Capacity Management for Large Virtual Server Estates A Rationalized Approach Copyright 2014, PerfCap Corporation

The Capacity Planning Dilemma Computing style shift: complex distributed systems “Real CP” too expensive/complex & “Cycles are free” Vast estates of underutilized systems Real capacity planning marginalized Little or no capacity planning and little cost control Reactive capacity management Expensive “Black Swans” capacity cost workload CM for Large Server Estates2

Applications and Infrastructure Trade Finance Retail Banking Cash Management Trust & Securities Securities Origination, Sales, Trading Corporate Advisory FOREX Trading Mutual Funds Investments Alternative Investments (RREEF) Institutional Asset Management Insurance Asset Management Online Banking ETFs … Multitude of applications share same IT infrastructure. Each application has its particular capacity management needs. IT managers struggling to balance costs and performance. CM for Large Server Estates3

 Monitor Key Performance Indicators  Select and define KPI thresholds which suggest performance problems  Alert and trigger investigation when KPI thresholds are crossed.  Attempt to predict future behavior of KPIs based on past history  Determine risk by predicted time to failure  Trigger investigation and corrective action in a timely fashion PM/CP Process Reactive analysis Proactive management CM for Large Server Estates4

The Problem Automate monitoring of performance data Automate risk evaluation Automate timely triggers for capacity investigation Selectively perform in-depth capacity planning How do you do capacity management for a large server estate? CM for Large Server Estates5

 Visualize performance, capacity and risk status of all distributed application services in a single enterprise-wide view  Go beyond simplistic trending to projections of actual system responsiveness reflecting end-user satisfaction  Do realistic capacity planning with limited business forecasts  A solution that scales from 10s to 10,000s of servers The Challenges CM for Large Server Estates6

Automated Solution Uses New:  Methodology - Risk Analysis  Metric - Headroom  Risk Visualization Format Status Dashboards Enterprise-wide rollup status (by service, business, etc.) Transition Reports CM for Large Server Estates7

Automated Collection and Analysis Internet Analysis CMDB hypervisors Physical Servers Storage Arrays VMs Array Console Networks Storage Events Trending Clusters Real Time Applications Performance/Capacity Reports Risk Dashboards Notifications CM for Large Server Estates8

Breakthrough Maximum Current Risk Status Color Transaction Response Time Time : Days/Weeks/Months Lead Time Automated Risk Analysis Using Common KPIs CM for Large Server Estates9

Application Performance The key issue of application performance is responsiveness. e.g. transaction response time, batch turnaround time, end-to-end processing time, time to db update, trade execution time, etc. CM for Large Server Estates10

Response Time vs KPI CM for Large Server Estates11

Application Response Time Changes As Workload Changes CM for Large Server Estates12

Using Trending to Determine Capacity If acceptable response time should not exceed 600 ms, then application load capacity should not exceed 19 transactions / second. Estimated application capacity is 19 trans/sec CM for Large Server Estates13

Application Performance Reality vs Linear Trend This is the typical relationship between load and response time. After “knee” of the curve is reached, response time degrades rapidly. CM for Large Server Estates14

True Application Capacity Actual application capacity is 9 trans/sec True capacity is not maximum sustainable load but maximum load with acceptable performance. CM for Large Server Estates15

Capacity Headroom Where do you want to operate? Current Workload Headroom Saturation Point Operational Capacity Workload Response Time Response time is a function of CPU, disk, memory, adapters, etc. Headroom is the portion of operational capacity remaining. CM for Large Server Estates16

Headroom Risk Analysis CM for Large Server Estates17

Risk History Dashboard CM for Large Server Estates18

Capacity Risk Monitoring Automated Risk Analysis Computations Risk Status History Dashboard Risk Status Dashboards Automated Color Transition Notification CM for Large Server Estates19

A Tractable Solution  Reduces capacity planner’s workload  Closer to real user-perceived performance  Capacity manage 10,000s of servers CM for Large Server Estates20

VIRTUALIZED INFRASTRUCTURES Same Issues, New Complexity CM for Large Server Estates21

New Challenges  New complexity  Hierarchical views / service views  What systems virtualized to save cost?  Performance/capacity consequences  “What-if” provisioning scenarios CM for Large Server Estates22

New Level of Complexity Must do CM on both physical and virtual levels. CM for Large Server Estates23

Key Principle It is essential to provide capacity management from both the perspective of each virtual machine and the perspective of the host systems on which the virtual machines operate. CM for Large Server Estates24

Capacity Risk (Two Perspectives) Enterprise View Host Views Data Centre Views Guest Views Cluster Views Service View - ERP Service View - Service View – CRM Service View – HR CM for Large Server Estates25

Capacity Risk (Two Perspectives) CM for Large Server Estates26

Projected Resource View (Any Level) London Data Centre, CPU GHz Resource Projections, 31-Dec-2011 CM for Large Server Estates27

Underutilized Systems Extract from CMDB CM for Large Server Estates28

Underutilized Risk Color Status Physical limit Breakthrough threshold Lead time Time Metric Underutilized threshold New risk color Use a new purple color status to identify virtualization candidates. CM for Large Server Estates29

Virtualization Consequences CM for Large Server Estates30

Virtualization Consequences What happens if I move VMs, re-provision VMs, clone VMs, change host hardware, etc.? CM for Large Server Estates31

Virtual Infrastructure CP Challenges Enterprise-to-host performance and capacity visibility  IT infrastructure servers  Distributed application services Automated performance analysis, advising and modeling Smooth scaling from 10s to 10,000s of servers “What if” modeling of vSphere clusters and services CM for Large Server Estates32