1. Adaptive Server Farms for the Data Center Contact: Ron Sheen Fujitsu Siemens Computers, Inc ron.sheen@fujitsu-siemens.com Sever Blade Summit, Getting the most from your blade servers, March 22, 2005

2. 2 The challenge – over provisioned, inflexible infrastructure Today‘s computing infrastructure – Static and unshared islands for each service Inefficient Costly Over provisioned Hard to manage Inflexible Utilization of UNIX/Windows servers is low (<25% over 24 hours across all servers) Source: 2003 META Group – The Data Center of the Future

3. 3 The challenge – Cost Reduction Labor costs - 75% of IT budgets - inefficiently applied across multiple, separate systems – mostly sustaining costs  Excessive administration, upgrades, support, service, overhead Administration and routine IT tasks are mostly manual  Limited labor resources/budget focused on manual, low value tasks IT over invests in resources for static, standalone systems  Provisioning to meet peak demand  Provisioning for backup systems and applications  Resources duplicated; cannot be efficiently shared

4. 4 Addressing these challenges The tools exist to address these challenges by:  Restructuring the data center to remove the barriers that isolate the various services into their various islands  Add tools to provision and monitor services and hardware  Add automation to automatically adapt to the changing needs of the services and users.

5. 5 Pooling and sharing of the overall resource Remove Boundaries Consolidate Storage Establish overall management Assign Services Service AService BService CService DService E Service B Service A Service C Service D

6. 6 Shared Storage B1B2 B3 Bn.… R/3 LAN2 (Storage) LAN1 (Public) Shared Storage Automation for QoS, Scaling, HA, Configuration and Provisioning Assume a typical blade server farm - multiple blades/shared storage Add server management, provisioning service, and control logic Define policies and install monitoring and control agents Server Management And Provisioning Management Cluster A A A A Monitoring and control agents (A) provide information on application state and QoS Clients

7. 7 QoS Monitoring & Management High Water Mark Low Water Mark Target Metric Range Time QoS Metric Measured QoS metric exceeds the specified maximum acceptable value Allocate more satellite nodes and deploy needed application to meet QoS target Measured QoS metric is below the specified minimal acceptable value (too many resources) Perform orderly shutdown of some instances thus reducing cost and freeing the resources for other work.

8. 8 Continuous Services Service B Service A Service C Service D Adaptive infrastructure provides automated, continuous service and high availability without the costs of traditional infrastructure Application or server fails: redirect user traffic …then restart application or server If no restart, but application service performance is satisfactory, there is no adjustment If service performance is not satisfactory, then provision or reallocate another application/server Control Nodes Server Management And Provisioning Service E

9. 9 Summary of steps To implement adaptive server farms:  Establish an infrastructure with sharable server platforms, storage and networks  Implement a deployment service  This can be a traditional deployment service like e.g. Altiris or Remote Deploy  Or a virtual server deployment like VMWare  Provide a highly-available management platform  Monitor and collect information from all servers  Correlate data and execute reaction scripts  Include agents on each server to provide data for the management platform  Direct agents for monitoring application availability and performance  Interfaces to application suite tools like Oracle Grid Manager The following slides illustrate two example deployments with SAP suite and Oracle.

10. 10 Cost Reduction – Solution with server consolidation and automation Service A Service C Service D Service B Service A Service B Svc C Svc D Control Systems Unshared Utilization 10% Peak 50% Different peaks Reduced investment Higher utilization Automation saves labor cost Spares Before After: Application Service Pools Spares with Automated Provisioning

11. 11 Server Consolidation example - SAP tech-1 Sample configuration for a SAP test environment Images SAP App SAP CI SAP Web WIN SQL SAP Web SAP Appl SAP CentralSpareWIN SQLSAP Web SAP Appl SAP CentralSpareWIN SQL SAP Web SAP Appl SAP CentralSpareWIN SQL SAP Web SAP Appl Sample configuration for a SAP prod. environment

12. 12 Server Consolidation example - Oracle

13. 13 Automated Server Farm Management  Dynamic service provisioning and workload management for blade server farms  Automated, mass installation of bare-metal blade servers  Automated, mass software deployment and software updates  Priority and workload-based reusage of resources  Continuous services  Benefits  Reduced administration  Low-cost scalability  Simplified continuous availability Images Linux + sendmail W2K + IIS Linux + Apache W2K + Citrix + MS Office Linux+Apache Linux+SendMailLinux+sendMail Linux+ApacheWin+IIS SpareWin+Citrix tech-1

14. 14 Conclusion Many of the costly operations in the data center can be automated These techniques will provide high available and high quality of service without the overhead and complexity of traditional clustering The following demo will illustrate how this all works together.