Grid Economics for the Data Center

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

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

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

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

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

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

Grid Economics for the Data Center

by Rex Wang

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