr Mgr, Solutions Architecture, AWS
Chris Scoggins, CEO, ATOC

Published in: Technology, Business
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  • 1. Paul DuffyOptimizing Your AWS Applications and Usage to Reduce CostsAWS Product Marketing
  • 2. Agenda• Objective– Review the spectrum of ways to save money on your AWS application• Tenet: Fit the cloud to your product and business model– Use Only What You Need (and pay only for what you use!)– Measure and Manage– Scale Opportunistically• Customer Spotlight– National Rail Enquiries
  • 3. Use Only What You NeedAnd pay only for what you use!
  • 4. Customer ExampleChris ScogginsCEO, National Rail Enquiries
  • 5. Background• Private company created in 1996 owned by the TOCs• From the busiest phone number in the UK to the #1 website in travel• Over 1 million visits everyday across web & mobile• Achieved over 99% migration to self-service• Customer complaints 1.3 per 100,000 contacts• Over £800m of sales leads provided to TOCs and 3rd parties p.a.• Over 500 services provided to 150 clients• Annual growth of 50%
  • 6. The Challenge• Volatility of up to 10x peak demand• Large deployed computer estate across 6 data centres• Ageing computer estate• Rapid growth in B2C and B2B business• Ever increasing rich functionality in channels• Multiple service desks• Suppliers experts in application development not hosting
  • 7. Why Cloud?• Agility and elasticity – use what we need, when needed• High performance – availability & resilience• Market knowledge – solution provided by hosting & SIAM experts• Low cost – pay for use, savings of 30%• Commodity culture – ready and easy to use• Flexibility and freedom – keep up to date & not locked in
  • 8. Scale on demandRigid On-Premise ResourcesWasteCustomerDissatisfactionActual demandPredicted DemandCapacityTimeElastic Cloud ResourcesActual demandResources scaled to demandCapacityTimeVS.
  • 9. Use only what you need: AWS cost savings opportunities• Right-size your cloud resources– Use resources that suit your needs (instance types, storage options, etc.)– Improve performance: reduce churn, underutilization, bottlenecks– Lower costs: maximize your output per dollar, don’t pay for performance you don’trequire• Fit your payment model to your business model– Do you value flexibility or predictability?– Use a portfolio of payment models• Measure and manage your application and cloud resources– Monitor your applications to identify new savings opportunities
  • 10. Right-size your cloud resources: broad EC2 selection• An instance type forevery purpose• Assess your memory& CPU requirements– Fit your applicationto the resource– Fit the resource toyour application• Only use a largerinstance whenneeded
  • 11. Optimize your storage choice too: S3 & Glacier• S3 and Glacier are both:– Secure– Flexible– Low-cost– Scalable: over 2 trillion customer objects– Durable: 99.999999999% (11 “9”s)AmazonGlacier
  • 12. Choosing between S3 and Glacier• Amazon Simple Storage Service (S3)– Designed to serve static content at high volumes, low latency, frequent access– Low cost: as low as 5.5¢ per GB-month (or 3.7¢ for reduced redundancy)• Amazon Glacier– Designed for long-term cold storage: infrequent access, long retrieval times (3-5 hrs)– Extremely low-cost: 1¢ per GB-month• Tips:– Optimize access: Reduce payload size, # of accesses (e.g., consolidated logs)– Monitor for unexpected access/growth patterns: e.g., misconfigured log archiving– Set Lifecycle Policies: object expiration dates; auto-move S3 files to GlacierIllumina, the leading provider of DNA sequencinginstruments, uses Glacier to store large blocks ofgenomic data all over the world
  • 13. Fit your payment model to your business model: EC2 pricing plansOn-DemandInstancesReservedInstancesSpotInstancesPay as you go for computingpowerFlat hourly rate, no up-frontcommitmentsPay an up-front fee for acapacity reservation and a lowerhourly rate (up to 72% savings)1-year or 3-year termsRI Marketplace: sell RIs you nolonger need; buy RIs at adiscountPay what you want for spare EC2capacity: your instances run ifyour bid exceeds the Spot pricePotential for large scale at lowcost: When they’re available,take advantage of 1,000s of SpotInstances at up to 90% savings10:0010:0510:1010:15
  • 14. Use a spectrum of payment modelsFor example:Frontend Applicationson On-Demand/Reserved Instances+Backend Applications*on Spot Instances* e.g., batch video transcoding
  • 15. Reserved Instance Marketplace: Buy and Sell Your RIs• Benefits for Buyers:– Same underlying EC2 hardware– Buy RIs at a discount from AWS price– Increased selection of term lengths &prices• Benefits for Sellers:– Moving to a new AWS region– Changing your instance type– Switching operating systems– Selling capacity when project ends
  • 16. Measure and Manage“If you cannot measure it, you cannot improve it.”- Lord Kelvin
  • 17. Overview of AWS Monitoring and Management Services• AWS provides detailed cloud monitoring and management– Consolidated Billing (see “Account Activity” navigation panel)– CloudWatch (see AWS Management Console)– Billing Alerts (see “Account Activity” navigation panel)– Trusted Advisor (see “Support Center”)– Other APIs: tags, programmatic access, etc.• Third-party services are also available
  • 18. Consolidated Billing: Single payer for a group of accounts• One Bill for multiple accounts• Easy Tracking of accountcharges (e.g., download CSV ofcost data)• Group Activities by PayingAccount (e.g., Dev, Stage, Test,Prod)• Volume Discounts can bereached faster with combinedusage• Reserved Instances are sharedacross accounts (including RDSReserved DBs)• AWS Credits are combined tominimize your bill
  • 19. Consolidated Billing Demo (1/3)• Get an overall summary totalfor all your users and accounts
  • 20. Consolidated Billing Demo (2/3)• From your payment accountlogin, view details of eachlinked account in one place
  • 21. Consolidated Billing Demo (3/3)• Drill down into detail’s of eachaccount• Download a CSV file for lineitem details, then analyze viaspreadsheet, pivot tables, etc.
  • 22. Amazon CloudWatch• Overview– Monitoring for AWS cloud resources and applications• AWS Resources: EC2, RDS, EBS, ELB, SQS, SNS, DynamoDB, EMR, Auto Scaling, …• Custom metrics from your application (use Put API call)– Gain insight, set alarms and notifications, react immediately– Start using within minutes, auto-scale with your application• Sophisticated Automation– Use CloudWatch metrics with Auto Scaling to dynamically scale EC2 instances
  • 23. Use CloudWatch to monitor & manage resource usage• Monitor your resource utilization– Are you using the right instance type?– Have you left instances idle?– Is your instance usage level or bursty?• Manage your resource utilization– Move bursty workloads to other instances– Rebalance your worker nodes– Scale nodes automatically with Auto Scaling
  • 24. Use CloudWatch to create Billing Alerts• Billing Alerts notify you when estimated charges reach a given threshold• Use Billing Alerts to track an individual developer, or your whole business• Easily set up your billing alarm and actions
  • 25. Trusted Advisor: Enterprise Strength Monitoring/Optimization• Monitors and recommendsoptimizations for:– Cost– Security– Fault Tolerance– Performance• Available to customers withBusiness and Enterprise-level supporthttp://aws.amazon.com/premiumsupport/trustedadvisor/
  • 26. Trusted Advisor: Cost Optimization Tips
  • 27. Trusted Advisor: Performance Tips
  • 28. Third-party services to optimize your AWS usage
  • 29. Scale OpportunisticallyOpportunity favors the prepared application
  • 30. Time-to-Result Case 1: Value of result quickly diminishesExample:EngineeringsimulationDelay  Loss ofproductivity,project slips
  • 31. Time-to-Result Case 2: Result is valuable…until it’s notExample:Weekendregression testsDelay  Minimalimpact until8:00AM Monday
  • 32. Consider Spot Instances for greater savings and scale• Spot in a nutshell– Spot instances run when Your Bid ≥ Spot Price– Spot instances = Spare EC2 instances– Spot instances might be interrupted at any time• Benefits– Savings: Up to 90% off On-Demand– Scale: Access up to 1,000s of EC2 instances• To use Spot– Decide on a bid price– Launch via Console, API, Auto Scaling– Monitor Bid Statuses via Console/API
  • 33. What applications work on Spot?• Good Spot applications are:– Delayable: to balance SLA/cost– Scalable: “embarrassingly parallel”– Fault-tolerant: can be terminated without losing all work– Portable across regions, AZs, instance types• Examples:– MapReduce (Hadoop, Amazon EMR)– Scientific Computing (Monte Carlo simulations)– Batch Processing (video transcoding)– Financial Computing (high-frequency trading algorithm backtesting)– and many others…Lucky Oyster crawled 3.4B Web Pages,building a 400M entry index in around14 hours for $100 (>85% savings)!
  • 34. • Auto Scaling auto-sizes your cluster based on preset triggers and schedules• Integrates with CloudWatch metrics• Use Auto Scaling to– Improve customer experience, application performance– Maximize CPU/IO/Memory utilization– Optimize other metricsUse Auto Scaling to dynamically scale your appScale with Real-Time Demand
  • 35. Auto-Scaling Example: Netflix
  • 36. Follow the Money vs. Follow the Customer• Optimize utilization– Auto Scale on utilization metrics: CPU, memory, requests, connections, …• Optimize price paid– Scale with Spot instances when Spot prices are low– e.g., Run batch processes off-peak (nights, weekends) when Spot prices are lower
  • 37. Follow the Money vs. Follow the Customer• Optimize customer experience with Auto Scaling• Example 1: Scale resources to meet customer demand– Video service Auto Scales instances to respond to customer web service requests• Example 2: Scale resources to ensure fresh results– A scientific paper search engine Auto Scales on queue depth (# of new docs to crawl)– 10 instances steady state and up to 5,000+ to ensure minimum throughput time• Example 3: Scale resources preemptively before large demand– A TV show marketing site scales up before the show and back down after
  • 38. Cost-Saving Examples• Achieve potentiallylarge savings byprofiling yourapplication andpaying only forwhat you needBase Case Savings ExamplesYou run 10 m3.2xlarge’sOn-Demand 24x7:10 instancesX $1.00/inst-hoursX 24 hours/dayX ~30.5 days/month= $7,320/monthIf you need to run 100% of the time, indefinitely:10x 3-yr Heavy RIs @ 100% Utilization= $2,731/month (63% savings)If you can layer RIs and On Demand to meet demand:4x 3-yr Heavy RIs @ 100% Utilization4x 3-yr Light RIs @ 15% Utilization2x On-Demand @ 5% Utilization= $1,843/month (75% savings)If you Auto Scale from 2 to 10 instances aroundprimetime TV (6-11pm, Mon-Fri):2x 3-yr Heavy RIs @ 100% Utilization8x 3-yr Light RIs @ 15% Utilization= $1,683/month (77% savings)If you can use 40x Spot Instances at 25% up-time:= $840/month (89% savings)
  • 39. Conclusion (Part I):Fit the cloud to your product and business model• Use Only What You Need (and pay only for what you use!)• Measure and Manage• Scale Opportunistically
  • 40. An example putting it all together: Saving on Batch Processinghttp://aws.amazon.com/architecture/3. ScaleOpportunistically:Auto Scale workernodes based on sizeof input queue1. Pay Onlyfor What YouUse: Right-size yourcloudresources2. Monitor andManage your systemwith CloudWatch,Billing Alerts, TrustedAdvisor
  • 41. Conclusion (Part II):Use the cloud to create new products & business modelsOn-Premises• Failure isexpensive• Experimentinfrequently• Less InnovationOptimized Cloud• Failure isinexpensive• Experiment earlyand often• More Innovation
  • 42. THANK YOU
  • 43. APPENDICES
  • 44. Other simple optimization tips• Don’t forget to…– Disassociate unused EIPs– Delete unassociated Amazon EBS volumes– Delete older Amazon EBS snapshots– Leverage Amazon S3 Object Expiration– Defer batch activity (e.g., Hadoop) to periodswhen your RIs are regularly underutilized(For Enterprise-level support, Trusted Advisor canhelp with some of these.)• Netflix’s Janitor Monkey automates clean-up– Reduces “unintentional” resource usage– Reduces cost and clutter
  • 45. Other Spot Instance Use Cases• Batch Processing: Generic batch processing (scale out computing)• Hadoop: MapReduce processing (e.g., Search, Big Data)• Scientific Computing: Scientific trials, simulations, analysis• Video/Image Processing: Encoding, transcoding, rendering• Testing: Continuous testing, load testing websites, etc.• Web/Data Crawling: Analyzing data and processing it• Financial: Hedge fund analytics, energy trading, etc.• HPC/HTC: Embarrassingly parallel jobs• Cheap Compute: Backend servers for Facebook games, MineCraft
  • 46. Steady StateExample: Corporate WebsiteSpiky PredictableExample: MarketingPromotions WebsiteUncertain unpredictableExample: Social game orMobile WebsiteApplication Usage Patterns
  • 47. Amazon Elastic MapReduceHadoop ClusterHDFSTaskNodeTaskNodeCoreNodeCoreNodeInputData OutputDataAmazon S3MetadataAmazon SimpleDBBI AppsUpload large datasets orlog files directlyDataSourceCode/ScriptsAmazon S3ServiceAmazon ElasticMapReduceHiveQLPig LatinCascadingMapperReducerRuns multipleJobFlow StepsNameNodeJDBC/ODBCHiveQLPig LatinQueryAmazon EMR (Hadoop): Run Task Nodes on Spot
  • 48. Paying as you go on AWS lowers your Total Cost of Ownership• By paying only for what you use,you can save on:– Servers– Storage– Network– Environment– Administration• Example: 82% TCO savings forThomsen Reuters• Learn more:aws.amazon.com/economics
  • 49. Example Spot Customers
  • 50. Example Architecture 2: Web Application Hostinghttp://aws.amazon.com/architecture/

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