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Oracle Cloud Strategy: Oracle’s Vision for Next-Generation Application Grid, Virtualization and Clou d

Paolo RamassoMaster Principal Sales Consultant Oracle Italy

Business Imperatives and Challenges

Efficiency: expand despite constraints Flexibility&Agility: despite constraints Flexibility&Agility:

change course quickly

Quality of service: rise above the competition

4/27/2010 3©2008 Oracle Corporation

The Old WayThe Old Way

Grid Computing Virtualizes and Pools IT Resources

Getting Startedwithwith

Cloud

What’s Different about Cloud?

Users expect a cloud infrastructure to support*:

1. The illusion of infinite computing resourcesinfinite computing resources available onon--demanddemand• 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 short-term, as-needed basis• Dynamic capacity management scale up or down (“short-term /

no commitment”)

*Paraphrasing UC Berkeley Reliable Adaptive Distrib uted Systems Laboratory (http://radlab.cs.berkeley. edu/)

© 2009 Oracle – Proprietary and Confidential 7

SaaS, PaaS and IaaS

Applications delivered as a service to end-users over the Internet

Software as a Service

App development & deployment

Infrastructure as a Service

Platform as a ServiceApp development & deployment platform delivered as a service

Server, storage and network hardware and associated software delivered as a service

Public Clouds and Private Clouds

INTERNET

Public Clouds

IaaS

PaaS

SaaSINTRANET

Private Cloud

• Used by multiple tenants on a shared basis

• Hosted and managed by cloud service provider

• Exclusively used by a single organization

• Controlled and managed byin-house IT

• Large number IaaS

PaaS

SaaS

T T

Users

Public Clouds:• Lower upfront costs• Economies of scale• Simpler to manage

Private Cloud:• Lower total costs• Greater control over security,

compliance & quality of service• Easier integration

Both offer:• High efficiency• High availability• Elastic capacity

provider

• Limited variety of offerings

• Large number of applications

Build Your Own Private PaaS Clouds with Oracle Technology

Adding…

• Flexibility

• Agility

…through virtualization

• Across the stack

On demand

• metering

• Billing

The Grid

Grid&Virtualization

Self-Service Provisioning

Applications

Resources that are…

• Distributed

• Scalable

• Highly Available

…through virtualizationThe Grid

Real Application Clusters

Mid-Tier Server ClustersPhysical Pools

Virtual Machines

Physical Pools

Virtualization

OS

Database

Middleware

WLSWLS

WLS

WLS WLS

WLS

WLS

WLS

App Svr App Svr

App Svr

App Svr

App Svr

App Svr

App Svr App Svr

App Svr

App Svr

1: Leverage Application ServerBuild foundation for efficiency and scalability

4/27/2010 ©2008 Oracle Corporation 11

•Use application server clustering for scale-out

Possible actions:

•Consolidate to WebLogic Server (or Tuxedo for C/C++ /COBOL)

•Use scripting to automate scaling

CoherenceCoherence

CoherenceCoherence

JRockit JRockit

2: Enhance Scalability and PerformanceMake your infrastructure more dynamic and resilient

App Svr App Svr

App Svr

App Svr

App Svr

App Svr App Svr

App Svr

CoherenceCoherence

4/27/2010 ©2008 Oracle Corporation 12

•Add Coherence in-memory data grid to existing machi nes

Possible actions:

•Add Coherence nodes on non-app-server machines

•Add JRockit Real Time JVM

3:Enhance Agility Adding Virtualization

Application 1 Application 2 Application 3

Application Grid: WebLogic Server, Coherence, Tuxedo, JRockit

Shared Services

Integration:SOA Suite

Security:Identity Mgmt

Process Mgmt:BPM Suite

User Interaction:WebCenter

Platform as a Service Cloud Management

Lifecycle Management:

Configuration Mgmt:Discovery, Gold Templates,

Change Detection,Rollback, Compliance

Oracle Enterprise Manager

© 2009 Oracle – Proprietary and Confidential 13

Virtualization: Oracle VM

Operating Systems: Oracle Enterprise Linux

Servers

Storage

Database Grid: Oracle Database, RAC, ASM, Partitioning,IMDB Cache, Active Data Guard, Database Security

Infrastructure as a Service

Application QualityManagement:

Testing,Patch Management

Application PerformanceManagement:

RUEI, SLA Managment,Monitoring, Diagnostics

Lifecycle Management:Provisioning,

VM Templates,Large-Scale Automation

Oracle Virtual Assembly Builder Package Multi-Tier Applications

Oracle WebLogic Suite -based Application Grid

Oracle WebLogic Suite-based Application Grid

Oracle SOA Suite

Oracle BPM Suite

Oracle WebCenter

Oracle Identity

Mgt

Oracle Database

Introspection&

Application A Application B

Virtualized

Enterprise M

anager

Assembly Builder

Assembly Builder

Deployment

& Assembly

Oracle VMTemplateBuilder

OVF Packaging

Oracle VM Server

VirtualizedSoftware

Appliances

Enterprise M

anagerO

racle VM

Manager

Assembly A Assembly B

Oracle Virtual Assembly Builder Tool

Assemblies,Appliances

Catalog

PropertiesInspector

Deployment Resource

Pools

Assembly Editor

Private PaaS Lifecycle

App Developer

App Users

2. Build App

3. Use App

4. Scale up/down

App

• Assemble app using shared components

• Deploy through self-service

• Adjust capacity based on policies

• Monitor via self-service

App Owner 5. Chargeback• Meter usage

and charge

Self-Service InterfaceShared Components

• Set up PaaS• Set up shared

components• Set up self-

service portal

IT

1. Set Up Cloud

and charge back to app owners or departments

Oracle VM

Oracle Enterprise Linux

Oracle Database

Oracle Fusion Middleware

Oracle Enterprise Manager

Cloud Computing with Oracle with Oracle

Fusion Middleware

JRockit GC Latency Management

0

15

30

45

60

75

90

105

120

0 2000 4000 6000 8000 10000 12000 14000 16000 18000

During Low Load: GC spikes and occasional timeouts visible

Traditional JavaJRockit Real Time

and occasional timeouts visible

During High Load: GC pauses can result in unacceptable response times

0

15

30

45

60

75

90

105

120

0 2000 4000 6000 8000 10000 12000 14000 16000 18000

JRRT Makes garbage collection deterministic. Allowing for the guarantee of SLAs.

Traditional Scale-Out Approaches…

#1. Avoid the challenge of maintaining consensus• Opt for the “single point of knowledge”

#2. Have crude consensus mechanisms, that typically fail and result in data integrity issues (including loss)

Client + Server Model(Hub + Spoke)

Master + Worker Model(Grid Agents)

Active + Passive(High Availability)

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

Enterprise Applications

Real TimeClients

WebServices

Oracle Coherence

Data Services

Distributed in Memory Data Management

including fault tolerance and load balancing

• Ensures that data capacity scaleswith processing capacity

MainframesDatabases Web Services

Oracle CoherenceData Grid

The Coherence Approach…

• Traditional scale-out approaches limit• Scalability, Availability, Reliability and Performance

• In Coherence…• Servers share responsibilities (health, services, data…)• No SPoB • 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

CachingApplications request data from the Data Grid rather than backend data sources

AnalyticsApplications ask the Data Grid questions from simple queries to advanced scenario modelingadvanced scenario modeling

TransactionsData Grid acts as a transactional System of Record, hosting data and business logic

EventsAutomated 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

• Coherence provides many Topologies for Data Management

• Local, Near, Replicated, Overview, Disk, Off-Heap, Extend (WAN), Extend (Clients)

Data Access Topologies

• 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

Partitioned Topology : Data Update

• Synchronous Update

• Avoids potential Data Loss & Corruption

• Predictable Performance

• Backup Partitions are partitioned away from Primaries for resilience

• No engineering requirement

Partitioned Topology

• No engineering requirement to setup Primaries or Backups

• Automatically and Dynamically Managed

Partitioned Topology : Recovery

• 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)

Partitioned Topology

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 • 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

• 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!

Partitioned Topology

• 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. 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. Multi-

threaded. 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) {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 valuetrades.invokeAll(trades.invokeAll(

new EqualsFilter(“getSecurity”,“ORCL”),new StockSplit(2.0));

• Aggregate Entries based on a Filterpositions.aggregate(

new EqualsFilter(“getSecurity”,“ORCL”),new SumFilter(“amount”));

Coherence as a Separate Tier with FMW

WebCenter IDM

Firewall Firewall Firewall

ExternalUsers

InternalUsers

Java EE

SOACoherence Data

Grid Service

Web

Tier RAC

RouterInternet

InternalUsers

InternalUsers

Maximum Availability ArchitectureActive/Active

WebTier

Firewall

Global Router

Firewall

WebTier

Firewall

ActiveData Center 2

Firewall

Low Latency High

ActiveData Center 1

WebCenter

IDM

RA

CSOA J2EE

Firewall

WebCenter

IDM

RA

C

SOA J2EE

Firewall

Coherence Data Grid Service

High Bandwidth

WAN

Passive

OracleAS Guard

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,

maintenancemaintenance

WebLogic Server Clusters

WebLogic Application Grid

Databases MainframesLegacy Commodity Virtualized

WebLogic Server Virtual Edition:Less Is More

Management Simplicity with Performance and Utilization

APPAPP

WebLogicWebLogic

JRockitJRockit

WLS-VEWebLogic Server

JRockit VE

Server

Server

OSOS

APPAPP

WebLogicWebLogic

JRockitJRockit

Oracle VMOracle VM

OSOS

Oracle VMOracle VM

Server

LVMLVM

APPAPP

WLSWLSVEVELVMLVM

APPAPP

WLSWLSVEVE LVMLVM

APPAPP

WLSWLSVEVELVMLVM

APPAPP

WLSWLSVEVE

OSOS

APPAPP

WebLogicWebLogic

JRockitJRockit

OSOS

APPAPP

WebLogicWebLogic

JRockitJRockit

WebLogic Server Virtual EditionValue 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

WLS-VEWebLogic Server

JRockit VE

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

Engine Tier

WLS

Coherence*Web

New for 11 g: ActiveCache

• Direct integration with HTTP container and TopLink ORM• Use standard JavaEE APIs

• Ideal for applications with • large data objects

WLS

Coherence*Web

WLS

Coherence*Web

client

Transient Data Tier

• large data objects• memory constraints• volatile scaling needs

• Foundation of Application Grid performance and reliability

• Easy to use, deploy, manageCoherence

CoherenceCoherence

CoherenceCoherence

Typical WebLogic Server Cluster

RDBMSRDBMSSessionsSessionsHTTP/JSPHTTP/JSP EJBEJB

WebLogic Server

SessionsSessionsHTTP/JSPHTTP/JSP EJBEJB JDBCJDBC

JDBCJDBC

Load Balancer

HTMLHTML

Application Server Tier

WebLogic Server

SessionsSessionsHTTP/JSPHTTP/JSP EJBEJB

WebLogic Server

JDBCJDBC

Oracle Web Tier – Web Cache

HTMLHTML

WebLogic Server with Coherence*WebSeparate traffic processing, session management

Coherence

Sessions and CacheSessions and Cache

RDBMSRDBMS

Coherence

Sessions and CacheSessions and Cache

WebLogic Server

ServletServlet EJBEJB JDBCJDBCLoad Balancer

Engine Tier State Tier

RDBMSRDBMS

Coherence

Sessions and CacheSessions and Cache

Coherence

Sessions and CacheSessions and Cache

WebLogic Server

ServletServlet EJBEJB JDBCJDBC

Oracle Web Tier – Web Cache

HTMLHTML

New in 11g: GridLink for RACSupport 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

Managed Server

Managed Server

WebLogic Cluster

WebLogic Domain

Multi Data Source (PAY)

Multi Data Source (CRM)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

CRM CRM PAY

Shared Storage

RAC Node 1

RAC Node 2

RAC Node 3

(PAY)(CRM)

DS1 DS2 DS3 DS1 DS2 DS3

Oracle RAC Cluster

Enterprise Grid Messaging in 11gR1Oracle 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 10gR3Oracle Fusion Middleware 10gR3

OC4J/Oracle Application Server, BPEL PM, SOA Suite…

AQ JMS API

JDBC

Oracle AQ DBMS Store

Self-Tuning and Work ManagersWebLogic's Self-Tuning Thread Pool

Network Socket Handlers(“Muxers”)

Request Queue

Self TuningThread Pool

• Active• Standby• Stuck• Hogging

Asynchronously dispatched workfrom WebLogic kernel, subsystem,or application

1. Monitor rate ofrequest processing

2. Adjust thread poolsize accordingly

High Availability ApplicationsZero Down Time Application Deployment

• Application versions run side-by-side in same JVM

• Controlled test mode and automated

Existing External Client Connections

New External Client Connections

New Application New Application VersionVersion

Retiring Application Retiring Application VersionVersionand automated

rollback• Automatic retirement:

graceful quiescence or timeout

• Ensures continuity of in-flight transactions

Managed WebLogic ServerSingle Java VM

VersionVersion VersionVersion

Administrative Test Client Connections

Test First inAdministrative

Mode

Lightweight, Testable Plain Java Objects

• Less configuration complexity

• Easier testability and alignment with OS tools• Ant / Maven / Hudson

• Java EE 6.0 makes

Context Dependency & Injection

JSF2

EJB 3.1

Facelets

JSR 330

Presentation

Dependency Injection

Business Services

• 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

52©2010 Oracle Corporation

Managed Beans

JPA 2

EJB 3.1

JAX-RS

JAX-WS

Persistence

Basic Component

REST

SOAP

DBWSDBWSSDOSDOEISEISMOXyMOXyJPAJPA

Java SEJava SE Java EEJava EE OSGiOSGi SpringSpring ADFADF

The Persistence RI for Java EE 6:

Eclipse Persistence Services Project (EclipseLink)

XML Data Legacy SystemsDatabases

53©2010 Oracle Corporation

• 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

TopLink Grid: Removing Database Bottlenecks

App TierClusters

• Prior to TopLink Grid, there were two strategies for scaling JPA applications into a cluster:• Disable Shared Cache• Cache Coordination—communicate

changes via messaging

54©2010 Oracle Corporation

Data TierDatabase

TopLink Grid with Coherence CacheApplication

EntityManager

L1 Cache

Application

EntityManager

L1 Cache

EntityManagerFactory EntityManagerFactory

Coherence

55©2010 Oracle Corporation

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 n2 as not all members are messaged—minimum communication is to primary and backup nodes.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. 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 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 recordis 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 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:• 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 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.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&SunOracle&Sun

Java RuntimeJava 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• 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 ServerJava 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 • 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