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IBM Case Study: Leveraging Software-Defined Infrastructure Control to Densify and Reduce Costs in Virtual Environments

Ken Christiance, Distinguished Engineer, IBM Riyaz Somani, co-founder and EVP Product Strategy, Cirba


Self-‐Service

The Reality of the IT Landscape Today

“Rogue” IT

IT-‐Controlled Managed Infrastructure

Virtual Infrastructure

Cloud Compu:ng

Dedicated Shared

1990’s

2000’s

2010’s

Agility & Cost


Economic Inhibitors in Private Virtual / Clouds

1. Mul>-‐Tenancy with varying requirements

2. No Demand Pipeline model

3. Complex and Rapidly Changing


Density (Consolidation Ratio) Low High

Low

High

Cri$cal Produc$on Workloads Non-‐Cri$cal

Produc$on Workloads Batch &

Dev/Test Workloads

Cost Risk

1. Multi-tenancy - challenges

Density is Governed by Opera>onal

Policy


Systems are the foundation of enterprise hybrid IT

Public Cloud

Private Cloud

Traditional infrastructure

existing

Enterprise Hybrid IT

new

Hybrid environments are becoming the ‘new norm’, as companies build-‐out systems of engagement, leveraging their systems of record

Shared Off-Premises

Cloud

Dedicated On-Premises

Cloud

Traditional IT Dedicated Off-Premises

Cloud

Key considerations: •  Data •  Open integration •  Common management •  Existing investments

Enterprise Applications

Cloud Enabled

Cloud Native Apps/Services

Hybrid Cloud


IBM Strategic Outsourcing Transforma$on • Emerging Technologies / Innova$on • Alignment with Strategic Business Objec$ve

By the Numbers • ~500,000 Systems Managed / ~330 Delivery Data Centers • >1,000 Global Customers

BeVer Sizing for Workload Demand • Business Focus on Efficiency • Op$miza$on for Enterprise Hybrid IT


IBM / Cirba Partnership – Better Sizing for Workload Demand

Business Objec$ves

• Hardware Cost Savings • SoZware License Cost Savings • Reduc$on of Capacity Related Incidents

Standards

• Policy / Profile Driven Approach for Workloads • SoZware Defined Infrastructure Control (Cirba)

Execu$on

• Op$mize Image Placement and Right Sizing • Op$mize Demand Management / Capacity Planning Processes • High Degree of Automa$on


Value Proposition

SoMware Defined Infrastructure Control

Supply & Demand Analy>cs Permuta>ons & Combina>ons

-‐ 7 granted patents, 7 pending -‐

densify

Demand Management Route and Reserve

densify

$

Supply Op>miza>on Defrag & De-‐risk

Policy Defined Control


Value Proposition

Intelligently Densifying Infrastructure Demand Management Route and Reserve

Supply Op>miza>on Defrag and De-‐risk

Policy Driven Control

SoMware Defined Infrastructure Control

Hypervisors

Resources Compute Storage Network SoZware Licenses

Cloud PlaRorms vRealize ICO


Policy Enables Software Defined Control Production

Critical Production

IT Production

Cloud Production Batch/HPC Pre-Prod Dev/Test

Density Low Med Med Low Med High

Performance High Med Med Very High Med Low

Availability N+2 N+1 N+1 N/A N/A N/A

Compliance Rigorous Medium Mul$-‐Tenant

Low to None None None

Volatility Low Med High High Med High

Operational Cycles

Business Defined IT Defined Unbound Windowed Simulated None

Licensing Host-‐Based Host-‐Based Catalog-‐Based

Environment-‐Based Transient Developer

Edi$on


Supply Optimization

Compute

Storage

Network

Software

Fragmented capacity Conten$on risk High DRS ac$vity S/W Inefficiency

Virtual Infrastructure ESX

VM VM

ESX

VM VM VM DRS

VM ESX

VM VM

ESX

VM VM VM DRS

VM vROps

vCenter


Supply Op$miza$on

Supply Optimization

Policy-‐Based Analy3cs

Op$mized Density SoZware Savings

Lower Opera$onal Risk Lower Vola$lity

Defragmented Capacity


VM VM

ESX

VM VM VM DRS

VM ESX

VM VM

ESX

VM VM VM DRS

VM vROps

vCenter


Cloud Orchestra$on

Demand Management

Supply Op$miza$on

Supply Optimization


Self-‐Service Requests

Even Higher Density Lower Service Level Risk Compliance Enforcement Cloud Process Automa$on

New Request: Requires Windows OS SQL server Has Customer Data Needs Gold tier storage Should run on West coast


VM VM

ESX

VM VM VM DRS

VM ESX

VM VM

ESX

VM VM VM DRS

VM vROps

vCenter

Rou3ng API


Cloud Orchestra$on

Demand Management

Supply Op$miza$on

Demand Management


Self-‐Service Requests

Rou3ng API Enterprise-‐Wide Rou$ng True Capacity Reserva$on External/Hybrid Modeling

Enterprise Process Automa$on


VM VM

ESX

VM VM VM DRS

VM ESX

VM VM

ESX

VM VM VM DRS

VM vROps

vCenter

No predic$ve element No capacity reserva$ons

No future infrastructure modeling No “Re-‐Rou$ng” analysis

Enterprise App Deployments

Migra>on & Consolida>on

Exis>ng Applica>ons


Soaking Up Stranded Capacity

0

200

400

600

800

1000

1200

12-‐Jan 19-‐Jan 26-‐Jan 2-‐Feb 9-‐Feb 16-‐Feb 23-‐Feb 2-‐Mar 9-‐Mar 16-‐Mar

VM Cap

acity

Units

Date

Stranded Capacity Over Time for Total Estate

Used Capacity Available Capacity Stranded Capacity

19-‐Feb: Added Supply (+50 Units) 30 Jan: Policy Change (+57 Units) Policy Limit

Addressable Performance Risk

Over Allocated Resources

Non-Optimal Placements

Early & Unnecessary Closing


Customer Examples – Major FS

Volume Reduction

Analytics for qualifying, right-sizing, migrating, handling exceptions, and reserving

capacity

Next Gen Clouds (50,000 VMs)

“Legacy” Virtual Environment (20,000 VMs)

Physical Servers across Lines of Business (60,000)


Customer Examples – Major FS

Ongoing Control

Advanced rebalancing, s/w license segmentation,

allocation optimization, capacity reservation

Density increase of 33% gives $1.5MM savings per pod

in spend avoidance. S/W license segmentation

yields another $720K per pod

2014 Savings: $45 Million

Next Gen Clouds (50,000 VMs)

$ `

“Legacy” Virtual Environment (20,000 VMs)

Physical Servers across Lines of Business (60,000)


Fortune 1000 Customer Example

* 1,267 OS instances in Par:al Environment

•  Image Placement & Right-‐Sizing •  Automated Capacity Control Customer Focus

•  6 weeks into deployment Scope

•  45% Density Improvement Opportunity •  40% S/W Windows DCL Reduc$on

Opportuni>es / Outcomes

•  $3.2 Million+* Financial Savings Impact / Opportunity


BSWD – 4 Lessons Learned

Policy based Control

Demand Management

Workload Placement Flexible Hos$ng

Driving Efficiency


Thank you!

Ken Christiance, Distinguished Engineer, IBM Riyaz Somani, co-founder and EVP Product Strategy, Cirba

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