Extreme Capacity Management for Cloud Computing

Michael Salsburg & Steve GuarrieriUnisysCMG 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– Commoditization

Administration Evolution

• Ratio of Operators / Administrators to servers has reversed

• Commoditization of administration is under way

Cloud Computing - Escape Velocity

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Emerging Technologies

Utility Computing

SOA

Server Virtualization

Cloud Computing

Cloud ComputingProviders and Consumers

Vendors

Integrator

Provides Hardware / Software

Components

Provider

ProvidesITSM / Self-Service / Automation

Capabilities

Tenant

ProvidesIaaS / PaaS

End Users

Sub Tenants Provides

Added Capabilities

ProvidesCloud Services /

SaaS / Applications

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

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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

Applications follow a Pattern

Utilization and Balance

Data Tier

Application Tier

Web Tier

Define Perfect BalanceWhen one component of the “trinity” is exhausted the other two should also be near exhaustion

HypothesisThere are essentially three profiles, matching the three tiers

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?

Example – Vmmark results

Web Server App Server File Server DB Server

Application SPECweb™2005-based

SPECjbb™2005-based

dbench MySQL

VM OS SLES 10 64-bit Win 2003 64 bit SLES 10 64-bit SLES 10 64-bit

VM Platform 2 CPU512MB RAM8 GB disk

2 CPU1GB RAM8 GB disk

1 CPU256MB RAM8 GB disk

2 CPU2GB RAM10 GB disk

CPU Utilization 30% 10% 14% 19%

Storage I/O/s 11 .7 417.6 84

Network I/O/s 3412 1 1564 1772

http://www.vmware.com/products/vmmark/

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)

Questions?

Michael.salsburg@unisys.com