Adaptive Server Farms for the Data Center

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. 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. 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. 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. ServiceC ServiceD ServiceA Pooling and sharing of the overall resource • Remove Boundaries • ConsolidateStorage • Establish overall management • Assign Services ServiceB ServiceE Service A Service B Service C Service D Service E

6. Management Cluster Management Cluster Shared Storage Shared Storage Server Management And Provisioning LAN1 (Public) B1 B2 Bn B3 A A .… A A R/3 R/3 R/3 R/3 LAN2 (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 Monitoring and controlagents (A) provide information on application state and QoS Clients

7. Measured QoS metric exceeds the specified maximum acceptable value Allocate more satellite nodes and deploy needed application to meet QoS target High Water Mark Target Metric Range Low Water Mark QoS Metric Time 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. QoS Monitoring & Management

8. ServiceD ServiceC ServiceA Control Nodes Control Nodes Continuous Services 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 ServiceB ServiceE Server Management And Provisioning

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

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

12. Oracle front end environment Web CachJ2EE SAP Web Spare SAP Appl Web CachJ2EE Web CachJ2EE Web CachJ2EE Web CachJ2EE SAP Web Spare SAP Appl Web CachJ2EE SAP Web SAP Appl SAP Central WIN SQL Spare Images Oracle RAC Oracle APP Svr Oracle APP Svr Oracle RAC Oracle APP Svr SAP Appl Oracle APP Svr SAP Appl SAP Web SAP Web Oracle APP Svr SAP Central WIN SQL Spare Spare Oracle APP Svr Web Cache & J2EE Oracle App S Oracle RAC Oracle RDBMS OLTP application environment Oracle decision support environment Oracle RAC Oracle RAC Oracle RAC Oracle RAC Spare SAP Web SAP Web SAP Appl Oracle RAC Oracle RDBMS Spare Oracle RAC Server Consolidation example - Oracle

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 + Apache Linux + sendmail W2K + IIS W2K + Citrix + MS Office Win+Citrix Linux+Apache Linux+Apache Linux+Apache Linux+SendMail Linux+sendMail Win+IIS Win+IIS Win+Citrix Spare tech-1

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.