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Extreme Capacity Management for Cloud Computing

Extreme Capacity Management for Cloud Computing. Michael Salsburg & Steve Guarrieri Unisys CMG Late Breaking Paper. Datacenter Evolution. Upgrades were planned many quarters in advance Each upgrade represented a major portion of the IT budget Virtuous Cycle Integration Simplification

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Extreme Capacity Management for Cloud Computing

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  1. Extreme Capacity Management for Cloud Computing Michael Salsburg & Steve Guarrieri Unisys CMG Late Breaking Paper

  2. Datacenter Evolution • Upgrades were planned many quarters in advance • Each upgrade represented a major portion of the IT budget • Virtuous Cycle • Integration • Simplification • Commoditization

  3. Administration Evolution • Ratio of Operators / Administrators to servers has reversed • Commoditization of administration is under way

  4. Cloud Computing - Escape Velocity Cloud Computing Utility Computing SOA Server Virtualization

  5. Cloud ComputingProviders and Consumers Provides Hardware / SoftwareComponents Vendors Integrator ProvidesITSM / Self-Service / AutomationCapabilities Provider ProvidesIaaS / PaaS ProvidesCloud Services /SaaS / Applications Tenant End Users Sub Tenants ProvidesAdded Capabilities

  6. Key Attributes of Cloud Computing • Self-Service - This principle is described using the NIST definition.With self-service, a consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service’s provider.It is the availability of cloud infrastructure as a service that differentiates cloud computing from more traditional approaches. • Ubiquity – services can be consumed from an Internet-enabled device • Elasticity – As service demands change, the amount of cloud infrastructure dedicated to these services can grow and shrink accordingly • Utility – This is the economic catalyst for using a cloud. • Pay as you Grow and Shrink • Multi-tenancy • No Capital Expenses

  7. FEED ME!!! • Cloud Computing • Sizing without prior knowledge of workloads • Keeping up with the commitment to elasticity • Quick provisioning process may cause over-provisioning of specific resources

  8. Applications follow a Pattern

  9. Utilization and Balance Define Perfect Balance When one component of the “trinity” is exhausted the other two should also be near exhaustion Data Tier Application Tier Hypothesis There are essentially three profiles, matching the three tiers Web Tier

  10. Hardware-Independent Approach • Issues with the tuple • What happens when a workload is moved from one type of server to another? • What happens when files are moved to SSD? • What is the real definition of network utilization from the server’s point of view?

  11. Example – Vmmark results http://www.vmware.com/products/vmmark/

  12. Further Investigation • Develop rules of thumb (ROT) based on recognizable patterns and relative arrival rates within these tuples • Study empirical data from cloud workloads • This implies knowledge of utilizations / arrival rates / service times as well as the type of processes using these servers • Input to our evolving rules of thumb from other investigators (that’s YOU)

  13. Questions? Michael.salsburg@unisys.com

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