David Chappel S O A Grid

1. This Presentation Courtesy of the International SOA Symposium October 7-8, 2008 Amsterdam Arena www.soasymposium.com info@soasymposium.com Founding Sponsors Platinum Sponsors Gold Sponsors Silver Sponsors 1 <Insert Picture Here> SOA on App Grid: Not Your MOM‟s Bus Dave Chappell VP & Chief Technologist, SOA 1

2. The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle. Next Level Challenges of SOA Moving Forward While Reducing Complexity Benefits New Challenges • Re-Focus on Innovation, New Business Services Large XML Payloads • Service Enablement of IT Assets Tearing Down / • Leverage investment, reuse De-coupling • Business Agility Silos • Better align with the Business Needs Unexpected Usage • Automate Business Function Demands • Business Process Orchestration • Composite Applications Meeting SLA • Flexibility Expectations • Loose Coupling, Modularity Increased Cost of • Easily integrated, upgraded, replaced Transacting • IT Cost Reduction Sharing Information • Shared Services Across Multiple • Eliminate Redundancy Services 4 2

3. <Insert Picture Here> VP Architecture Leading Commercial Rating and Information Insight Company "If you want a 3 second response time, you get 1 sec in the presentation layer, 1 sec in the middle tier, and 1 sec in the backend. Period!”* *commenting on the need for middle tier caching and in-memory state management 5 Concerns linger about lack of performance, scalability 62% Performance or scalability issues 64% 65% 53% Difficult to manage services 58% 61% 65% Poor reliability 60% 59% 2005 53% 2006 Does not meet business needs 55% 54% 2007 Too difficult to adapt and build new 36% * applicatons 54% 52% Lack of security 60% * Not asked in 2005 52% 0% 20% 40% 60% 80% Q: What would constitute SOA failure? 2007 Base: 549 / 2006 Base: 473 / 2005 Base: 167 (Among current deployers) 6 3

4. Service Grid (a.k.a. SOA on App Grid) 8 Service Grid Combines Orchestration, Mediation, State Caching, Demand based provisioning, Deterministic GC WS-Addr WS-Addr Service <ReplyTo> <ReplyTo> Consumer Callback Callback My CRM / Portal ERP Services 1 2 3 4 5 BPEL CEP BAM Rules Service Provider State-aware continuous availability for service infrastructure, services, application data, and processing logic 1 2 3 4 =Service state data 5 =Stateful Service Orchestration 9 4

5. SOA on App Grid Value Proposition • Reduce cost of hitting back-end systems multiple times over the life of a session • Improve response times for service requests and front-end users while keeping load off backend systems • Improved fault tolerance without sacrificing performance • Ensuring predictable latency under increased sustained loads • Achieving dramatic overall increase in performance and throughput • Predictable scalability for XTP • Based on advanced software clustering techniques • Scales out linearly, whether 2 or 2,000 servers • Heterogeneous Environment • High-end / low-cost commodity hardware 10 Key Capabilities • Caching Results of Services Calls • To offload loads on backend services and SOA Infrastructure use side cache to cache results of service calls, c.f. Web cache • Without sacrificing consistency and programmability • Seamless State Management for Stateful Services • Without the need to enforce “state passing” / scrap book design patterns across the organization • Reduced/Eliminate dependency on disk persistence and State Repositories • Without sacrificing HA • Ability to Efficiently Handle Process XML Payloads within SOAs • Without Loss in throughput and increases in latency s • Data Grid and Compute Grid • Linearly scalable shared memory and logic • Intelligent co-location and affinity between processing logic and Grid storage 11 5

6. Data/State Management Techniques in SOA Service State Repository Service Complexity State passing via Tight Coupling XML Payloads Between services 1 2 3 4 Cookies + Servlet 5 Session Stateless Service Sophistication, Stateful Longevity 12 SOA on App Grid Here and Now - Case Studies 13 6

7. Amir Razmara Director, The Gap  Problem:  Coherence*Web Solution: Universal user profile, preferences, Create a “Global Cache Cloud” to shopping cart and single sign-on across maintain a brand agnostic session 4 brands caching layer, enabling the global Shared sessions across all 4 brands session leading to a need for a global session Any server from any brand is able to (each brand has its own cluster of obtain a session from the global cache servers) cloud, enabling SSO and shared bag Possible Solutions: Session durability, immune from crashes in the application tier  State repository with DB Sessions maintained during the nightly  Data Grid backed session cell switch for publishing content management with Coherence*Web *GAP - OOW2008 S299392 - Beyond Performance: Pushing Transaction… 14 State Management in SOA on App Grid | Web Cache Shared Servlet Session State EJB Business logic: Traditional middle tier EJB Web Cntr POJO Data Grid: Web Containers share servlet session state via data grid DB Grid EJB Database: Web Cntr Use for storing, managing transactions .NET *GAP 15 7

8. State Management in SOA on Application Grid | Shared Services Primary Node Service Business logic: Component Services manage their state Web Cntr in the data grid Service Data Grid: Component Use for managing transient state (read and write) DB Grid Service Web Cntr Component Database: Use for storing, managing transactions Write through for state information from data grid Service Component = Write Behind Queue Similar architecture, similar benefits! 16 Kiran Dattani Director SOA & Enterprise Integration Pfizer Worldwide Technology  Problem Real-time SOA: Need Sub 2.5 second response times – Architect the overall process and boundary crossing incur cost application for performance Need to trade-off – between agility and The more backend systems you hit performance during a real-time process, the more response time costs you incur Possible Solutions Make up for the loss in performance XML processing hardware/ appliances from boundary costs by: Mid-tier caching / data grid Doing work in advance – Cache Solution Adopted Do less work in real-time Data grid to cache data for real-time SOA 17 8

9. FIDUCIA IT AG manages and runs … • 63M bank accounts • 5.5M online accounts • 3.4B funds transfers / year • 13.5B IMS transactions / year • Peak: 70M Transactions / day 2,750 Transactions / Second • 101.000 PCs • 9,500 Routers • 9,300 Switches • 10,700 ATMs • 10,023 Statement Printers • 1,192 Service Terminals *Fiducia - OOW2008 S299392 - Beyond Performance: Pushing Transaction… 18 <Insert Picture Here> Mathis Schorer Architect Fiducia  Problem  Caching of Mainframe Data Make use of existing Services in a Improved service performance across batch driven environment all servers Make better use of existing hardware Reduction in mainframe MIPS usage resources  Replacing Queues with Coherence Push throughput & Make use of high parallelism Much easier Setup Move workload off the mainframe = Improved performance saving MIPS, Saving Euros! Drastically reduced load on Oracle Database  Solutions Considered Ability to form a “cloud” of computing (1) Queuing; (2) Data Grid resources *Fiducia - OOW2008 S299392 - Beyond Performance: Pushing Transaction… 19 9

10. SOA on App Grid Patterns for Shared Service State 20 <Insert Picture Here> Director Architecture Leading Educational Publishing Co. “Composable stateful services is an architectural challenge we are struggling with as we build out our new e-learning enterprise architecture. We are very concerned about scalability using conventional approaches You definitely got the attention of our architects and engineers who are now thinking about how SOA grid technology can help” 21 10

11. SOA Grid Patterns • SOA Grid: Basic Patterns for State Management Using Middle Tier Caching • Fault Tolerant Collection • Inline Service State Cache • Side Cache • Stateful Service Load Balancing • Stateful Service Availability/Failover • Advanced State Management Patterns • State Based Notification • Asynch DB Update • Near Cache Optimization • Sticky Load Balancers and Cached Service State • Business Logic Affinity • Claim Check/Pass-by-ref/Deferred State 22 Fault-Tolerant Collection - Primary/Backup Synchronization In-Memory State Storage / Networked Datagrid servers/ Hashmap Primary iFace Node Put() P Application HashKey/CacheKey Object B Backup Node Application [1...n] Object Available Today with Oracle Coherence 23 11

12. Fault-Tolerant Collection - Primary/Backup Synchronization In-Memory State Storage / Networked Datagrid servers/ Hashmap FT Collection Primary iFace Put() Node P Application HashKey/CacheKey Object Backup B Node Application Object Application Object Available Today with Oracle Coherence 24 Fault-Tolerant Collection - Primary/Backup Synchronization In-Memory State Storage / Networked Datagrid servers/ Hashmap FT Collection X iFace Put() Application Object P Primary Backup Node Node Application Object B Application Object Available Today with Oracle Coherence 25 12

13. Fault-Tolerant Collection - Primary/Backup Synchronization In-Memory State Storage / Networked Datagrid servers/ Hashmap FT Collection iFace Put() Application X Object Primary Node Application Object P Backup B Node Application Object Available Today with Oracle Coherence 26 Service State Repository Service Component Service Component Service Component Service Component Service State Service Component Service Component Service Service State Component State Repository Service Service Component Service Component Component Service Component Service Service Service Service Component Component Service Component Component Component 28 13

14. In-Memory FT Service State Service Component Service Component Service Component Service Component Service State Service Component Service Service State Component Service Component Service Service Component Component Service Component = Primary / Backup Service Service memory storage Component Component 29 In-Memory FT Service State Service Component Service Component Service Component Service Component Service State Service Component Service Component Service Service State Component Service Service Component Service Component Component Service Component Service = Primary / Backup Service Service Service memory storage Component Component Service Component Component Component 30 14

15. Inline Shared State Cache Service ‘A’ Invocation Shared State CachePrimary Put(StateData) Node P WSA:Statekey Cachekey Service Provider A Load Balancer Backup Node B Service Consumer Service Provider A1 31 Here today: Usage Patterns with Oracle Coherence and ESB/BPEL After | Caching Results to Service Calls to Backend Systems Service MQ Provider (Transform, Route, Retry) ESB Mediation Service Provider Legacy Service Cache Primary Consumer Node Put (State Data) P Side- CacheKey Cache Service Backup Node B Process Flow / Orchestration Healthcare Portal: Minimizing expensive backend access 32 15

16. After | Caching Results to Service Calls to Backend Systems Service MQ Provider (Transform, Route, Retry) ESB Mediation Service Provider Legacy Service Cache Primary Consumer Node Get(Cachekey) P Side- State Data Cache Service Backup Node B Process Flow / Orchestration Healthcare Portal: Minimizing expensive backend access 33 Side-Cache in OSB (ALSB) Proxy / Pipeline (Telco Use Case) 35 Telco: Usage Patterns with Oracle Coherence and OSB 16

17. Near-Cache Optimization In-Memory State Storage / Networked Datagrid servers/ Primary Node Fetch P Service Service Cache Consumer Provider Backup Node B Service Service Consumer Provider DB Grid Service Provider 41 Geico: 400X Improvement on Reads, 40X Improvement on Writes Shared Service State Repository Revisited In Memory Data Grid w/ Async Write-Behind, Sync Write-thru, Cached-Read Service Bus / BPEL Svc D 1 2 3 4 5 Transform Transform Svc A Svc A Svc B Svc B Svc C Svc C & Route & Route Svc A Relational Store Svc B Svc C SQL (Oracle DB) SQL SQL 46 17

18. SOA on App Grid Not Your MOM‟s Bus 47 SOA Grid – Not Your MOM‟s Bus Why send it when its already there? Web WS-Addr WS-Addr Service <ReplyTo> <ReplyTo> Consumer Callback Callback Portal CRM ERP 1 2 3 4 5 BPEL CEP BAM Rules Web Service Provider •This is already accomplished today using stateful service caching patterns with out-of-box capabilities of Oracle SOA Suite and Coherence 48 *Fiducia 18

19. Case Study: Computing versus Communicating Conventional SOA Custom 46 network Redundant Data 2 2 40 Cache hops/data 2 Mediation Custom serialization Client Biz Logic Redundant Data steps Cache 46 Boundary Crossings SOA Grid Coherence 5 - 7 network Mediation Service hops/data ESB Client Service serialization Service steps Process Flow Horizontally 5-7 scalable 49 Learn More http://www.oracle.com/technology/products/ • Whitepapers • Webcasts • Buyers Guides • Analyst Reports • Case Studies • Podcasts • Technical Information & Forums • http://www.oracle.com/technology/products/webservices_manager • http://ws-security.blogspot.com (Blog) • http://blogs.oracle.com/davidchappell/ (Blog) 50 19

20. Questions, Feedback, Use Cases • Mohamad Afshar • Mohamad.afshar@oracle.com • Dave Chappell • David.Chappell@oracle.com 51 52 20

21. SOA on App Grid Patterns for Shared Service State 53 SOA Grid Patterns • SOA Grid: Basic Patterns for State Management Using Middle Tier Caching • Fault Tolerant Collection • Inline Service State Cache • Side Cache • Stateful Service Load Balancing • Stateful Service Availability/Failover • Advanced State Management Patterns • State Based Notification • Asynch DB Update • Near Cache Optimization • Sticky Load Balancers and Cached Service State • Business Logic Affinity • Claim Check/Pass-by-ref/Deferred State 54 21

22. Fault-Tolerant Collection - Primary/Backup Synchronization In-Memory State Storage / Networked Datagrid servers/ Hashmap Primary iFace Node Put() P Application HashKey/CacheKey Object B Backup Node Application [1...n] Object Available Today with Oracle Coherence 55 Fault-Tolerant Collection - Primary/Backup Synchronization In-Memory State Storage / Networked Datagrid servers/ Hashmap FT Collection Primary iFace Put() Node P Application HashKey/CacheKey Object Backup B Node Application Object Application Object Available Today with Oracle Coherence 56 22

23. Fault-Tolerant Collection - Primary/Backup Synchronization In-Memory State Storage / Networked Datagrid servers/ Hashmap FT Collection X iFace Put() Application Object P Primary Backup Node Node Application Object B Application Object Available Today with Oracle Coherence 57 Fault-Tolerant Collection - Primary/Backup Synchronization In-Memory State Storage / Networked Datagrid servers/ Hashmap FT Collection iFace Put() Application X Object Primary Node Application Object P Backup B Node Application Object Available Today with Oracle Coherence 58 23

24. Inline State Cache Service ‘A’ Invocation State Cache Primary Put(StateData) Node P WSA:Statekey Cachekey Service Provider A Load Balancer Backup Node B Service Consumer Service Provider A1 59 Here today: Usage Patterns with Oracle Coherence and ESB/BPEL Learn More http://www.oracle.com/technology/products/ • Whitepapers • Webcasts • Buyers Guides • Analyst Reports • Case Studies • Podcasts • Technical Information & Forums • http://www.oracle.com/technology/products/webservices_manager • http://ws-security.blogspot.com (Blog) • http://blogs.oracle.com/davidchappell/ (Blog) 60 24

25. Questions, Feedback, Use Cases • Mohamad Afshar • Mohamad.afshar@oracle.com • Dave Chappell • David.Chappell@oracle.com 61 62 25

26. <Insert Picture Here> Appendix 63 More Patterns Available • Claim Check / Pass-by-ref / Deferred State • Stateful Service Load Balancing • Stateful Service Availability/Failover • State Based Notification • Asynch DB Update • Near Cache Optimization • Sticky Load Balancers and Cached Service State • Business Logic Affinity 64 26

27. For More Information • Dave Chappell Blog – • http://blogs.oracle.com/davidchappell • SOA Magazine – SOA – Ready for Primetime: The Next Generation, Grid Enabled SOA • http://www.soamag.com/I10/0907-1.asp • SOA Magazine –The Next Generation, Grid Enabled SOA : Not Your MOM‟s Bus • http://www.soamag.com/I14/0108-1.asp • XTPP – Gartner Research ID # G00151768 – Massimo Pezzini • Data Grid – • http://www.oracle.com/products/middleware/coherence/index.html • General SOA Information – http://www.oracle.com/technologies/soa/index.html • http://www.oracle.com/soa • SOA Best Practices: The BPEL Cookbook 65 Industry Coverage …the result is almost linear scalability… …from 2 million to more than 60 million “aggregations” per second… (based on a benchmark initiated by an investment-bank) http://www.gridtoday.com/grid/1142166.html February 2007 Top 10 Product in Network World Next Generation Data Center Product Review ‟With Coherence, performance has improved by as much as 100 times …‟ Network World, March 2007 66 27

28. Sample Customers Over 1,000 production installations! 67 <Insert Picture Here> How do you make this happen? 68 28

29. Oracle SOA Suite BPA Suite REAL-TIME BAM GOVERNANCE CEP Enterprise Manager VISIBILITY & PROCESSING Enterprise Alerts Events System Modeling Business Monitoring GOVERNANCE Monitoring ORCHESTRATIONStreams Data BPM Suite BPEL Process Manager Web Services Manager Business User Native Business Human WS Policies Modeling BPEL ROUTING &Rules SERVICES DATA Workflow Security JDeveloper Oracle Service Bus Data Integrator Enterprise Repository Application Routing Transform ETL & Data Development Replication Quality SOA lifecycle Mediation CONNECTIVITY governance Framework Adapters B2B Registry Apps DB Legacy Partners UDDI Coherence Cache Messaging J2EE Application Server JRockit VM & RT (Oracle AS, WebLogic, WebSphere, JBoss) 69 Oracle Coherence Ever Expanding Universe of Users • Oracle Coherence Web Servers 101100010110010111011001011001011100011101111110001110 brokers Data Supply with Application 10110001011001011101100101100101110001110 Servers Data Demand Data Demand • Scale out Data Grid in middle tier using Java Objects commodity hardware WebLogic Suite Data Supply Data Sources 70 29

30. JRockit Real Time JRockit -- the Industry‟s FASTEST Real Time solution for Standard Java • Deterministic, Extreme Low Response JVM • Deterministic Garbage Collection with 1ms guarantee • Under heavy load • Design goal – Pause less!! JRRT Sun Java • „Swap out‟ any JVM with Zero- coding for instantaneous GC Response times under fixed load predictability 60 • No code re-writes 50 40 response time (ms) • Advanced tools for monitoring and JRRT Det GC JRockit 30 Java Sun VM Tuned tuning application performance 20 10 0 99.9 pctl 99 pctl 95 pctl 80 pctl median average 71 WebLogic Operations Control Monitoring and Service Management Dynamic Resource Allocation • SLAs by way of WLOC policies and rules • Constant rules evaluation • Dynamic scale management Protects app availability • Failover within the pool • Failover across pools • Rules can protect performance as well as availability Active monitoring, alerting • What WLOC starts, it monitors, protects • Agent-based framework 72 30

31. WL EM Diagnostic Packs Management and Monitoring • Manage multiple WebLogic domains from a single console • 24x7 monitoring of availability, performance, load, and usage metrics of WebLogic Server and the host • Blackouts, events, notifications, and metrics comparison • Real-time and historical performance monitoring • Historical trends of metrics and events • Reports • End User Monitoring • System/Service Modeling and Dashboards 73 Oracle Fusion Middleware 74 31

32. Learn More http://www.oracle.com/technology/products/ • Whitepapers • Webcasts • Buyers Guides • Analyst Reports • Case Studies • Podcasts • Technical Information & Forums • http://www.oracle.com/technology/products/webservices_manager • http://ws-security.blogspot.com (Blog) • http://blogs.oracle.com/davidchappell/ (Blog) 75 Questions, Feedback, Use Cases • Mohamad Afshar • Mohamad.afshar@oracle.com • Dave Chappell • David.Chappell@oracle.com 76 32

33. For More Information • Dave Chappell Blog – • http://blogs.oracle.com/davidchappell • SOA Magazine – SOA – Ready for Primetime: The Next Generation, Grid Enabled SOA • http://www.soamag.com/I10/0907-1.asp • SOA Magazine –The Next Generation, Grid Enabled SOA : Not Your MOM‟s Bus • http://www.soamag.com/I14/0108-1.asp • XTPP – Gartner Research ID # G00151768 – Massimo Pezzini • Data Grid – • http://www.oracle.com/products/middleware/coherence/index.html • General SOA Information – http://www.oracle.com/technologies/soa/index.html • http://www.oracle.com/soa • SOA Best Practices: The BPEL Cookbook 77 <Insert Picture Here> Appendix Appendix Extra Slides 78 33

34. What does it mean to get it right? transact ions per second Linear Scale up Latency What happens in practice What happens in practice CPUs Predictable latency Time So what do you do when you don‟t get it right? -Revisit how the application was architected -Throw hardware at the problem OR -Make sure you architect it so that it‟s scalable first time around! 79 JRockit High Speed JavaVM with Predictable Performance Traditional Java VM – Stop the World Pauses External Interfaces Memory & Resource Management JRockit - < 10 ms Pause I/O Thread Mgmt. Language Model Code Generation 80 34

35. WebLogic Server Java Enterprise Platform Services Component Architecture Fabric JPA Web JavaServer Security (TopLink) Services Faces WebLogic Server (Java EE 5.0) Clusters, Messaging, Transactions, Workload Mgt, JMX WebLogic Operations OSGI Microkernel Control 81 Coherence Reliable, Transactional, In-Memory Data Grid Better Throughput & Lower Latency Java, JavaEE, .NET & Other Applications Guaranteed Zero Data Loss Dynamic Database Access & O-R Mapping Capacity Addition Efficient Data Access 82 35

36. Oracle Service Bus Unmatched Performance & Scalability 22.7 Million Messages Per Hour • Oracle Service Bus 10gR3 • Intel Tigerton Processors • Oracle Enterprise Linux 5.0 • 4X2.93GHz Quad Core Processors • 6 GB RAM Per Server • Number of Process Steps/Instance = 2 • Number of Transformations = 1 • Size of Document Payload = 5 KB 83 Oracle BPEL Process Manager Unmatched Performance & Scalability 3.5 Million Process Instances Per Hour • Oracle BPEL Process Manager 10gR3 • Intel Tigerton Processors • Oracle Enterprise Linux 5.0 • 4X2.93GHz Quad Core Processors • 6 GB RAM Per Server • Number of Process Steps/Instance = 8 • Number of Transformations = 4 • Size of Document Payload = 10 KB 84 36

37. Oracle AIA for Communications 2.0 Leverages BPEL Performance & Scalability 185,400 Business Transactions Per Hour • Oracle BPEL Process Manager 10gR3 • Siebel CRM 7.8.2; Oracle BRM 7.3 • Intel Xeon Processors • 2x3.6 GHz Processor • 4 GB RAM Per Server • Windows 2003 Service Pack 1 • Size of Document Payload = 3 KB • Number of XML Schema Elements = 62 85 Oracle Complex Event Processor Unmatched Performance & Scalability 1 Million Events Processed per Second • <1 Millisecond Latency for Events • 1000s of Rules & Event Queries • Events from Java, Queues, DBMS, SOA • Oracle Event Server 10gR3 • Oracle Enterprise Linux 5.0 • 4X2.93GHz Quad Core Processors • 6 GB RAM Per Server 86 37

38. Get Customer Activities *Pfizer - OOW2008 S299392 - Beyond Performance: Pushing Transaction… 87 *Find Customer BPEL Process • Web 2.0 apps demand real-time SOA • Response time limited by slowest system in a real-time SOA / BPEL flow • Ask yourself are you getting the benefit of the real-time call? If not, why pay SOA tax? • Do work in advance by pre-populating the data grid with data • If possible cache the entire results / output of the flow • Cache results to service calls as a façade that are part of the flow • Reduce amount of work at run-time • This is easy to put together with BPEL PM and Coherence *Pfizer - OOW2008 S299392 - Beyond Performance: Pushing Transaction… 88 38

39. Before | Service Calls to Backend Systems Service MQ Provider (Transform, Route, Retry) ESB Mediation Service Provider Legacy Service Consumer Process Flow / Orchestration Healthcare Portal: Minimizing expensive backend access 89 Traditional SOA Stateless/State Passing Model Service to Service Carry XML From Svc D 1 2 3 4 Service Bus / BPEL 5 Stateless Stateless Stateless Transform Svc A Svc B Svc C & Route State Table State Table State Table Svc A Svc B Relational Store Svc C SQL SQL (Oracle DB) SQL 90 39

40. Shared State Repository Implement the “Claim Check” Pattern Svc D 1 2 3 4 Service Bus / BPEL 5 Transform Svc A Svc B Svc C & Route State Table State Table State Table Svc A Svc B Relational Store Svc C SQL SQL (Oracle DB) SQL 91 Shared Service State Repository RevisitedData Grid w/ Async Write-Behind, Sync Write-thru, In Memory Cached-Read Service Bus / BPEL Svc D 1 2 3 4 5 Transform Svc A Svc B Svc C & Route Svc A Relational Store Svc B Svc C SQL (Oracle DB) SQL SQL 92 40

41. SOA on App Grid Case Study Web-Tier 94 The Universal Experience: Sister Tabs *GAP - OOW2008 S299392 - Beyond Performance: Pushing Transaction… 95 41

42. The Universal Experience: Shopping Cart *GAP - OOW2008 S299392 - Beyond Performance: Pushing Transaction… 96 State Management in SOA on App Grid | Web Cache Shared Servlet Session State EJB Business logic: Traditional middle tier EJB Web Cntr POJO Data Grid: Web Containers share servlet session state via data grid DB Grid EJB Database: Web Cntr Use for storing, managing transactions .NET *GAP 97 42

43. State Management in SOA on Application Grid | Shared Services Primary Node Service Business logic: Component Services manage their state Web Cntr in the data grid Service Data Grid: Component Use for managing transient state (read and write) DB Grid Service Web Cntr Component Database: Use for storing, managing transactions Write through for state information from data grid Service Component = Write Behind Queue Similar architecture, similar benefits! 98 SOA on App Grid Case Study Caching Middle Tier, Offloading Backend Systems 99 43

44. <Insert Picture Here> Kiran Dattani Director SOA & Enterprise Integration Pfizer Worldwide Technology “It’s hard to beat zero!” 100 After | Caching Results to Service Calls to Backend Systems Service MQ Provider (Transform, Route, Retry) ESB Mediation Service Provider Legacy Service Cache Primary Consumer Node Put (State Data) P Side- CacheKey Cache Service Backup Node B Process Flow / Orchestration Healthcare Portal: Minimizing expensive backend access 101 44

45. After | Caching Results to Service Calls to Backend Systems Service MQ Provider (Transform, Route, Retry) ESB Mediation Service Provider Legacy Service Cache Primary Consumer Node Get(Cachekey) P Side- State Data Cache Service Backup Node B Process Flow / Orchestration Healthcare Portal: Minimizing expensive backend access 102 After | With Fault Tolerance Service MQ Provider (Transform, Route,, Retry) ESB Mediation Service Provider Legacy Service Cache X Consumer Primary Node Get(Cachekey) P Side- State Data Cache Service Backup Node B Process Flow / Orchestration Healthcare Portal: Minimizing expensive backend access 103 45

46. Side-Cache in OSB (ALSB) Proxy / Pipeline (Telco Use Case) 104 Here today: Usage Patterns with Oracle Coherence and ESB/BPEL 46

David Chappel S O A Grid

Esté a ler uma amostra.

Confira estes a seguir

David Chappel S O A Grid

Mais Conteúdo rRelacionado

Semelhante a David Chappel S O A Grid (20)

Mais de SOA Symposium (20)

Mais recentes (20)