Information Exchanges – Scaling strategies
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This presentation on architecture strategies for information networks aims to: - Present case studies to bring out Architecture Patterns/approach to implement information Networks/Exchanges - Compare the Architecture decisions. Living in the information age we are witness to the constant innovation in strategies to provide and source information in a more timely, orderly, and usable manner; and this is applicable to both individuals and Enterprises alike. One of the challenges for Enterprises today is to devise efficient mechanisms and means to aggregate information from multiple sources (be it across multiple supplier organizations, or individual experts), in real time, and in a scalable manner. Valtech has worked closely with its clients to help address this challenge using approaches as: ‘Information Exchange Hubs’ and ‘Information Networks’
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Information Exchanges – Scaling strategies
- 1. Architecture Strategies For Information Networks Mohammed Shuaib, Principal Architect Chiranth Channappa, Manager - Business Solutions Group
- 2. Information Networks An Information Network (IN) facilitates collaboration of disparate information sources, across: people, organizations and applications • Types of applications that INs • Benefits of INs are built on: • Higher value to the customers- • Transactional systems convenience, turnaround time, value for money • Repositories • Expands the reach of business • Infotainment channels • Catalyst in fueling new business possibilities 2
- 3. History of Information Networks Number & complexity of Information Networks Pre-1980 1980 1990 2000 Present Day Sabre SFTI ITS NYSE B2B Networks ARPANET Direct+ NHIN B2C Networks & Marketplaces Social Networks Usenet 3
- 4. Outline • Problem Statement with the help of a Case Study • Understanding of the business • Architectural Challenges identified • Architecture Strategies & Experiences • Common pitfalls • Solution Considerations • Application of solution considerations for the case study 4
- 5. Planning a Holiday? 5
- 6. The itinerary Flight to Malaysia Stay at Island #1 Cruise to island #2 Stay at island #2 Flight back to India 6
- 7. Players involved Stay at island Stay at island • Airlines #1 • Cruise Liner #2 • Airlines • Onboard • Hotel service • Hotel • Local taxi providers • Taxi Service services • Restaurants • Sightseeing Operators Flight to Cruise to Flight back to Malaysia island #2 India Complexity for • Managing multiple direct service providers • Coordinating the itinerary, e.g. rescheduling / the consumer cancellations 7
- 8. Solving the complexity: The Travel Hub Customers Consumers Agents Travel Information Services Hub Suppliers Other service providers Airlines Car Rentals / Taxis Hotels 8
- 9. Architectural Challenges Integration & Scalability Performance Interoperability Maintenance Others 9
- 10. Architectural Challenges Integration & Scalability Performance Interoperability • # users: 100K • < 5 sec response time • Multiple Protocols • Growth: 40% YOY • Multiple providers with • Multiple Channels • Up to 1000 tps varying latency • Multiple encoding standards Maintenance Others • Testability (Simulation) of providers • Personalization rules • Change management (rollout of • Flexibility for new providers changes) • Maintainability • Monitoring and diagnostics 10
- 12. Travel Hub: Building Blocks Information Hub Application 1 Providers Provider 1 Application 2 Internet End Point Business Integration Internet Adapters Service Layer Infrastructure Provider 2 Application n Provider N Data Access Database 12
- 13. Focus areas for this Talk Scalability Interoperability Performance 13
- 14. Understanding Scalability Response Time Infinite scalability (ideal) # of users 14
- 15. Understanding Scalability Realistic Scalability Curve Scaling threshold Response Time Infinite scalability (ideal) # of users 15
- 16. Understanding Scalability Realistic Scalability Curve New scaling threshold Revised Scalability Scaling threshold Curve Response Time Infinite scalability (ideal) # of users 16
- 17. Top factors hindering scalability #1: Contention for Centralized Resources Data Repository Multiple requests for same record / data causes a bottleneck for an information network to scale 17
- 18. Top factors hindering scalability #1: Contention for #2 Replication Centralized Resources Overheads 1 2 4 3 Cluster of 4 systems Replication Overhead = 6x Data Repository 1 2 3 8 4 Multiple requests for same record / data causes a bottleneck for an 7 6 5 information network to scale Cluster of 8 systems Replication Overhead = 28x! 18
- 19. Top factors hindering scalability #1: Contention for #2 Replication #3 Statefulness Centralized Resources Overheads 1 2 Load Balancer Active 4 3 Server #1 Cluster of 4 systems Replication Overhead = 6x Active Data Server Repository #2 1 2 3 Active Server 8 4 #3 Multiple requests for same record / data causes a Load balancing is more bottleneck for an 7 6 5 effective with information network to scale statelessness Cluster of 8 systems Replication Overhead = 28x! 19
- 20. Factoring Scalability in the HUB Architecture REST based state-less service layer Information Hub Providers Application 1 Provider 1 End Point Business Integration Clustered Internet Application 2 Internet Business Adapters Service Layer Infrastructure Integration Provider 2 Service Layer Infrastructure Provider N Application n Data Access Flexible MOM 1. Partitioning & Replicating clustering 2. NOSQL or Hybrid model Partitioned Partitioned Database Database 20
- 21. Interoperability Agreements! • Protocol What does it take to • Interface make applications • Data talk with each • Service levels other? • Performance • Reliability • Throughput 21
- 22. Interoperability Premise for HUB Architecture Provider Specific Business OTA Message Broker Message Services Request Request Layer Message Gateway Provider 1 Endpoint Response Translator Aggregator Faults OTA Response Message Adaptor Provider 2 2 22
- 23. Applying Interoperability agreements in the Hub Inbound interface: REST Outbound interface: based services Provider-specific Information Hub Application 1 Providers Provider 1 Application 2 Internet End Point Business Integration Internet Adapters Service Layer Infrastructure Provider 2 Application n Provider N Data Access Outbound protocol: Inbound protocol: Data standard: Database Provider-specific HTTP OTA specified (XML Based) 23
- 24. Performance How much time does the server get to process requests? Information Hub Application 1 Providers Provider 1 Application 2 Internet End Point Business Integration Internet Adapters Service Layer Infrastructure Provider 2 Application n Provider N Data Access Total Latency = Server Latency + N/W latency N/W Latency Server Latency Bandwidth constraint and latency are Control server latency, data size constant and out of control! 24
- 25. Performance Hindrances: The Usual Suspects • Remote calls • I/O Bottlenecks • Synchronization • Object creation (Memory allocation) • Algorithm inefficiencies 25
- 26. Principal Performance Construct for HUB Architecture Parallel Processing: Employed parallel processing to process interactions with multiple providers Business Message Broker Services Request Request Adaptor Provider Layer Message 1 1 Response Adaptor Provider Client 2 2 Endpoint Adaptor Provider Faults Response 3 3 Message 26
- 27. Why a Message Broker? Loose coupling • Can add end points dynamically • Originator does not need to know the consumer Asynchronous Model • Asynchronous model enabled by default Container Managed Services • Container managed service such as Alternatives to reliability & availability Message Broker: Additional Features • Map Reduce • Support for monitoring and diagnostics • Concurrency API 27
- 28. Other performance strategies used • Employed a distributed caching provider, Caching • To cache: personalization information, configuration, static/invariant immutable, and any frequently accessed data: Look-ups, masters etc. • DB Connections, Queue connections, Pooling • Temporary Queue (used to receive responses) • Data Transfer Object pooling Asynchronous • Updating of history Processing • Audit trail Aggregation & Pre- • Offers and Campaigns applicability • Various complex report calculations were pre computed into materialized Computing views 28
- 29. Conclusion • Why are architectural considerations different for Information Networks from traditional enterprise or standalone applications? • Traditional approaches for enterprise applications may fail quite severely for Information Networks – need for more innovative approaches 29
- 30. 30
Editor's Notes
- SABRE: Semi-Automatic Business-Related Environment), a computer reservation system or GDS which was developed to automate the way American Airlines booked reservations. Experimental system went online in 1960, full fledged launch in 1964.ITS: Intermarket Trading System (ITS). ITS provided an electronic link between the NYSE and competing exchanges, enabling brokers to access all markets nation- wide to find the best purchase or sale price for a security. Launched in 1978.NYSE Direct+: Automatic Execution Service for execution of limit trading orders at NYSE. Launched in 2000.SFTI: NYSE’s Secure Financial Transaction Infrastructure – Enabledtechnology providers to offer products to trading firms via a hosted environment. Launched in 2008.Usenet: distributed Internet discussion system, established in 1980Sample usenet newsgroups:Comp dotosdotunixdot shellAlt dot arts dot poetry
- Share insights from Financial Industry
- Onboard (aboard the cruise liner) service providers would include spa, entertainment services, restaurant services, etc.Essentially, this slide needs to establish that even for a simple holiday, there can be almost a dozen parties involved for the consumer to enjoy a seamless experience.
- Typical use cases for the holiday consumerSearch servicesReserve/block services Book servicesReview & change reservationsCancel reservationsSolving this complexity: The “Travel Hub”TravelocityMakeMyTrip.comNeither hub is a customer of Valtech. These names have been provided only for reference.
- linear scalability - the ability to maintain a consistent throughput rate proportionally as resources are added to the system. Adding resources incurs additional overheadthis is the "scalability factor", Types of scalability:- This is called linear scalability - If the scalability factor stays constant as you scale. A scalability factor below 1.0 is called sub-linear scalability.Though rare, its possible to get better performance (scalability factor) just by adding more components (i/o across multiple disk spindles in a RAID gets better with more spindles). This is called supra-linear scalability.- negative scalability - If the application is not designed for scalability, its possible that things can actually get worse as it scales..
- linear scalability - the ability to maintain a consistent throughput rate proportionally as resources are added to the system. Adding resources incurs additional overheadthis is the "scalability factor", Types of scalability:- This is called linear scalability - If the scalability factor stays constant as you scale. A scalability factor below 1.0 is called sub-linear scalability.Though rare, its possible to get better performance (scalability factor) just by adding more components (i/o across multiple disk spindles in a RAID gets better with more spindles). This is called supra-linear scalability.- negative scalability - If the application is not designed for scalability, its possible that things can actually get worse as it scales..
- linear scalability - the ability to maintain a consistent throughput rate proportionally as resources are added to the system. Adding resources incurs additional overheadthis is the "scalability factor", Types of scalability:- This is called linear scalability - If the scalability factor stays constant as you scale. A scalability factor below 1.0 is called sub-linear scalability.Though rare, its possible to get better performance (scalability factor) just by adding more components (i/o across multiple disk spindles in a RAID gets better with more spindles). This is called supra-linear scalability.- negative scalability - If the application is not designed for scalability, its possible that things can actually get worse as it scales..
- Other factors affecting scalability are:Overuse of DB SP’s, Synchronization/Locking, I/O
- Other factors affecting scalability are:Overuse of DB SP’s, Synchronization/Locking, I/O
- Other factors affecting scalability are:Overuse of DB SP’s, Synchronization/Locking, I/O
- 250 KB will take about 2 Sec over a B/W of 1 Mbps and 100ms latencyWe would typically get less than one second of server processing time!
- Open for Q&A