From Event to Action: Accelerate Your Decision Making with Real-Time Automation
vSphere APIs for performance monitoring
1. vSphere APIs for performance monitoring
London Workshop
October – 2010
Balaji Parimi, Staff Engineer, Ecosystem Performance, VMware, Inc.
Ravi Soundararajan, Senior Staff Engineer, Performance, VMware, Inc.
8. Why?
vSphere gives you awesome, helpful charts
But you don’t have to rely solely on these charts
Do you want to learn how to make your own charts?
• Keep watching
10. Agenda
What sorts of stats are useful?
How does vSphere retrieve them?
How can you get these stats and use them yourself?
11. Useful stats
Basics of performance monitoring in virtual infrastructure
• Find underperforming resources
• Find overcommitted resources
• Identify issues due to resource sharing among VMs
Resources we will look at
• CPU
• Memory
• Disk
• Network
13. CPU basics
ESX
CPU0 CPU1 CPU2 CPU3
VM0 VM1 VM2 VM3
VM4
Run (accumulating used time)
Ready (wants to run, no physical CPU available)
Wait: blocked on I/O or voluntarily descheduled
VM5
VM6
Run
Ready
Wait/Idle
14. Why is my VM slow?
CPU saturated (cpu.usage.average)
Ready time? (cpu.ready.summation)
Latency to be swapped in? (cpu.swapwait.summation)
16. Small ready time
Ready time vCPU1: 150ms
Real-time chart: refresh 20s
150ms / 20s = 0.75% (No big deal)
Right y-axis is relevant
17. Now, turn on CPU burner on same host…
CPU burner
~100% of 1 vCPU
18. And see what happens to original VM’s ready time
SpecJBB ready time
~2000ms = 10%
(ps. SpecJBB perf. dropped by 10%)
19. Latency to load in VM: cpu.swapwait.average
Sometimes there is a latency to load VM data from disk: cpu swapwait
CPU takes 20s to load in data before VM can run!
20. CPU issues: Summary
CPU saturated?
High Ready time
• Problematic if it is sustained for high periods
• Sample rule of thumb: > 20% per vCPU investigate further
• Possible contention for CPU resources among VMs
• Workload Variability? Fix with VMotion/DRS
• Resource limits on VMs? Check Limits, reservations and shares
• Actual over commitment? Fix with Vmotion/DRS/more CPUs
High SwapWait time
• Consider setting memory reservation (see next section, “Memory”)
22. Memory
ESX must balance memory usage
• Page sharing to reduce memory footprint of Virtual Machines
• Ballooning to relieve memory pressure in a graceful way
• Host swapping to relieve memory pressure when ballooning insufficient
• Compression to relieve memory pressure without host-level swapping
ESX allows over commitment of memory
• Sum of configured memory sizes of virtual machines can be greater than
physical memory if working sets fit
Memory also has limits, shares, and reservations
Host swapping can cause performance degradation
23. VM1
Ballooning, compression, and swapping (1)
Ballooning: Memctl driver grabs pages and gives to ESX
• Guest OS choose pages to give to memctl (avoids “hot” pages if possible): either free
pages or pages to swap
• Unused pages are given directly to memctl
• Pages to be swapped are first written to swap partition within guest OS and then
given to memctl
Swap partition w/in
Guest OS
ESX
VM2
memctl
1. Balloon
2. Reclaim
3. Redistribute
F
24. Swap
Partition
(w/in guest)
Ballooning, swapping, and compression (2)
Swapping: ESX reclaims pages forcibly
• Guest doesn’t pick pages…ESX may inadvertently pick “hot” pages ( possible VM
performance implications)
• Pages written to VM swap file
VM1
ESX
VM2
VSWP
(external to guest)
1. Force Swap
2. Reclaim
3. Redistribute
25. ESX
Compression
Cache
Ballooning, swapping and compression (3)
Compression: ESX reclaims pages, writes to in-memory cache
• Guest doesn’t pick pages…ESX may inadvertently pick “hot” pages ( possible VM
performance implications)
• Pages written in-memory cache faster than host-level swapping
Swap
Partition
(w/in guest)
VM1 VM2
1. Write to Compression Cache
2. Give pages to VM2
26. Ballooning, swapping, and compression
Bottom line:
• Ballooning may occur even when no memory pressure just to keep memory
proportions under control
• Ballooning is preferable to compression and vastly preferably to swapping
• Guest can surrender unused/free pages
• With host swapping, ESX cannot tell which pages are unused or free and may
accidentally pick “hot” pages
• Even if balloon driver has to swap to satisfy the balloon request, guest chooses what
to swap
• Can avoid swapping “hot” pages within guest
• Compression: reading from compression cache is faster than reading from disk
27. Swapping in Guest! = Swapping in Host
DVDstore benchmark: SQL DB benchmark… uses lots of memory
About to start memory hogger program in guest
28. Force Guest swapping: No Host-level swapping
Before memhog: no guest swapping After memhog, guest swaps, but
Host does not!
29. Viewing Host-level swapping with performance charts
Setup: 2 VMs…one dvdstore, one memhog, competing for host memory
Host swaps out dvdstore VM memory to fulfill memhog VM requests
Host swaps in dvdstore VM memory to fulfill dvdstore VM requests
30. Using Swap Rate Counters: Remember CPU SwapWait?
Cpu.swapwait.summation: CPU is waiting for memory to be swapped in
34. ESX storage stack
Different latencies for local disk vs. SAN (caching, switches, etc.)
Queuing within kernel and in hardware
vSphere shows
• Total Command Latency
• Kernel Latency
• Device Latency
• Bandwidth/IOPS
35. Disk performance problems 101
What should I look for to figure out if disk is an issue?
• Am I getting the IOPs I expect?
• Am I getting the bandwidth (read/write) I expect?
• Are the latencies higher than I expect?
• Where is time being spent?
What are some things I can do?
• Make sure devices are configured properly (caches, queue depths)
• Use multiple adapters and multipathing
• Check networking settings (for iSCSI/NAS)
36. Another disk example: Slow VM power on
Trying to Power on a VM
• Sometimes, powering on VM would take 5 seconds
• Other times, powering on VM would take 5 minutes!
Where to begin?
• Powering on a VM requires disk activity on host Check disk metrics for host
37. Let’s look at the vSphere client…
Max Disk Latencies range from 100ms to 1100ms…very high! Why?
(counter name: disk.maxTotalLatency.latest)
Rule of thumb:
latency > 20ms is
Bad.
Here:
1,100ms
REALLY BAD!!!
38. High disk latency: Mystery solved
Host events: disk has connectivity issues high latencies!
Bottom line: monitor disk latencies; issues may not be related to
virtualization!
40. Network performance problems 101
What should I look for to figure out if network is an issue?
• Am I getting the packet rate that I expect?
• Am I getting the bandwidth (read/write) I expect?
• Is all traffic on one NIC, or spread across many NICs?
• [more advanced… not available through counters]: out-of-order packets?
What are some things I can do?
• Check host networking settings
• Full-duplex/Half-duplex
• 10Gig network vs 100Mb network?
• Firewall settings
• Check VM settings: all VMs on proper networks?
41. Network performance troubleshooting
Customer complains about slow network
• She’s running netperf on a GigE Link
• She sees only 200Mbps
• Why? I bet it’s that VMware stuff!!
• Note to reader: Please don’t blame VMware first ☺
Where do we start?
42. All VMs using same NIC (VM network)
All VMs using “VM Network” and sharing 1 physical NIC
43. Where do we begin? Check VM bandwidth
Measure VM Bandwidth (net.transmitted.average)
• 200 Mb/s
• Screenshot from the vSphere client
44. Check Host Bandwidth
Measure Host Bandwidth (net.transmitted.average)
• Host sees around 900Mbps…why is VM at 200Mbps?
• Hmm… are we sharing this NIC with multiple VMs?
45. All traffic is going through one NIC!
Measure per-physical-NIC traffic
Hmm… all VM traffic is going through 1 NIC
Let’s split the VMs across NICs
All traffic through one
NIC on this host
47. Network issues: Configuration woes
Network adapter set to “full
duplex, 100 Mbps”:
< 0.1Mbps!
Specific combo of switch and
adapter caused this
performance degradation!
Lesson: Check specs &
configuration!
Network adapter set to
“autonegotiate”: 90Mbps
48. Agenda
What sorts of stats are useful?
How does vSphere retrieve them?
How can you get these stats and use them yourself?
49. Stats infrastructure in vSphere
ESX
VM VM VM VM VM
vCenter Server
(vpxd, tomcat)
ESX
VM VM VM VM VM
ESX
VM VM VM VM VM
DB
1. Collect 20s
and 5-min host
and VM stats
2. Send 5-min
stats to
vCenter
3. Send 5-min
stats to DB
4. Rollups
50. Rollups
DB
1. Past-Day (5-minutes) Past-Week
2. Past-Week (30-minutes) Past-Month
3. Past-Month (2-hours) Past-Year
4. (Past-Year = 1 data point per day)
DB only archives historical data
• Real-time (i.e., Past hour) NOT archived at DB
• Past-day, Past-week, etc. Stats Interval
• Stats Levels ONLY APPLY TO HISTORICAL DATA
51. Anatomy of a stats query: Past-hour (“RealTime”) Stats
Client
ESX
VM VM VM VM VM
vCenter Server
(vpxd, tomcat)
ESX
VM VM VM VM VM
ESX
VM VM VM VM VM
DB
1. Query
2. Get stats
from host
3. Response
No calls to DB
Note: Same code path for past-day stats within last 30 minutes
52. Anatomy of a stats query: Archived stats
Client
ESX
VM VM VM VM VM
vCenter Server
(vpxd, tomcat)
ESX
VM VM VM VM VM
ESX
VM VM VM VM VM
DB
1. Query
3. Response
No calls to ESX host (caveats apply)
Stats Level = Store this stat in the DB
2. Get Stats
53. Agenda
What sorts of stats are useful?
How does vSphere retrieve them?
How can you get these stats and use them yourself?
54. Phew! Ok, How do I get these stats?
You want a chart like this?
PowerCLI
• CPU Usage for a VM for last hour:
• $vm = Get-VM –Name “Foo”
• Get-Stat –Entity $vm –Realtime –Maxsample 180 –Stat
cpu.usagemhz.average
• Grab appropriate fields from output, use graphing program, etc.
55. Looks simple… What’s going on behind the scenes?
To get stats, this is what is going on FOR EACH GET-STAT CALL
• Retrieve PerformanceManager
• QueryPerfProviderSummary $vm Says what intervals are supported
• QueryAvailablePerfMetric $vm Describes available metrics
• QueryPerfCounter Verbose description of counters
• Create PerfQuerySpec Query specification to get the stats
• QueryPerf Get stats
Bottom line: The PowerCLI toolkit spares you details…Easy to use!
56. PowerCLI Is so easy… Why use Java / C#?
PowerCLI is great for scripting
• Stateless
• Hides details
But with Java / C#
• You can squeeze out more performance!
• Much higher scalability
57. Pseudo code
Get MOREF
for each Get-Stat {
QueryAvailablePerfMetric();
QueryPerfCounter();
QueryPerfProviderSummary();
create PerfQuerySpec();
QueryPerf();
}
Get MOREF
QueryAvailablePerfMetric();
QueryPerfCounter();
QueryPerfProviderSummary();
create PerfQuerySpec();
for each Get-Stat {
QueryPerf();
}
PowerCLI Java
perfCounter property
Of
PerformanceManager
58. Performance implications: Need to write scalable scripts!
Entities
(cpu.usagemhz.average)
PowerCLI
(Time in secs)
Java
(Time in secs)
1 VM 9.2 14
6 VMs 11 14.5
39 VMs 101 16
363 VMs 2580 (43 minutes) 50
Java provides opportunities for scalable, ongoing stats collection
Let’s examine Java code in more detail…
A Naïve script that works for small environments may not be suitable
for large environments
Highly-tuned
Java Stats
Collector
59. GetPerfStats – Main method
Get MOREF
Get CounterIds
QueryAvailablePerfMetric
QueryProviderSummary
create PerfQuerySpec
QueryPerf
Get MOREF
QueryAvailablePerfMetric();
QueryPerfCounter();
QueryPerfProviderSummary();
create PerfQuerySpec();
for each Get-Stat {
QueryPerf();
}
perfCounter
63. Get CounterIds
Get available counterIDs
from perfCounter property of
PerformanceManager
Map human-readable stat name to counterID
(e.g., cpu.usagemhz.average 101)
QueryPerf (…) requires counterID
70. So, what is Java / C# buying us?
Avoiding redundant work
More compact return format (CSV vs. objects)
Low-overhead tracking of ongoing inventory changes
Etc.
If we dig deeper, we can optimize even more…
71. Digging deeper: The PerfQuerySpec architecture
To grab counters:
QueryPerf(PerfQuerySpec[] querySpec)
PerfQuerySpec: Specifies which counters to grab
PerfQuerySpec[]: [pQs1, pQs2, pQs3, …]
Array of PerfQuerySpec objects pQs1, pQs2, pQs2
Can grab multiple stats using single QueryPerf call
Entity (host,
VM)
Format
(CSV,
normal)
MetricId StartTime EndTime IntervalID
(20s, 300s)
maxSample
72. Complexities of QueryPerf
How Does vSphere Process QueryPerf(querySpec[])?
1. vCenter receives queryPerf request with querySpec[]
2. vCenter takes each querySpec one at a time
3. vCenter gets data for each querySpec before processing next one
Options for querySpec[]:
1. 1 entry 1 stat or set of stats for a single entity (e.g., all CPU)
2. Multiple entries. Examples:
• Each entry for a different entity …
• Each entry for a different stat type, same entity
VM1,cpu.* VM2,cpu.* H3,mem.*
VM1,cpu.* VM1,net.* VM1,mem.
*
pQs1 pQs2 pQs3
73. Implications of QuerySpec
Format of QuerySpec Allows Multiple Client Options
1. Grab each stat one at a time
2. Grab a group of stats per entity at once
3. Grab all stats for all entities at once
4. Grab stats for a subset of entities at once
Some Tradeoffs:
1. Network processing (large result sets vs. small result sets)
2. Client aggregation overhead
3. vCenter processing (Each QueryPerf handled in a single thread)
74. What about in-guest stats?
Using VIX APIs:
• Create a script that can get what ever stats you are interested in.
• Make the script write the stats to a file.
• Copy file from the guest.
• Session covering this topic
• PPC-15 – Guest Operations using VMware VIX APIs and Beyond
75. Back to the Future (1)
Now I know how to I convert this… (many metrics on different charts)
76. Back to the Future (2)
To This (CPU, Memory, Disk, and Network on the same chart)
80. What about VMs running on a Host?
Memory usage of VMs on a Host
81. Summary, Part 1: Some useful Counters to monitor
Resource Metric Host or
VM?
Description
CPU Usage Both CPU % used
Ready VM Ready to run, but limit or no available physical CPU
SwapWait VM CPU time spent waiting for host-level swap-in
Memory Swapin,
swapinrate
Both Memory ESX host swaps in from disk (per VM, or
cumulative over host)
Swapout,
swapoutrate
Both Memory ESX host swaps out to disk (per VM, or
cumulative over host)
Disk commands Both Operations done during stats refresh interval
totalLatency Host End-to-end disk latency (available for reads & writes)
Usage Both Disk bandwidth utilized (available for reads & writes)
Network Packets
received,
transmitted
Both Operations done during stats refresh interval
Usage Both Network bandwidth used (available for reads & writes)
82. For completeness…VM memory metrics
Metric Description
Memory Active (KB) Physical pages touched recently by a virtual machine
Memory Usage (%) Active memory / configured memory
Memory Consumed
(KB)
Machine memory mapped to a virtual machine,
including its portion of shared pages. Does NOT
include overhead memory.
Memory Granted (KB) VM physical pages backed by machine memory. May
be less than configured memory. Includes shared
pages. Does NOT include overhead memory.
Memory Shared (KB) Physical pages shared with other virtual machines
Memory Balloon (KB) Physical memory ballooned from a virtual machine
Memory Swapped (KB) Physical memory in swap file (approx. “swap out –
swap in”). Swap out and Swap in are cumulative.
Overhead Memory (KB) Machine pages used for virtualization
83. Host memory metrics
Metric Description
Memory Active (KB) Physical pages touched recently by the host
Memory Usage (%)* Active memory / configured memory
Memory Consumed
(KB)
Total host physical memory – free memory on host.
Includes Overhead and Service Console memory.
Memory Granted (KB) Sum of memory granted to all running virtual
machines. Does NOT include overhead memory.
Memory Shared (KB) Sum of memory shared for all running VMs
Shared common (KB) Total machine pages used by shared pages
Memory Balloon (KB) Machine pages ballooned from virtual machines
Memory Swap Used
(KB)
Physical memory in swap files (approx. “swap out –
swap in”). Swap out and Swap in are cumulative.
Overhead Memory (KB) Machine pages used for virtualization
*For a cluster, mem.usage.average = (consumed + overhead)/total mem
84. Summary, Part 2: Cheat sheet
Rules of Thumb
• Ready Time > 20% sustained is undesirable
• Host-level swapping is bad, > 1MBps is especially bad
• Disk latencies > 20 ms BAD
• Use IOmeter to assess disk bandwidth and latency
• Network
• run netperf to get network baselines
85. Summary, Part 3: SDK/API Tips and tricks
Collect static data once
• CounterIDs, metricIDs, MOREFs etc.
• Use Views to keep this data up to date.
• Reuse PerfQuerySpec as much as possible
Use CSV format
• Reduces serialization cost and the size of metadata
Choose metrics and query intervals carefully
• Query the real-time stats at a slower rate than the refresh rate
• Choose correct stats levels
Use parallelism (multi-threaded clients)
86. Conclusion
vSphere gives a bunch of awesome charts
If you want to see the data differently, use the API
PowerCLI is great for simple scripts
When designing for scalability, consider Java / C#
87. Resources
Developer Support
• Dedicated support for your organization when building solutions using vSphere
APIs, PowerCLI, vSphere Web Services SDKs and many more VMware SDKs
• http://vmware.com/go/sdksupport
PowerCLI Training
• 2 day instructor led training, 40% lecture, 60% lab
• http://vmware.com/go/vsphereautomation
VMware Developer Community
• SDK Downloads, Documentation, Sample Code, Forums, Blogs
• http://developer.vmware.com
Technology Alliance Partner (TAP) Program
• Updated partner benefits
• http://www.vmware.com/partners/alliances/programs/
88. 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.
“These features are representative of feature areas under development. Feature commitments 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.”
