Enterprise Capacity Optimization - Capacity Management Over Everything

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Enterprise Capacity Optimization Capacity Management over Everything

David Wagner TeamQuest Advocate


• Today’s Capacity Management Problems

• Introducing a “Big Data” Approach

• Some Examples

Agenda


TODAY’S CAPACITY MANAGEMENT PROBLEMS

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• Basic Analytics – Monitoring trends – Siloed problem analysis – Capacity planning focused on optimizing server cost versus performance

• Extremely technology-centric – Servers, mainframes – Occasionally storage or network – in isolation

• Big value and return, but also a big effort – Requires highly trained staff – Requires building a central, long-term repository (CMIS) – Problems with scalability of staff and tools … Politics!

Capacity Management Today’s Tradition


• Build on traditional value – Optimize more resources – Amplify – Accelerate

• Increase business relevance – Valuable predictive analytics in business and service context – Optimize efficiency

• Virtualization and cloud scale everything – Many-to-many interrelationships; capacity critical

New Capacity Management Goals


Needed: Cross-enterprise Capacity Management


“What is the health of the infrastructure?”

“How efficiently are we using the

infrastructure?”

“How many of those do we have?”

“Changes compared to the last reporting

period?”

“What’s the capacity forecast?”

“What were the exceptions?” “What’s the risk

assessment?”

Questions You Need Answered


The Environment: Continuously Changing


• Continuous consolidation and repurposing – Least used (servers, virtual servers) – Most power (use, cost) – Most expensive (total OpEx) – By application – By specific point in time

• Financial Optimization

Virtualization Optimization


• Asset database – Server CapEx, OpEx and licensing costs

• Service catalog – Mapping of applications to servers/virtual servers

• Power consumption – kWh per server

• Power cost – Spreadsheet: cost per kWh over time

• Resource utilization – By server, virtual server and application

Virtualization Optimization Data Sources


• Service Strategy – Financial management

• Service Design – Service Level and Availability Management

• Service Transition – Asset, Change and Configuration Management

• Service Operations – Service Desk – Application and IT operations – Event, Incident, Problem

• Or, in simpler terms… – Integrate capacity across ITIL Service Management and Service Delivery! – Integrate capacity with “everything”!

What Is Needed: Leverage Existing Solutions and Data


• Build a huge “data mart” (i.e. PMDB)

• Complexity = (data ETL) x (# sources) x (maintenance effort)

• Compliance: Data duplication, privacy, audit, etc…

• Extremely costly and time-consuming

• Not Agile

• Lock in

A Sub-optimal Solution: Data Warehousing


INTRODUCING A “BIG DATA” APPROACH

A Better Solution

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• Federate data, don’t centralize it – Technology (e.g. server, network, storage, etc.) – Service (catalog, metrics, tickets, etc.) – Business (KPIs, plans, transactions, etc.)

• Automated analytics across all data sources – Flexible and adaptive – Turn data into actionable information – Single-pane-of-glass capacity management

A Better Solution: A “Big Data” Approach


The “Big Data” Approach: Play Well with Others


SOME EXAMPLES A “Big Data” Approach

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• Response time versus performance – Speed MTTR in service context

• Business, service, asset and power – Continuous financial optimization

• Service tickets and performance/capacity – Risk management

• Spanning pure technology silo challenges – Improve availability and speed MTTR

Examples of Analysis across Data Sources

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Correlate Utilization with Response Time

Response Time (from HP BSM, fka: Mercury BAC)

Correlated with Utilization (from BMC BCO)

Value


Financial Optimization Report


Service Desk and Capacity


Servers AND Storage - Together





Huge Value for VMware Environments: Cross the Chasm!


Servers AND Storage: Automating High-value Analytics








• Large Insurance – Saved $25M over 3 years – expense deferral while maintaining SLAs – Improved resource utilization by 400%

• Large Finance – Deferred massive, planned DataCenter build out by 18 months and

counting ($20M+ – Reduced Service Risks, lowered operational costs, expanded staff

scope/reach

• AAS Provider – Sped MTTR for client-facing services by 400%

• Telecommunications – Sped client-facing infrastructure MTTR, cost-reduction, right-sizing

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Results


• Inventory processes – Data collection – Data storage – Capacity Analysis/Reporting

• Inventory Business KPIs

• Identify “low hanging fruit” – By Service – By BU – By Platform – By Technology/silo

• Get started

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Summary


• Powerful analytics

• Cloud scalability

• Fast ROI

• Cross-silo solution

TeamQuest Software & Services IT Service Optimization

• Improved productivity

• Decreased costs

• Reliable service delivery

• Risk mitigation