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Grid Economics for the Data Center
 

Grid Economics for the Data Center

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How grid computing revolutionizes the economics of the data center.

How grid computing revolutionizes the economics of the data center.

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  • Welcome to this session about Grid Economics for the Data Center. Today I’m going to be talking about how Oracle’s technology strategy, and particularly it’s Grid technologies, can help transform an Enterprise data center. I’ll be using examples of real customer implementations in this presentation to illustrate what can be achieved today in terms of resulting technical and business benefits.

Grid Economics for the Data Center Grid Economics for the Data Center Presentation Transcript

  • 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
  •