vsphere performance best practices - vmware › vmworld › 2012 › top10 › vsp1800.pdf · $25...

vSphere Performance Best Practices

Peter Boone, VMware, Inc.

INF-VSP1800

#vmworldinf

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Disclaimer

This session may contain product features that are currently under development.

This session/overview of the new technology represents no commitment from VMware to deliver these features in any generally available product.

Features are subject to change, and must not be included in contracts, purchase orders, or sales agreements of any kind.

Technical feasibility and market demand will affect final delivery.

Pricing and packaging for any new technologies or features discussed or presented have not been determined.

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Overview

What a performance problem sounds like: • “My VM is running slow and I don’t know what to do!”

• “I tried adding more memory and CPUs but the problem got worse!”`

• “My VM is slow on one host but fast on another!”

What to look for? Where to start?

We will explore some of the most common performance-related

issues that our support centers receive cases for

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A word about performance….

Troubleshooting methodology must define: • How to find root cause

• How to fix the problem

Must answer these questions: 1. How do we know when we are done?

2. Where do we start looking for problems?

3. How do we know what to look for to identify a problem?

4. How do we find the root-cause of a problem we have identified?

5. What do we change to fix the root-cause?

6. Where do we look next if no problem is found?

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Agenda

Benchmarking & Tools Best Practices and Troubleshooting

The 4 “food groups”

• Memory

• CPU

• Storage

• Network

© 2012 VMware Inc. All rights reserved

BENCHMARKING & TOOLS

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Benchmarking

Consistent and reproducible results Important to have base level of acceptable performance

• Expectation vs. Acceptable

Determine baseline of performance prior to deployment • Benchmark on a physical system if applicable

Avoid subjective metrics, stay quantitative • “The system seems slower”

• “This worked better last year”

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Benchmarking

Benchmarking should be done at the application layer • Use application-specific benchmarking tools and load generators

• Check with the application vendor

Isolate variables, benchmark optimum situation before introducing load

Understand dependencies • Human interaction

• Other “food groups”

• Compare apples-to-apples

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Aggregates thousands of metrics into Workload, Capacity, Health scores Self-learns “normal” conditions using patented analytics

Smart alerts of impending performance and capacity degradation

Identifies potential performance problems before they start

Slide 11

Tools – vCenter Operations

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Tools – vCenter Operations Slide 12

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Tools – esxtop

Valuable tool built in to vSphere hosts View or capture real-time data

• View or playback data later

• Import data in 3rd party tools

vSphere Client performance graphs get their data from esxtop data • Presentation/unit may be different (e.g. %RDY)

Little overhead impact on the host


MEMORY

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Memory – Allocation

A VM’s RAM is not necessarily physical RAM • vRAM + overhead = maximum physical RAM

Whether or not that memory is physical or virtual depends on…

• Host configuration

• Shares

• Limits

• Reservations

• Host load

• Idle/Active VMs

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Memory – Overhead

Source: vSphere 5.0 Resource Management Guide

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Memory – Host Memory Management

Occurs when memory is under contention Transparent Page Sharing

Ballooning

Compression

Swapping

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Memory – Transparent Page Sharing

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Memory – Ballooning

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Memory – Compression

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Memory – Swapping

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Memory – Swapping

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Memory – VM Resource Allocation

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Memory – Resource Pool Allocation

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Memory – Ballooning vs. Swapping

Ballooning is better than swapping Guest can surrender unused/free pages

Guest chooses what to swap, can avoid swapping “hot” pages

Idle memory tax uses ballooning

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Memory – Rightsizing

Generally, it is better to OVER-commit than UNDER-commit If the running VMs are consuming too much host/pool memory…

• Some VMs may not get physical memory

• Ballooning or host swapping

• Higher disk IO

• All VMs slow down

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Memory – Rightsizing

If a VM has too little vRAM… • Applications suffer from lack of RAM

• The guest OS swaps

• Increased disk traffic, thrashing

• SAN slow down as a result of increased disk traffic

If a VM has too much vRAM… • Higher overhead memory

• Possible decreased failover capacity

• Longer vMotion time

• Larger VSWP file

• Wasted resources

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Memory – Troubleshooting

Wrong resource allocation May not notice a limit, e.g. VM or template with a limit gets cloned

Custom share values

Ballooning or swapping at the host level • Ballooning is a warning sign, not a problem

• Swapping is a performance issue if seen over an extended period

Swapping/paging at the guest level • Under-provisioned guest memory

Missing balloon driver (Tools)

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Memory – Best Practices

Avoid high active host memory over-commitment • No host swapping occurs when total memory demand is less than the physical

memory (Assuming no limits)

Right-size guest memory • Avoid guest OS swapping

Ensure there is enough vRAM to cover demand peaks

Use a fully automated DRS cluster

• Test that vMotion works

• Use Resource Pools with High/Normal/Low shares

• Avoid using custom shares


CPU

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CPU – Overview

Raw processing power of a given host or VM • Hosts provide CPU resources

• VMs and Resource Pools consume CPU resources

CPU cores/threads need to be shared between VMs Fair scheduling vCPU time

• Hardware interrupts for a VM

• Parallel processing for SMP VMs

• I/O

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CPU – esxtop

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CPU – esxtop

Interpret the esxtop columns correctly %USED – Physical CPU usage %SYS – Percentage of time in the VMkernel %RUN – Percentage of total scheduled time to run %WAIT – Percentage of time in blocked or busy wait states %IDLE – %WAIT- %IDLE can be used to estimate I/O wait time

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CPU – Performance Overhead & Utilization

Different workloads have different overhead costs (%SYS) even for the same utilization (%USED) CPU virtualization adds varying amounts of system overhead

• Direct execution vs. privileged execution

• Non-paravirtual adapters vs. emulated adaptors

• Virtual hardware (Interrupts!)

• Network and storage I/O

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CPU – vSMP

Relaxed Co-Scheduling: vCPUs can run out-of-sync Idle vCPUs incur a scheduling penalty

• configure only as many vCPUs as needed

• Impose unnecessary scheduling constraints

Use Uniprocessor VMs for single-threaded applications

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CPU– Scheduling

Over committing physical CPUs

VMkernel CPU Scheduler

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CPU– Scheduling



X X

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CPU– Scheduling



X X X X

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CPU – Ready Time

The percentage of time that a vCPU is ready to execute, but waiting for physical CPU time

Does not necessarily indicate a problem • Indicates possible CPU contention or limits

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CPU – NUMA nodes

Non-Uniform Memory Access system architecture

Each node consists of CPU cores and memory A CPU core in one NUMA node can access memory in another

node, but at a small performance cost

NUMA node 1 NUMA node 2

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CPU – NUMA nodes

The VMkernel will try to keep a VM’s vCPUs local to its memory • Internal NUMA migrations can occur to balance load

Manual CPU affinity can affect performance

• vCPUs inadvertently spread across NUMA nodes

• Not possible with fully automated DRS

VMs with more vCPUs than cores available in a single NUMA node

may see decreased performance

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CPU – Troubleshooting

vCPU to pCPU over allocation • HyperThreading does not double CPU capacity!

Limits or too many reservations • can create artificial limits.

Expecting the same consolidation ratios with different workloads

• Virtualizing “easy” systems first, then expanding to heavier systems

• Compare Apples to Apples • Frequency, turbo, cache sizes, cache sharing, core count, instruction set…

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CPU – Best Practices

Right-size vSMP VMs Keep heavy-hitters separated

• Fully automated DRS should do this for you

• Use anti-affinity rules if necessary

Use a fully automated DRS cluster

• Test that vMotion works

• Use Resource Pools with High/Normal/Low shares

• Avoid using custom shares


STORAGE

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Storage – esxtop Counters

Different esxtop storage views • Adapter (d)

• VM (v)

• Disk Device (u)

Key Fields: • DAVG + KAVG = GAVG

• QUED/USD – Command Queue Depth

• CMDS/s – Commands Per Second

• MBREADS/s

• MBWRTN/s

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Storage – Troubleshooting with esxtop

High DAVG: issue beyond the adapter • bad/overloaded zoning, over utilized storage processors, too few platters in the

RAID set, etc.

High KAVG: issue in the kernel storage stack

• Driver issue

• Full queue

Aborts: GAVG exceeding 5000 ms

• Command will be repeated, storage delay for the VM

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Storage – Benchmarking with iometer

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Storage – Storage I/O Control

Allows the use of Shares per VMDK Throttling occurs when datastore reaches latency threshold

• Higher share VMDKs perform IO first

vCenter monitors latency across all hosts • Not effective if datastore shared with other vCenters

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Storage – Storage DRS

Datastore clusters • Maintenance mode

• Anti-affinity rules

vCenter monitors for latency and disk space

• Migrate VMDKs for better performance or utilization

Not effective with automated tiering SANs • Check HCL to confirm these features are compatible

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Storage – Troubleshooting

Snapshots Excessive traffic down one HBA / Switch / SP can cause latency

• Consider using Round Robin in conjunction with ALUA

• Always be paranoid when it comes to monitoring storage I/O

Consider your I/O patterns • Peak time for storage IO?

• Virus scans, database maintenance, user logins

Always consult with array vendor • They know the best practices for their array!

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Storage – Best Practices

Use different tiers of storage for different VM workloads • Slower storage for OS VMDKs

• Faster storage for databases or other high-IO applications

Use the Paravirtual SCSI adapter

• Reduced overhead, higher throughput

Use path balancing where possible, either through plugins

(Powerpath) / Round Robin and ALUA, if supported. Use Storage DRS with SIOC

• Balance for both free space and latency

• Simplified datastore management


NETWORK

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Network – Load Balancing

Load balancing defines which uplink is used • Route based on Port ID

• Route based on IP hash

• Route based on MAC hash

• Route based on NIC load

Probability of high-bandwidth VMs being on the same physical NIC

Traffic will stay on elected uplink until an event occurs

• NIC link state change, adding/removing NIC from a team, beacon probe timeout…

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Network – Troubleshooting

Check counters for NICs and VMs • Network load imbalance

• 10 Gbps NICs can incur a significant CPU load when running at 100%

Ensure hardware supports TSO • Use latest drivers and firmware for your NIC on the host

For multi-tier VM applications, use DRS affinity rules to keep VMs on same host • Same vSwitch / VLAN, rules out physical network

If using Jumbo Frames, ensure it is enabled end-to-end

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Network – Best Practices

Use the vmxnet3 virtual adapter • Less CPU overhead

• 10 Gbps connection to vSwitch

Use the latest driver/firmware for the NICs on the host Use network shares

• Requires Virtual Distributed Switch 4.1

Isolate vMotion and iSCSI traffic from regular VM traffic • Separate vSwitches with dedicated NIC(s)

• Most applicable with Gigabit NICs

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In conclusion…

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Key Takeaways – Performance Best Practices

Understand your environment • Hardware, storage, networking

• VMs & applications

Advanced configuration values do not need to be tweaked or

modified • In almost all situations

Use fully automated DRS

Use Paravirtual virtual hardware

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Important Links

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Important Links

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vSphere Performance Best Practices

Peter Boone, VMware, Inc.

INF-VSP1800

#vmworldinf

vsphere performance best practices - vmware › vmworld › 2012 › top10 › vsp1800.pdf · $25...

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