Transcript Performance best practices

vSphere Performance Best Practices
Rob Moran
Premier Services Engineer – VMware Global Support Services – Cork, Ireland
© 2009 VMware Inc. All rights reserved
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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
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BENCHMARKING & TOOLS
© 2009 VMware Inc. All rights reserved
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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Tools – vCenter Operations
Slide 10
 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
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Tools – vCenter Operations
Slide 11
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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 the kernel
and VSI
• Presentation/unit may be different (e.g. %RDY)
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MEMORY
© 2009 VMware Inc. All rights reserved
Memory – Overhead
 A VM’s RAM is not necessarily machine RAM
• vRAM + overhead = maximum machine RAM
Source: vSphere 5.1 Resource Management Guide
• Note: These are estimated values
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Memory – Transparent Page Sharing
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Memory – Host Memory Management
Occurs when memory is under contention
 Ballooning
 Compression
 Swapping
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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
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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
• Use Resource Pools with High/Normal/Low shares
• Avoid using custom shares
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CPU
© 2009 VMware Inc. All rights reserved
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
 %RDY - The percentage of time a VM is ready to run, but no
physical processor is ready to run it which may result in decreased
performance
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%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
• Imposes 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
Over committing physical CPUs
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VMkernel CPU Scheduler
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X
CPU– Scheduling
Over committing physical CPUs
XX
X X
VMkernel CPU Scheduler
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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
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NUMA node 2
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
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STORAGE
© 2009 VMware Inc. All rights reserved
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 3rd party
plugins / Round Robin and ALUA, if supported.
 Use Storage DRS with SIOC
• Balance for both free space and latency
• Simplified datastore management
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NETWORK
© 2009 VMware Inc. All rights reserved
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 (Load Based Teaming)
 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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How to measure the network?
 scp from/to ESXi host is not valid check!
 With scp we will involve underlying storage on source and
destination VM/host
 CPU can affect the test, scp will encrypt/decrypt the network flow
 Copy to ESXi host can give false result as the management
interface has very limited resources
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How to check network performance?
 VM – VM on same ESXi host. This will exclude physical network
problems
 VM –VM on different ESXi host. This will involve physical NICs and
switch as well
 Physical – VM. Will also test physical devices but we can focus on
one VM
 Physical – Physical: this will give us some number about what to
expect
 Use iperf/jperf/netperf. Free tool for network test
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Iperf
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Iperf
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Windows and Linux version
Will not use storage
We can use different option for test (UDP/TCP)
Automatically calculates bandwith
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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 hardware
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Important Links
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Important Links
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