<Insert Picture Here> Oracle Cloud Strategy: Oracle s Vision for Next- Generation Application Grid, Virtualization and Cloud Paolo Ramasso Master Principal Sales Consultant Oracle Italy
Business Imperatives and Challenges Efficiency: expand despite constraints Flexibility&Agility: change course quickly Quality of service: rise above the competition 4/27/2010 2008 Oracle Corporation 3 The Old Way
Grid Computing Virtualizes and Pools IT Resources Getting Started with Cloud
What s Different about Cloud? Users expect a cloud infrastructure to support*: 1. The illusion of infinite computing resources available on- demand Capacity always needs to be there through automation and proactive operations before users perceive a constraint ( infinite ) Users need to be able to self-serve ( on-demand ) 2. The elimination of up-front commitment by users Fine-grained, actual usage/allocation-based chargeback rather than purchase ahead of time ( no up front commitment ) 3. The ability to pay for use of computing resources on a shortterm, as-needed basis Dynamic capacity management scale up or down ( short-term / no commitment ) *Paraphrasing UC Berkeley Reliable Adaptive Distributed Systems Laboratory (http://radlab.cs.berkeley.edu/) 2009 Oracle Proprietary and Confidential 7 SaaS, PaaS and IaaS Software as a Service Applications delivered as a service to end-users over the Internet Platform as a Service App development & deployment platform delivered as a service Infrastructure as a Service Server, storage and network hardware and associated software delivered as a service
Public Clouds and Private Clouds Used by multiple tenants on a shared basis Hosted and managed by cloud service provider Limited variety of offerings Public Clouds SaaS PaaS IaaS Public Clouds: Lower upfront costs Economies of scale Simpler to manage I N T E R N E T Both offer: High efficiency High availability Elastic capacity Private Cloud I N T R A N E T Users SaaS PaaS IaaS Exclusively used by a single organization Controlled and managed by in-house IT Large number of applications Private Cloud: Lower total costs Greater control over security, compliance & quality of service Easier integration Build Your Own Private PaaS Clouds with Oracle Technology Self-Service Provisioning The Grid Resources that are Distributed Scalable Highly Available Mid-Tier Server Clusters Grid&Virtualization Adding Flexibility Agility through virtualization Virtual Machines Physical Pools Across the stack On demand metering Billing Applications Middleware Database OS Virtualization Physical Pools Real Application Clusters
1: Leverage Application Server Build foundation for efficiency and scalability App Svr App Svr App WLS Svr App WLSvr App WLSvr App WLS Svr App WLS Svr App WLS Svr App WLSvr App WLS Svr Possible actions: Use application server clustering for scale-out Consolidate to WebLogic Server (or Tuxedo for C/C++/COBOL) Use scripting to automate scaling 4/27/2010 2008 Oracle Corporation 11 2: Enhance Scalability and Performance Make your infrastructure more dynamic and resilient App Svr App Svr App Svr App Svr App Svr App Svr App Svr App Svr Coherence Coherence JRockit JRockit Coherence Coherence Possible actions: Add Coherence in-memory data grid to existing machines Add Coherence nodes on non-app-server machines Add JRockit Real Time JVM 4/27/2010 2008 Oracle Corporation 12
3:Enhance Agility Adding Virtualization Application 1 Application 2 Application 3 Integration: SOA Suite Platform as a Service Shared Services Process Mgmt: BPM Suite Security: Identity Mgmt User Interaction: WebCenter Application Grid: WebLogic Server, Coherence, Tuxedo, JRockit Database Grid: Oracle Database, RAC, ASM, Partitioning, IMDB Cache, Active Data Guard, Database Security Infrastructure as a Service Operating Systems: Oracle Enterprise Linux Virtualization: Oracle VM Servers Storage Cloud Management Oracle Enterprise Manager Configuration Mgmt: Discovery, Gold Templates, Change Detection, Rollback, Compliance Lifecycle Management: Provisioning, VM Templates, Large-Scale Automation Application Performance Management: RUEI, SLA Managment, Monitoring, Diagnostics Application Quality Management: Testing, Patch Management 2009 Oracle Proprietary and Confidential 13 Oracle Virtual Assembly Builder Package Multi-Tier Applications Oracle SOA Suite Oracle BPM Suite Oracle WebCenter Oracle Identity Mgt Oracle WebLogic Suite-based Application Grid Oracle Database Introspection & Assembly Oracle VM Template Builder Assembly Builder OVF Packaging Deployment Enterprise Manager Oracle VM Manager Virtualized Software Appliances Application A Application B Assembly A Assembly B Oracle VM Server
Oracle Virtual Assembly Builder Tool Assemblies, Appliances Catalog Properties Inspector Deployment Resource Pools Assembly Editor Private PaaS Lifecycle 3. Use App 2. Build App Assemble app using shared components Deploy through self-service 1. Set Up Cloud IT Set up PaaS Set up shared components Set up selfservice portal App Developer App Shared Components App Users Oracle Fusion Middleware Oracle Database Oracle Enterprise Linux Oracle VM 4. Scale up/down Adjust capacity based on policies Monitor via selfservice Self-Service Interface App Owner 5. Chargeback Oracle Enterprise Manager Meter usage and charge back to app owners or departments
Cloud Computing with Oracle Fusion Middleware JRockit GC Latency Management 120 105 Traditional Java JRockit Real Time 90 75 60 45 30 15 0 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 During Low Load: GC spikes and occasional timeouts visible 120 105 90 75 60 45 30 15 0 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 JRRT Makes garbage collection deterministic. Allowing for the guarantee of SLAs. During High Load: GC pauses can result in unacceptable response times
Traditional Scale-Out Approaches #1. Avoid the challenge of maintaining consensus Opt for the single point of knowledge Client + Server Model (Hub + Spoke) Active + Passive (High Availability) Master + Worker Model (Grid Agents) #2. Have crude consensus mechanisms, that typically fail and result in data integrity issues (including loss) Oracle Coherence Data Grid Distributed in Memory Data Management Enterprise Applications Real Time Clients Data Services Web Services Oracle Coherence Data Grid Provides a reliable data tier with a single, consistent view of data Enables dynamic data capacity including fault tolerance and load balancing Ensures that data capacity scales with processing capacity Databases Mainframes Web Services
The Coherence Approach Traditional scale-out approaches limit Scalability, Availability, Reliability and Performance In Coherence Servers share responsibilities (health, services, data ) No SPoB No SPoF Massively scalable by design Logically servers form a mesh No Masters / Slaves etc. Members work together as a team Data Grid Uses Caching Applications request data from the Data Grid rather than backend data sources Analytics Applications ask the Data Grid questions from simple queries to advanced scenario modeling Transactions Data Grid acts as a transactional System of Record, hosting data and business logic Events Automated processing based on event
Coherence in the Application-Tier Coherence in the Application Tier:.NET/C++/Java (Wan) Clients OAS, WebSphere, JBoss OAS, WebSphere, JBoss
Partitioned Topology : Data Access Data Access Topologies Coherence provides many Topologies for Data Management Local, Near, Replicated, Overview, Disk, Off-Heap, Extend (WAN), Extend (Clients) Partitioned Topology Data spread and backed up across Members Transparent to developer Members have access to all Data All Data locations are known no lookup & no registry! Partitioned Topology : Data Update Partitioned Topology Synchronous Update Avoids potential Data Loss & Corruption Predictable Performance Backup Partitions are partitioned away from Primaries for resilience No engineering requirement to setup Primaries or Backups Automatically and Dynamically Managed
Partitioned Topology : Recovery Partitioned Topology Membership changes (new members added or members leaving) Other members, in parallel, recover / repartition No in-flight operations lost Some latencies (due to higher priority of recovery) Reliability, Availability, Scalability, Performance are the priority Degrade performance of some requests Partitioned Topology Deterministic latencies for data access and update Linearly scalable by design No TCP/IP connections to create / maintain No loss of in-flight operations while repartitioning No requirement to shutdown cluster to recover from member failure add new members add named caches No network exceptions to catch during repartitioning Dynamic repartitioning means scale-out on demand
Partitioned Topology : Local Storage Partitioned Topology Some members are temporary in a cluster They should not cause repartitioning Repartitioning means work for the other members (and network traffic) So turn off storage! Topology Composition : Near Topology Coherence allows Topologies to the Composed Base Topologies Local, Replicated, Partitioned / Distributed, Extend Composition Topologies Near, Overflow Near Topology Compose a Front and a Back Topology Permit L1 and L2 caching Both Front and Back may be completely different Both may have different Expiry Policies Expiry Policies LRU, LFU, Hybrid, Seppuku, Custom
Topology Composition : Near Topology PIF/POF POF (Portable Object Format) is the language independent serialization format used by Coherence*Extend PIF (Portable Invocation Format) is a language independent remote method invocation. All Coherence*Extend protocol messages are PIFs (e.g. GetRequest) which are nothing more than a POF preceeded by a conversation identifier (a POF integer value) Credit for the name belongs to Gene Gleyzer: The phonetic origin of PIF-POF is from Russian language comic strips, in which piff-paff (pronounced peef-puff ) is the equivalent of the English bang-bang the sound of a gun.
Features : Traditional Implements Map interface Drop in replacement. Full concurrency control. Multithreaded. Scalable and resilient! get, put, putall, size, clear, lock, unlock Implements JCache interface Extensive support for a multitude of expiration policies, including none! More than just a Cache. More than just a Map Features : Observable Interface
Full Example // listen for insert events on Person // This can be done in an easier way by using a new AbstractMapListener() // and then overriding only the method you want to // person.addmaplistener(new MapListener() { } ); public void entrydeleted(mapevent mapevent) { } // ignore public void entryinserted(mapevent mapevent) { } Person p = (Person)mapEvent.getNewValue(); System.out.println("New person added: " + p.getfirstname() + " " + p.getsurname()); public void entryupdated(mapevent mapevent) { } // ignore Features : QueryMap Interface
Features : QueryMap Interface Find Keys or Values that satisfy a Filter. entryset( ), keyset( ) Define indexes (on-the-fly) to extract and index any part of an Entry Executed in Parallel Create Continuous View of entries based on a Filter with real-time events dispatch Perfect for client applications watching data Features : InvocableMap Interface
Features : InvocableMap Interface Execute processors against an Entry, a Collection or a Filter Executions occur in parallel (aka: Grid-style) No workers to manage! Processors may return any value trades.invokeall( new EqualsFilter( getsecurity, ORCL ), new StockSplit(2.0)); Aggregate Entries based on a Filter positions.aggregate( new EqualsFilter( getsecurity, ORCL ), new SumFilter( amount )); Coherence as a Separate Tier with FMW Firewall Firewall Firewall External Users Internal Users WebCenter IDM Internet Router Java EE Web Tier RAC Internal Users Internal Users SOA Coherence Data Grid Service
Maximum Availability Architecture Active/Active Active Data Center 1 Firewall Global Router Active Data Center 2 Firewall Web Tier Web Tier Firewall Firewall WebCenter SOA J2EE Low Latency High Bandwidth WAN WebCenter SOA J2EE Firewall IDM Coherence Data Grid Service Passive RAC OracleAS Guard RAC IDM Firewall Oracle WebLogic Server The Number #1 Java EE application server, designed for the most Mission-Critical of applications Developer-friendly productive, standards-based development Focus on quality of service performance, scalability, reliability, availability Built-in manageability configuration, monitoring, diagnostics, maintenance WebLogic Server Clusters WebLogic Application Grid Legacy Commodity Databases Virtualized Mainframes
WebLogic Server Virtual Edition: Less Is More Management Simplicity with Performance and Utilization WLS-VE WebLogic Server APP JRockit VE WebLogic JRockit OS Server APP WebLogic APP WebLogic JRockit OS Oracle VM APP WebLogic JRockit OS APP WLS LVM VE APP WLS LVM VE APP WLS LVM VE Oracle VM APP WLS LVM VE JRockit OS Server Server WebLogic Server Virtual Edition Value Proposition Management Simplicity Eliminate requirement for provisioning and managing Guest Operating Systems Only application administration, no separate OS administration Assembly Builder delivers simplified deployment of entire domain onto virtualized resources Higher Performance Tailored to run Java (Only the bare minimum of OS services needed for java) Optimized to run on Virtual Platforms Better physical hardware utilization Eliminating the OS reduces consumption of system resources such as memory and CPU cycles WLS-VE WebLogic Server JRockit VE
New for 11g: ActiveCache client Direct integration with HTTP container and TopLink ORM Use standard JavaEE APIs Ideal for applications with large data objects memory constraints volatile scaling needs Foundation of Application Grid performance and reliability Easy to use, deploy, manage Engine Tier WLS WLS WLS Coherence*Web Coherence*Web Coherence*Web Transient Data Tier Coherence Coherence Coherence Coherence Coherence Typical WebLogic Server Cluster Load Balancer HTTP/JSP Sessions WebLogic Server EJB JDBC HTML Oracle Web Tier Web Cache HTTP/JSP Sessions WebLogic Server EJB JDBC RDBMS HTTP/JSP Sessions EJB JDBC WebLogic Server Application Server Tier
WebLogic Server with Coherence*Web Separate traffic processing, session management Load Balancer Servlet EJB WebLogic Server JDBC Sessions and Cache Coherence Sessions and Cache Coherence HTML Oracle Web Tier Web Cache RDBMS Servlet EJB WebLogic Server JDBC Sessions and Cache Coherence Sessions and Cache Coherence Engine Tier State Tier New in 11g: GridLink for RAC Support for Oracle RAC Services New supported configuration for Oracle Database Services Based on existing WebLogic Multi DataSource support One DataSource for each RAC instance Simplify administration when using DB Services DataSources for activated/deactivated as RAC Services reconfigured No App Server/DB Admin synchronization requires Load balancing, failover, transactions supported Same as in WebLogic Server 10.3 and prior releases Managed Server Multi Data Source (CRM) WebLogic Domain WebLogic Cluster Managed Server Multi Data Source (PAY) DS1 DS2 DS3 DS1 DS2 DS3 CRM CRM PAY RAC Node 1 Shared Storage RAC Node 2 Oracle RAC Cluster RAC Node 3
Enterprise Grid Messaging in 11gR1 Oracle AQ JMS Integration Oracle AQ JMS implementation Messaging implementation based on Oracle DB Standard JMS API with messages persisted to Oracle DB Can leverage DB features (triggers, stored procedures) Used widely in Oracle installed base Oracle Fusion Middleware 10gR3 OC4J/Oracle Application Server, BPEL PM, SOA Suite AQ JMS API JDBC Oracle AQ DBMS Store Self-Tuning and Work Managers WebLogic's Self-Tuning Thread Pool Network Socket Handlers ( Muxers ) Request Queue Self Tuning Thread Pool Active Standby Stuck Hogging Asynchronously dispatched work from WebLogic kernel, subsystem, or application 1. Monitor rate of request processing 2. Adjust thread pool size accordingly
High Availability Applications Zero Down Time Application Deployment Application versions run side-by-side in same JVM Controlled test mode and automated rollback Automatic retirement: graceful quiescence or timeout Ensures continuity of in-flight transactions Existing External Client Connections New Application Version New External Client Connections Retiring Application Version Managed WebLogic Server Single Java VM Administrative Test Client Connections Test First in Administrative Mode Lightweight, Testable Plain Java Objects Less configuration complexity Easier testability and alignment with OS tools Ant / Maven / Hudson Java EE 6.0 makes advances in this area (CDI) Context and Dependency Injection EJB3 JPA JSF 2.0 (Beans, etc) Java EE will only continue to improve Presentation Dependency Injection Basic Component Persistence JSF2 Facelets JSR 330 Context Dependency & Injection Managed Beans JPA 2 EJB 3.1 JAX-RS JAX-WS Business Services REST SOAP 2010 Oracle Corporation 52
The Persistence RI for Java EE 6: Java SE Java EE OSGi Spring ADF JPA MOXy EIS SDO DBWS Eclipse Persistence Services Project (EclipseLink) Databases XML Data Legacy Systems 2010 Oracle Corporation 53 TopLink Grid: Removing Database Bottlenecks Larger application clusters can lead to database bottlenecks with traditional JPA scaling approaches like: Adding nodes to a cluster Tuning database performance to reduce query time Prior to TopLink Grid, there were two strategies for scaling JPA applications into a cluster: Disable Shared Cache Cache Coordination communicate changes via messaging App Tier Clusters Data Tier Database 2010 Oracle Corporation 54
TopLink Grid with Coherence Cache Application Application EntityManager L1 Cache EntityManager L1 Cache EntityManagerFactory EntityManagerFactory Coherence 2010 Oracle Corporation 55 TopLink Grid Grid Cache Ensures all nodes have coherent view of data. Database is always right Shared Cache is always right Entities read, modified, or created are available to all cluster members. Updates no longer cost n 2 as not all members are messaged minimum communication is to primary and backup nodes. Coherence cache size is the sum of the available heap of all members larger cache size enables longer tenure and better cache hit rate Can be used with existing applications and all EclipseLink performance features without altering application results
Grid Cache Reading Objects 1. Queries are performed using JPA em.find(..) or JPQL. 2. A find() will result in a get() on the appropriate Coherence cache. If found, Entity is returned. 3. If get() returns null or query is JPQL, the database is queried with SQL. 4. The queried Entities are put() into Coherence and returned to the application. Grid Cache Writing Objects 1. Applications persist Entities using standard JPA and commit a transaction. 2. The new and/or updated Entities are inserted/updated in the database and the database transaction committed. 3. If the database transaction is successful the Entities are put() into Coherence which makes them available to all cluster members.
Grid Read In the Grid Cache configuration, all reads (both pk and non-pk) are executed against the grid (by default). For Entities that typically: Need to be highly available Must have updates written synchronously to the database; database is system of record Features: Database is always correct committed before grid updated Supports all EclipseLink performance features (including batch writing, parameter binding, stored procedures, and statement ordering). High performance parallel JP QL query execution Can be optionally used with CacheLoader. Grid Read Reading Objects 1. Queries are performed using JPA em.find(..) or JPQL. 2. JQPL will be translated to a Coherence Filter and used to query results from Coherence. A find() will result in a get() on the appropriate Coherence cache. The database is not queried by EclipseLink. If Coherence is configured with a CacheLoader then a find() may result in a SELECT, but JQPL will not.
Grid Read Writing Objects 1. An application commits a transaction with new Entities or modifications to existing Entities. 2. EclipseLink issues the appropriate SQL to update the database and commits the database transaction. 3. Upon successful commit, the new and updated Entities are put() into Coherence. Grid Entity The Grid Entity configuration is the same as the Grid Read configuration except that all writes are executed against the grid, not the database. With this configuration Coherence is effectively the "system of record" as all Entity queries are directed to it rather than the database. For Entities that typically: May have updates written asynchronously to the database (if CacheStore configured) Features: Can be optionally used with CacheStore to update the database. Database will not be up to date until Coherence flushes changes through CacheStore Will not benefit from EclipseLink performance features such as batch writing
Grid Entity Reading Objects (Same as Grid Read) 1. Queries are performed using JPA em.find(..) or JPQL. 2. JQPL will be translated to a Coherence Filter and used to query results from Coherence. A find() will result in a get() on the appropriate Coherence cache. The database is not queried by EclipseLink. If Coherence is configured with a CacheLoader then a find() may result in a SELECT, but JQPL will not. Grid Entity Writing Objects 1. An application commits a transaction with new Entities or modifications to existing Entities. 2. EclipseLink put()s all new and updated Entities into Coherence. 3. If a CacheStore is configured, Coherence will synchronously or asynchronously write the changes to the database, depending on configuration.
Demonstrations Toplink Grid for Scaling out JPA Oracle&Sun
Java Runtime Java SE Rapidly deliver Java SE 7 with many new features Modularization Developer productivity Multiple languages Higher performance Support for Multi-Core Processors HotSpot and JRockit are strategic JVMs Converge best features of HotSpot and JRockit Management and Real-time Monitoring Run natively on Hypervisors Optimize/Remove Permgen Thread Local, Server Class Garbage Collection NUMA Compiler Optimization for Multi-Cores Continued support for all leading OSs Java Application Server Java EE Evolve current Java EE RI to further address key initiatives Modularity with Open Standards New Lightweight Server Profiles UI and Rich Internet Applications Glassfish and WebLogic are strategic Application Servers Glassfish remains Java EE Reference Implementation WebLogic remains strategic Enterprise Application Server No change in support timelines or distribution model for Glassfish