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Understanding I/O Performance with PATROL- Perform and PATROL-Predict Debbie Sheetz Sr. Staff Consultant BMC Software.

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Presentation on theme: "Understanding I/O Performance with PATROL- Perform and PATROL-Predict Debbie Sheetz Sr. Staff Consultant BMC Software."— Presentation transcript:

1 Understanding I/O Performance with PATROL- Perform and PATROL-Predict Debbie Sheetz Sr. Staff Consultant BMC Software

2 C4P075 2 I/O Performance Analysis Overview I/O metric definitions Baseline I/O performance analysis What–if I/O performance analysis

3 C4P075 3 How Important is I/O to Performance? Predict/Visualizer presents a unified view of the system so that the relative contributions of CPU and disk I/O can be assessed Dont solve a problem that you dont have CPU is the dominant factor here

4 C4P075 4 Source of I/O Metrics Key to understanding I/O is to know your metrics Disks are reported/collected as they are defined/known to UNIX or NT This may or may not correspond 1-to-1 to physical units Disk configuration is collected from standard interface for the particular OS Disk statistics are collected from standard interface for the particular OS (same metrics used by iostat, etc.) Analyze/Predict interprets and reports based on these metrics

5 C4P075 5 I/O Configuration Collection Issues Sometimes the disk configuration is reported as Unknown Three possible causes 1. Disk configuration is not available from the OS 2. Standard interface to OS fails to return the disk configuration 3. Collected disk configuration is not matched by an entry in the hardware (.hrw) and.odm RAID is not collected directly This DOES NOT AFFECT the baseline metrics or baseline model calibration For certain what-if disk modeling scenarios, the disk must be identified

6 C4P075 6 Key I/O Metrics A few metrics tell most of the story about disk I/O Disk throughput Data transferred (e.g. bytes, words, etc.) – Disk reads/writes Disk accesses Disk utilization (active time)

7 C4P075 7 I/O Metrics: Throughput Data transferred (e.g. bytes, words, etc.) PATROL-Perform and Predict report I/Os in 4 KB units – Consistency for reporting (Analyze, Visualizer, Predict) – Ease of modeling I/O cross-node and cross-platform Units measured vary by platform – HP, OSF: words Disk Statistics, Words Xfered – Solaris, AIX: blocks Disk Statistics, Blocks Read/Written – NT: bytes NT Physical Disk, Disk Read/Write

8 C4P075 8 I/O Metrics: Throughput Disk accesses (i.e. transfers) Number of times an I/O request was made of the disk – Size of data transfer can vary – Doesnt matter where the I/O is actually serviced: » Physical disk (seek, latency, and data transfer) » Cache on the disk » Cache on the disk controller – Doesnt matter whether RAID or non-RAID Similar metrics collected for UNIX/NT – UNIX Disk Statistics, Transfers – NT NT Physical Disk, Disk Transfers/Sec

9 C4P075 9 I/O Metrics: Throughput Disk reads/writes Number of times a read vs. write I/O request was made of the disk – Size of data transfer can vary Different metrics collected for UNIX/NT – Solaris, AIX Disk Statistics, Blocks Read/Written – HP, OSF Not Available – NT NT Physical Disk, Disk Read/Write Bytes/Sec Reported in Analyze/Predict in 4 KB=I/O rates

10 C4P I/O Metrics: Utilization (Active Time) Disk utilization (active time) Amount of time disk was observed to be actively servicing an I/O request – Doesnt matter where the I/O is actually serviced: » Physical disk (seek, latency, and data transfer) » Cache on the disk » Cache on the disk controller – Doesnt matter whether RAID or non-RAID Should reflect the relative efficiency of I/O processing when compared with disk throughput measures – Use disk service time for this (service time = utilization / IOs)

11 C4P I/O Metrics: Utilization (Active Time) Disk active time Different metrics collected for UNIX/NT – UNIX Disk Statistics, Active Time – NT NT Physical Disk, % Disk Time – Windows 2000 NT Physical Disk, % Idle Time Windows/NT metrics are reinterpreted by Analyze – Perfmon caps calculated utilization at 100% – Observations of collected Windows/NT disk data show utilizations well over 100% – Analyze scales all collected NT times down – Perfmon and Analyze/Predict will not match

12 C4P I/O Metrics Collection Issues If iostat cant see it, the collector cant collect it The OS is supplying the metrics If the metrics are missing or incorrect, both iostat and PATROL-Perform/Predict, etc. will report the same Problem needs to be addressed by the OS vendor Refer any questions about valid I/O metrics to BMC Technical Support Always need to know the exact platform (e.g. HP 11.00, 64-bit) Run iostat and the collector in parallel Use current collector for the platform

13 C4P Baseline I/O Performance Analysis Overview Observe key disk I/O metrics from baseline measurements Identify I/O patterns For the system For a disk or group of disks – Distribution amongst disks For a workload/transaction Determine how important I/O is to overall performance

14 C4P Baseline I/O Performance Analysis Overview Observe key disk I/O metrics from baseline measurements Identify I/O performance characteristics Relative speed of I/O processing Read/write ratios Blocksize used Disk utilization objectives – Distribution amongst disks

15 C4P Baseline Case Study CPU pattern doesnt precisely match I/O pattern

16 C4P Baseline Case Study I/O is dominated by one oracle instance, but there are other contributors Study patterns within days and across days, weeks, etc.

17 C4P Baseline Case Study I/O is the major component of response time during prime time

18 C4P Baseline Case Study Distribution of I/O amongst disks is fairly even

19 C4P I/O Analysis Technique: CUTDISK How to filter I/O data so only the important disks are studied? Use CUT DISK feature In Analyze In Manager If already specified in.an file input to Manager, dont need Manager specification, too Analyze/Predict reports shorter, Visualizer files smaller, Visualizer database smaller, Visualizer graphics easier to present

20 C4P I/O Analysis Technique: CUTDISK Concept is to aggregate I/O from less utilized disks, preserve important disks individually I/Os are NOT removed from the model Choose appropriate threshold I/O rate or Disk utilization may be used Threshold value can be set for a specific purpose – Setting of 0 removes only disks which are not used at all – Setting of 5% utilization removes most disks – Paging disks are never removed

21 C4P I/O Analysis Technique: CUTDISK Specify under Options, Cut Disk Options in Analyze

22 C4P I/O Analysis Technique: CUTDISK Specify under Options, Advanced Features in Manager

23 C4P Baseline Case Study Observe Disk Utilization patterns Utilizations mostly even, most under 40%

24 C4P Baseline Case Study Observe Disk processing efficiency Looks good! Most service times under 5 ms per 4 KB transfer. A few outliers could use a closer look …

25 C4P Baseline Case Study Look at ssd4 High service time isnt so high after all: transfers divided by 9.85 I/Os is 1.3. That means service time is for 1.3 actual data transfers or 9.3 ms per physical transfer.

26 C4P Baseline Case Study Look at ssd3 High service time isnt really high here either: transfers divided by 1.37 I/Os is 7.8. That means service time is for 7.8 actual data transfers or 6.9 ms. Another way to think about this is that the average blocksize is 4 KB / 7.8 or.5 KB.

27 C4P Baseline Case Study In fact, good (larger) blocksizes explain the good disk performance These graphics show roughly a 2:1 ratio between I/Os and transfers, or an 8 KB blocksize

28 C4P Baseline Case Study Conclusion Even though I/O is a major contributor to response time, there are no obvious tuning opportunities Continue to study the key I/O metrics over time Identify trends in I/O performance

29 C4P What-if I/O Performance Analysis Overview Via the Predict model, you can change: I/O patterns For the system – Change in workload volume – Change in the types of workloads For a disk or group of disks – Distribution amongst disks Change in amount of transaction I/O required

30 C4P What-if I/O Performance Analysis Overview Via the Predict model, you can change: I/O performance characteristics Relative speed of I/O processing – Disk configuration change – Blocksize used

31 C4P What-if I/O Performance Analysis Overview Predict shows how this affects performance Performance objectives Workload/transaction response objectives Disk utilization objectives Reports I/O patterns System Distribution amongst disks Reports individual disk performance Can view results in Predict and/or Visualizer

32 C4P What-if Case Study Management wants to know how performance will change if a new RAID disk technology is implemented Study strategy 1. Perform Visualizer analysis of baseline I/O performance characteristics, build baseline model 2. Perform Visualizer analysis of benchmark of I/O using new disk technology (IBM Shark) 3. Use Predict to do what-if

33 C4P What-if Case Study: Benchmark Data Analysis Benchmark demonstrates substantial I/O rate Since current system has high I/O rates, a subset of the benchmark will be studied

34 C4P What-if Case Study: Benchmark Data Analysis Selected subset of the benchmark

35 C4P What-if Case Study: Benchmark Data Analysis Key I/O characteristics: I/Os vs. transfers Ratio of I/Os to transfers is about 5.7, or 23 KB per native I/O access

36 C4P What-if Case Study: Benchmark Data Analysis Key I/O characteristic: reads vs. writes Ratio of reads to writes is about 1.5:1

37 C4P What-if Case Study: Benchmark Data Analysis Key I/O characteristic: service time for 4 KB I/O Predominant service time is about.5 ms

38 C4P What-if Case Study: Benchmark Data Analysis Key I/O characteristic: service time for 4 KB I/O View by controller, disks over 5% utilization Note less efficiency at lower I/O load

39 C4P What-if Case Study: Change Model Only one change is needed in the Predict model Set the disk service time/IO according to the benchmark DO NOT use the hardware table method because more specific info is available Hardware table method applies ratio of new disk type to current disk type Both disk types must be in the hardware table Baseline disk type must be specified

40 C4P What-if Case Study: Change Model Model must be baselined Two methods for changing service time Edit the disk service time/IO in the GUI Use a command file if there are many disks Command file format MODIFY DISK hdisk10 EDISKTIME.5 MODIFY DISK hdisk11 EDISKTIME.5 Etc.

41 C4P What-if Case Study: Modeling Results Model is evaluated and net change is observed << Baseline What–if >>

42 C4P What-if Case Study: Modeling Results Relative reduction in response time reported with relative response time Reduction of 26% for the workload of interest

43 C4P What-if Case Study: Modeling Results Why not a larger reduction? New service time/utilization is about 75% of baseline (.5 ms /.65 ms) for the disks doing the most I/O << Baseline What-if >>

44 C4P What-if Case Study: Modeling Results What else will improve performance more? More even I/O distribution in benchmark

45 C4P What-if Case Study: Modeling Results What else will improve performance more? Possible use of more optimistic service time, e.g..45 ms observed with CUTDISK set at 100 IO/sec Should confirm with more benchmark data and/or vendor

46 C4P What-if Case Study Conclusion Change to new technology will Reduce I/O service time Reduce I/O wait time From reduced utilization (due to service time decrease) From better I/O distribution (due to more even utilizations) Reduction not as large as expected because current I/O performance is already good (.65 ms vs..5 ms) Allows for additional workload growth compared with current technology


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