Architectures For The Cloud
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The document discusses architectures for cloud computing. It begins by defining cloud computing and its key characteristics according to NIST. It then discusses different cloud deployment models and service models. The document uses the example of an e-commerce company called Spring Biking to illustrate how to architect applications for the cloud in a way that addresses challenges like lack of statefulness, failure handling, and elastic scaling. It discusses strategies like implementing BASE architectures using event sourcing and NoSQL databases.
Architectures For The Cloud
- 1. Architectures For The Cloud Eberhard Wolff Architecture & Technology Manager adesso AG Twitter: @ewolff Blog: http://ewolff.com 29.06.11
- 2. What is Cloud? ► National Institute for Standards and Technology (NIST) Definition ► On-demand self-service > Through a portal, no human interaction > Only pay what you need ► Broad network access > Standard access through the network ► Resource pooling > Server multiple customers with the same set of resources ► Rapid elasticity > More resources are available quickly ► Measured Service > Resources can be monitored 29.06.11
- 3. Cloud Deployment Model ► Public > Available to general public ► Private > Available to only one organization ► Community > Available to several organizations ► Hybrid > Combination of multiple models 29.06.11 3
- 4. IaaS – PaaS – SaaS Infrastructure Platform Software as a Service as a Service as a Service > Virtual Servers > Virtual App Server > Software or Service that you use > Similar to Virtualization > Handles Scale-Out > Components that you > Manage Everything > Mostly Managed by add/integrate into your Yourself Provider app
- 5. Why Cloud? ► Costs ► CapEx (CAPital EXpenditure) vs. OpEx ("OPerational EXpenditure") ► Only really works for Public Cloud ► Business agility ► Self Service portals - no more tickets ► Can create production like environments cheaply and quickly ► Original reason for Amazon to create a Cloud infrastructure: Software Engineers still spent 70% of their time on infrastructure ► Also for Private Cloud ► Business drives IT into the Cloud 29.06.11 5
- 6. How Is Cloud Different?
- 8. How is Cloud Different? ► Can easily and cheaply add new resources > Prefer starting new instances over highly available instances > Prefer adding instances over using a more powerful instance > Might end up with lots of instances ► Prefer dealing with failure over providing a highly available network ► So basically lots of non powerful instances with an unreliable network ► How can you end up with a reliable system then? 29.06.11 8
- 9. Enter Spring Biking! ► The revolutionary web site to create customized bikes! ► We got a few million € Venture Capital ► We need... > Catalog of all Mountain Bike parts and bikes > System to configure custom Mountain Bikes > Order system ► Cloud good idea > No CapEx > Rapid elasticity -> easy to grow ► Focusing on German market
- 10. Spring Biking: Architecture ► Standard Enterprise Architecture ► Relational database Application (Order, Configuration, Catalog) Database 29.06.11 10
- 11. Spring Biking: Architecture ► Standard Enterprise Architecture Wait, didn’t you Application (Order, Configuration, ► Relational database say it should run Catalog) in the Cloud? Database 29.06.11 11
- 12. How Spring Biking Deals with Cloud Challenges ► No state on the web tier > i.e. no session > State stored in database Application ► No CAP issues on the web tier – no data (Order, ► Easy to automatically start new instances if load Configuration, increases Catalog) ► Every PaaS should deal with elastic scaling ► Example: Amazon Elastic Beanstalk > Takes a standard Java WAR > Deploys it > Add elastic scaling ► Could build something similar yourself with an IaaS > Automated deployment > Elastic scaling and load balancing available from Amazon IaaS offerings 29.06.11 12
- 13. How Spring Biking Deals with Cloud Challenges ► Relational database fine for now > Example: Amazon RDS (Relational Database Service) > MySQL and Oracle > MySQL: Multi data center replication > Can deal with failure of one data center ► Add Content Delivery Network (CDN) > Not too many PaaS in Europe or Germany Database > Latency effects revenue – Every 100ms latency costs Amazon 1% of revenue > So add CDN to lower latency 29.06.11 13
- 14. Benefits for the Development Process ► Trivial to get a new version out ► Easy to create a production like environment for test or staging > Take snapshot from production database > Set up new database with snapshot > Create a new environment with a different release of the software > Automated for production > Production-like sizing acceptable: You pay by the hour ► This can also be done using Private Clouds! ► Can be more important than cost reduction ► Business Agility is a major driver for (private) Cloud! 29.06.11 14
- 15. Next step: Spring Biking Goes Global! ► Global demand for bikes is on all time high! ► We need to globalize the offering ► A central RDBMS for the global system is not acceptable > Amazon RDS offers one uniform database for a Region (e.g. US-East, EU-West) > Need a different solution for a global system ► Just an example ► Traditional Enterprise scales to a certain limit ► The question is which ► We are not all going to build Twitter or Facebook 29.06.11 15
- 16. CAP Theorem ► Consistency > All nodes see the same data ► Availability > Node failure do not prevent survivors from operating ► Partition Tolerance > System continues to operate despite arbitrary message loss C ► Can at max have two P A 29.06.11 16
- 17. CAP Theorem Consistency RDBMS Quorum 2 Phase Commit Partition Tolerance Replication DNS Availability
- 18. CAP Theorem in the Cloud ► Need A – Availability > A system that is not available is usually the worst thing > Shutting down nodes is no option ► Need P – Partition Tolerance > Network is not under your control > Lots of nodes -> partitioning even more likely ► No chance for C – Consistency C > Because we can’t ► CA used to be OK with a highly available network and a few nodes P A 29.06.11 18
- 19. BASE ► Basically Available Soft state Eventually consistent ► I.e. trade consistency for availability ► Eventually consistent > If no updates are sent for a while all previous updates will eventually propagate through the system > Then all replicas are consistent > Can deal with network partitioning: Message will be transferred later ► All replicas are always available ► Pun concerning ACID… 29.06.11 19
- 20. BASE in Spring Biking Application Application Application Database Database Database EU-West US-East Asia-Pacific Changes to catalog Eventually propagated 29.06.11 20
- 21. Network Partitioning / Inconsistency Application Application Application Database Database Database Eventually Inconsistent data is EU-West US-East Asia-Pacific consistent Network Partitioning 29.06.11 21
- 22. Implementing BASE Using Event Sourcing ► Do it yourself using a messaging system > JMS (ActiveMQ …) > RabbitMQ Event Domain > Amazon Simple Queue Service (SQS) Model > Amazon Simple Notification Server (SNS) > Easy to duplicate state on nodes > Fail safe: Message will eventually be transferred > …and high latency is acceptable ► Other reason to use Event Sourcing > Capture all changes to an application state as a sequence of events > Originates in Domain Driven Design > Also used as a log of actions (to replay, reverse etc) ► Might end up with an Event-driven Architecture > Might add Complex Event Processing etc. 29.06.11 22
- 23. Implementing BASE Using NoSQL ► Some NoSQL databases include replication ► Example: CouchDB > Replication between nodes > Master-master replication using versioning > Trivial to set up > All nodes have the same data > Sharding only possible with additional proxies 29.06.11 23
- 24. More Sophistacted ► Apache Cassandra ► Each node is master for certain data ► Data is replicated to N nodes ► Data is read from R nodes ► After a write W nodes must acknowledge ► N,R,W are configurable ► Replication done automatically ► Clustering built in ► Tuneable CAP 29.06.11 24
- 25. Different Parts Require Different Architecture ► So far: Catalog > Data must be available on each node > Slight inconsistencies are OK Application > i.e. new item added to catalog ► Stock information should be consistent > So customers are not disappointed Catalog > Might use caching-like structure ► Orders are immediately send to the back end > No local storage at all Order ► A lot more catalog browsing than ordering 29.06.11 25
- 26. More load on catalog -> Less load on order -> More instances Less instances Catalog Catalog Order No local Stock Stock data Database Database All send to Cache Cache backend Updates Stock Master 29.06.11 26
- 27. Handling Log Files ► Business requirements > Need to measure hits on web pages > Need to measure hits for individual products etc. ► Sounds like a batch > File in, statistics out ► But: Data is globally distributed ► Lots of data i.e. cannot be collected at a central place ► Data should stay where it is ► Some nodes might be offline or not available ► Prefer incomplete answer over no answer at all 29.06.11 27
- 28. More Than CAP ► CAP Theorem again ► Consistency, Availability, Network Partitioning ► You can only have two ► But: We want Availability ► …and a flexible trade off between Consistency and Network Partitioning ► Like Casssandra C ► I.e. CAP theorem is not the proper way to think about this P A 29.06.11 28
- 29. Harvest and Yield ► Yield: Probability of completing a request ► Harvest: Fraction of data represented in the result ► Harvest and Yield vs. CAP ► Yield = 100% -> Availability ► Harvest = 100% -> Consistency ► Can be used to think about Cassandra configurations ► Can also be used to execute some logic on all data ► …and wait until enough harvest is there to answer a query ► So: Send out a query to all log files ► …and collect the results 29.06.11 29
- 30. Map / Reduce ► Map: Apply a function to all data > Emit (item name, 1) for each log file line ► Master sorts by item name ► Reduce: Add all (item name, 1) to the total score ► Map can be done on any node ► Master collects data Reduce Map Map
- 31. Another Case Study ► Financials ► Build a Highly Available, High Throughput System, Low Latency System on Standard Hardware! ► Just like Google and Amazon ► Driver: Standard infrastructure – cheap and stable ► Driver: Even more availability, throughput, scalability and lower latency ► You will need to consider CAP, BASE, Harvest & Yield etc. ► Very likely in a Private Cloud 29.06.11 31
- 32. Another Case Study ► Random Project ► Make deployment easier! ► Make it easy to create test environment! ► Driver: Business Agility and Developer Productivity ► Will need to use automated installation + IaaS or PaaS ► Might be in a Public or Private Cloud 29.06.11 32
- 33. Custom Self Service Portal 29.06.11 33
- 34. Conclusion ► Current PaaS allow to run Enterprise applications unchanged ► At one point you will need to change ► CAP: Consistency, Availability, Partition Tolerance – choose two ► Cloud: AP, no C ► BASE: Basically Available, Soft State, Eventually Consistent ► Can be implemented using Event Sourcing ► …or a NoSQL persistence solution ► Create multiple deployment artifacts to scale each part ► Harvest / Yield: Fine grained CAP ► Map / Reduce to run batches in the Cloud 29.06.11 34