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Optimizing Your AWS Apps & Usage to Reduce Costs - IP Expo
 

Optimizing Your AWS Apps & Usage to Reduce Costs - IP Expo

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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 ...

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.

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    Optimizing Your AWS Apps & Usage to Reduce Costs - IP Expo Optimizing Your AWS Apps & Usage to Reduce Costs - IP Expo Presentation Transcript

    • Optimizing Your AWS Apps and Usage to Reduce Costs Ianni Vamvadelis Manager, Solution Architecture
    • 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
    • Use Only What You Need And pay only for what you use!
    • Customer Example • Chris Scoggins • CEO, National Rail Enquiries
    • 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%
    • 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
    • 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
    • Scale on demand Rigid On-Premise Resources Elastic Cloud Resources Resources scaled to demand Actual demand Predicted Demand Waste Time Customer Dissatisfaction VS. Capacity Capacity Actual demand Time
    • 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’t require 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
    • Right-size your cloud resources • An instance size for every purpose • Assess your memory & CPU requirements - Fit your application to the resource - Fit the resource to your application • Only use a larger instance when needed
    • Optimize your storage choice too: S3 & Glacier S3 and Glacier are both: - Secure - Flexible - Low-cost - Scalable: over 2 trillion - Durable: 99.999999999% (11 “9”s) Amazon Glacier
    • 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 Glacier Illumina, the leading provider of DNA sequencing instruments, uses Glacier to store large blocks of genomic data all over the world
    • Fit your payment model to your business model: EC2 pricing plans On-Demand Instances Pay as you go for computing power Flat hourly rate, no up-front commitments Reserved Instances Spot Instances Pay an up-front fee for a capacity reservation and a lower hourly rate (up to 72% savings) Pay what you want for spare EC2 capacity: your instances run if your bid exceeds the Spot price 1-year or 3-year terms Potential for large scale at low cost: When they’re available, take advantage of 1,000s of Spot Instances at up to 90% savings RI Marketplace: sell RIs you no longer need; buy RIs at a discount 10:00 10:05 10:10 10:15
    • Use a spectrum of payment models For example: Frontend Applications on On-Demand/Reserved Instances Backend Applications* on Spot Instances + * e.g., batch video transcoding
    • Reserved Instance Marketplace: Buy and Sell • 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
    • Measure and Manage “If you cannot measure it, you cannot improve it.” - Lord Kelvin
    • Overview of AWS Monitoring and Management Services AWS provides detailed cloud monitoring and management (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. - Consolidated Billing - - Third-party services are also available
    • Consolidated Billing: Single payer for a group of accounts • One Bill for multiple accounts • Easy Tracking of account charges (e.g., download CSV of cost data) • Group Activities by Paying Account (e.g., Dev, Stage, Test, Prod) • Volume Discounts can be reached faster with combined usage • Reserved Instances are shared across accounts (including RDS Reserved DBs) • AWS Credits are combined to minimize your bill
    • Consolidated Billing Demo (1/3) Get an overall summary total for all your users and accounts
    • Consolidated Billing Demo (2/3) From your payment account login, view details of each linked account in one place
    • Consolidated Billing Demo (3/3) • Drill down into detail’s of each account • Download a CSV file for line item details, then analyze via spreadsheet, pivot tables, etc.
    • 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
    • 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
    • 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
    • Trusted Advisor: Enterprise Strength Monitoring/Optimization • Monitors and recommends optimizations for: • Cost • Security • Fault Tolerance • Performance • Available to customers with Business and Enterprise-level support http://aws.amazon.com/premiumsupport/trustedadvisor/
    • Trusted Advisor: Cost Optimization Tips
    • Trusted Advisor: Performance Tips
    • Third-party services to optimize your AWS usage
    • Scale Opportunistically Opportunity favors the prepared application
    • Time-to-Result Case 1: Value of result quickly diminishes Example: Engineering simulation Delay  Loss of productivity, project slips
    • Time-to-Result Case 2: Result is valuable…until it’s not Example: Weekend regression tests Delay  Minimal impact until 8:00AM Monday
    • 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
    • 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 Lucky Oyster crawled 3.4B Web Pages, building a 400M entry index • Examples: in around 14 hours for $100 (>85% - MapReduce (Hadoop, Amazon EMR) savings)! - Scientific Computing (Monte Carlo simulations) - Batch Processing (video transcoding) - Financial Computing (high-frequency trading algorithm backtesting) - and many others…
    • Use Auto Scaling to dynamically scale your app • Auto Scaling auto-sizes your fleet based on preset alarms and schedules • Integrates with CloudWatch metrics • Use Auto Scaling to - Improve customer experience, application performance - Maximize CPU/IO/Memory utilization - Optimize other metrics Scale with Real-Time Demand
    • Auto-Scaling Example: Netflix
    • 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
    • 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
    • Cost-Saving Examples Achieve potentially large savings by profiling your application and paying only for what you need Base Case You run 10 m3.2xlarge’s OnDemand 24x7: 10 instances X $1.00/inst-hours X 24 hours/day X ~30.5 days/month = $7,320/month Savings Examples If 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% Utilization 4x 3-yr Light RIs @ 15% Utilization 2x On-Demand @ 5% Utilization = $1,843/month (75% savings) If you Auto Scale from 2 to 10 instances around primetime TV (6-11pm, Mon-Fri): 2x 3-yr Heavy RIs @ 100% Utilization 8x 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)
    • 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
    • An example putting it all together: Saving on Batch Processing 1. Pay Only for What You Use: Rightsize your cloud resources 2. Monitor and Manage your system with CloudWatch, Billing Alerts, Trusted Advisor 3. Scale Opportunistically: Auto Scale worker nodes based on size of input queue http://aws.amazon.com/architecture/
    • Conclusion (Part II): Use the cloud to create new products & business models On-Premises Optimized Cloud • Failure is expensive • Failure is inexpensive • Experiment infrequently • Experiment early and often • Less Innovation • More Innovation
    • THANK YOU http://aws.amazon.com/economics
    • APPENDICES
    • 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 periods when your RIs are regularly underutilized (For Enterprise-level support, Trusted Advisor can help with some of these.) • Netflix’s Janitor Monkey automates clean-up - Reduces “unintentional” resource usage - Reduces cost and clutter
    • Other Spot Instance Use Cases • Batch Processing: • Hadoop: • Scientific Computing: • Video/Image Processing: • Testing: • Web/Data Crawling: • Financial: • HPC/HTC: • Cheap Compute: Generic batch processing (scale out computing) MapReduce processing (e.g., Search, Big Data) Scientific trials, simulations, analysis Encoding, transcoding, rendering Continuous testing, load testing websites, etc. Analyzing data and processing it Hedge fund analytics, energy trading, etc. Embarrassingly parallel jobs Backend servers for Facebook games, MineCraft
    • Application Usage Patterns Steady State Spiky Predictable Uncertain unpredictable Example: Corporate Website Example: Marketing Promotions Website Example: Social game or Mobile Website
    • Amazon EMR (Hadoop): Run Task Nodes on Spot Data Source Code/ Scripts Amazon S3 Upload large datasets or log files directly Mapper Reducer Input Data Outpu tData Task Node Amazon Elastic MapReduce Service HiveQL Pig Latin Cascading Amazon S3 Name Node Amazon SimpleDB Task Node Runs multiple JobFlow Steps Core Node Core Node Metadata HiveQL Pig Latin Query HDFS JDBC/ODB C Amazon Elastic MapReduce Hadoop Cluster BI Apps
    • 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 for Thomsen Reuters • Learn more: aws.amazon.com/economics
    • Example Spot Customers
    • Example Architecture 2: Web Application Hosting http://aws.amazon.com/architecture/