Grid Economics for the Data Center Rex Wang VP Product Marketing, Oracle

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.

The Traditional Data Center Dedicated silos are inefficient Sized for peak load Constrained performance Difficult to scale Expensive to manage Dedicated Stacks Middleware Database Storage

Dedicated silos are inefficient

Sized for peak load

Constrained performance

Difficult to scale

Expensive to manage

Grid Computing Virtualizes and Pools IT Resources What is Grid Computing? Grid computing is a technology architecture that virtualizes and pools IT resources, such as compute power, storage and network capacity into a set of shared services that can be distributed and re-distributed as needed

Oracle Grid Computing in All Tiers Middleware Application Grid WebLogic Server Coherence In-Memory Data Grid JRockit Real Time Tuxedo Database In-Memory Database Cache Real Application Clusters Storage Automatic Storage Management Exadata Storage Server HP Oracle Database Machine Infrastructure Oracle VM Management Oracle Enterprise Manager Grid Control Most complete, open and integrated grid computing architecture in the industry

Middleware

Application Grid

WebLogic Server

Coherence In-Memory Data Grid

JRockit Real Time

Tuxedo

Database

In-Memory Database Cache

Real Application Clusters

Storage

Automatic Storage Management

Exadata Storage Server

HP Oracle Database Machine

Infrastructure

Oracle VM

Management

Oracle Enterprise Manager Grid Control

Virtualization and Clustering Are Complementary

Consolidation With Grid Computing Server A Server B Server C Server D Application A Application B Application C Application D Workload Avg Utilization <20% Applications A, B, C, D, E Net Workload Avg Utilization 70% Freed capacity to deploy elsewhere Consolidate with Oracle Grid Computing Take advantage of complementary workload peaks Higher utilization rates and efficiency Lower CapEx & OpEx Green footprint Oracle Shared Instance Server E Application E Server A Server B Server C Server D Server E

Take advantage of complementary workload peaks

Higher utilization rates and efficiency

Lower CapEx & OpEx

Green footprint

Scale Out With Grid Computing Applications A, B, C, D, E Net Workload If utilization too high, increase capacity Pay-as-you-go scale-out Smaller machines running at higher utilization Revolutionary capacity planning Avoid upfront CapEx and ongoing OpEx Take advantage of advances in hardware price-performance and energy efficiency World-class clustering at all levels of the stack: middleware, database, storage Oracle Shared Instance Server A Server B Server C Server D Add/Remove nodes to the cluster dynamically Scale linearly to hundreds of nodes Performance through parallelization Scale out with Oracle Grid Computing

Pay-as-you-go scale-out

Smaller machines running at higher utilization

Revolutionary capacity planning

Avoid upfront CapEx and ongoing OpEx

Take advantage of advances in hardware price-performance and energy efficiency

World-class clustering at all levels of the stack: middleware, database, storage

Add/Remove nodes to the cluster dynamically

Scale linearly to hundreds of nodes

Performance through parallelization

Quality of Service with Grid Computing Applications A, B, C, D, E Net Workload Deliver consistent, high Quality of Service Reliability through redundancy Predictable performance at any scale High availability – every application gets HA Oracle Shared Instance Server A Server B Server C Server D Load balancing Active-Active configuration Failover Disaster recovery Rolling upgrades Quality of Service with Oracle Grid Computing Server E

Deliver consistent, high Quality of Service

Reliability through redundancy

Predictable performance at any scale

High availability – every application gets HA

Load balancing

Active-Active configuration

Failover

Disaster recovery

Rolling upgrades

Agile Operations with Grid Computing AR AP HR AR HR AP Resource (CPU) Web J2EE DB PROVISIONING + WORKLOAD MANAGEMENT + AVAILABILITY Storage AP AR HR Sales Sales Sales BI BI PROVISIONING + WORKLOAD MANAGEMENT + AVAILABILITY Response Time Objectives IMDB Cache IMDB Cache IMDB Cache

Grid Economics for the Data Center Virtualization and clustering enable consolidation Pay-as-you-go scale-out High Quality of Service Automated grid management Lower CapEx & OpEx Avoid upfront CapEx & OpEx Avoid lost user productivity Raise IT staff efficiency

Virtualization and clustering enable consolidation

Pay-as-you-go scale-out

High Quality of Service

Automated grid management

Lower CapEx & OpEx

Avoid upfront CapEx & OpEx

Avoid lost user productivity

Raise IT staff efficiency