1. Slide 1 Towards Benchmarks for Availability, Maintainability, and Evolutionary Growth (AME) A Case Study of Software RAID Systems Aaron Brown 2000 Winter IRAM/ISTORE Retreat

2. Slide 2 Outline Motivation: why new benchmarks, why AME? Availability benchmarks: a general approach Case study: availability of Software RAID Conclusions and future directions

3. Slide 3 Why benchmark AME? Most benchmarks measure performance Today’s most pressing problems aren’t about performance –online service providers face new problems as they rapidly deploy, alter, and expand Internet services »providing 24x7 availability(A) »managing system maintenance(M) »handling evolutionary growth(E) –AME issues affect in-house providers and smaller installations as well »even UCB CS department

4. Slide 4 Why benchmarks? Growing consensus that we need to focus on these problems in the research community –HotOS-7: “No-Futz Computing” –Hennessy at FCRC: »“other qualities [than performance] will become crucial: availability, maintainability, scalability” »“if access to services on servers is the killer app-- availability is the key metric” –Lampson at SOSP ’99: »big challenge for systems research is building “systems that work: meet specifications, are always available, evolve while they run, grow without practical limits” Benchmarks shape a field! –they define what we can measure

5. Slide 5 The components of AME Availability –what factors affect the quality of service delivered by the system, and by how much/how long? –how well can systems survive typical failure scenarios? Maintainability –how hard is it to keep a system running at a certain level of availability and performance? –how complex are maintenance tasks like upgrades, repair, tuning, troubleshooting, disaster recovery? Evolutionary Growth –can the system be grown incrementally with heterogeneous components? –what’s the impact of such growth on availability, maintainability, and sustained performance?

6. Slide 6 Outline Motivation: why new benchmarks, why AME? Availability benchmarks: a general approach Case study: availability of Software RAID Conclusions and future directions

7. Slide 7 How can we measure availability? Traditionally, percentage of time system is up –time-averaged, binary view of system state (up/down) Traditional metric is too inflexible –doesn’t capture degraded states »a non-binary spectrum between “up” and “down” –time-averaging discards important temporal behavior »compare 2 systems with 96.7% traditional availability: system A is down for 2 seconds per minute system B is down for 1 day per month Solution: measure variation in system quality of service metrics over time

8. Slide 8 Example Quality of Service metrics Performance –e.g., user-perceived latency, server throughput Degree of fault-tolerance Completeness –e.g., how much of relevant data is used to answer query Accuracy –e.g., of a computation or decoding/encoding process Capacity –e.g., admission control limits, access to non-essential services

9. Slide 9 Availability benchmark methodology Goal: quantify variation in QoS metrics as events occur that affect system availability Leverage existing performance benchmarks –to generate fair workloads –to measure & trace quality of service metrics Use fault injection to compromise system –hardware faults (disk, memory, network, power) –software faults (corrupt input, driver error returns) –maintenance events (repairs, SW/HW upgrades) Examine single-fault and multi-fault workloads –the availability analogues of performance micro- and macro-benchmarks

10. Slide 10 Results are most accessible graphically –plot change in QoS metrics over time –compare to “normal” behavior »99% confidence intervals calculated from no-fault runs Methodology: reporting results Graphs can be distilled into numbers –quantify distribution of deviations from normal behavior, compute area under curve for deviations,...

11. Slide 11 Outline Motivation: why new benchmarks, why AME? Availability benchmarks: a general approach Case study: availability of Software RAID Conclusions and future directions

12. Slide 12 Case study Software RAID-5 plus web server –Linux/Apache vs. Windows 2000/IIS Why software RAID? –well-defined availability guarantees »RAID-5 volume should tolerate a single disk failure »reduced performance (degraded mode) after failure »may automatically rebuild redundancy onto spare disk –simple system –easy to inject storage faults Why web server? –an application with measurable QoS metrics that depend on RAID availability and performance

13. Slide 13 Benchmark environment: metrics QoS metrics measured –hits per second »roughly tracks response time in our experiments –degree of fault tolerance in storage system Workload generator and data collector –SpecWeb99 web benchmark »simulates realistic high-volume user load »mostly static read-only workload; some dynamic content »modified to run continuously and to measure average hits per second over each 2-minute interval

14. Slide 14 Benchmark environment: faults Focus on faults in the storage system (disks) How do disks fail? –according to Tertiary Disk project, failures include: »recovered media errors »uncorrectable write failures »hardware errors (e.g., diagnostic failures) »SCSI timeouts »SCSI parity errors –note: no head crashes, no fail-stop failures

15. Slide 15 Disk fault injection technique To inject reproducible failures, we replaced one disk in the RAID with an emulated disk –a PC that appears as a disk on the SCSI bus –I/O requests processed in software, reflected to local disk –fault injection performed by altering SCSI command processing in the emulation software Types of emulated faults: –media errors (transient, correctable, uncorrectable) –hardware errors (firmware, mechanical) –parity errors –power failures –disk hangs/timeouts

16. Slide 16 System configuration RAID-5 Volume: 3GB capacity, 1GB used per disk –3 physical disks, 1 emulated disk, 1 emulated spare disk 2 web clients connected via 100Mb switched Ethernet IBM 18 GB 10k RPM Server AMD K6-2-333 64 MB DRAM Linux or Win2000 IDE system disk = Fast/Wide SCSI bus, 20 MB/sec Adaptec 2940 RAID data disks IBM 18 GB 10k RPM SCSI system disk Disk Emulator AMD K6-2-350 Windows NT 4.0 ASC VirtualSCSI lib. Adaptec 2940 emulator backing disk (NTFS) AdvStor ASC-U2W UltraSCSI Emulated Spare Disk Emulated Disk

17. Slide 17 Results: single-fault experiments One exp’t for each type of fault (15 total) –only one fault injected per experiment –no human intervention –system allowed to continue until stabilized or crashed Four distinct system behaviors observed (A) no effect: system ignores fault (B) RAID system enters degraded mode (C) RAID system begins reconstruction onto spare disk (D) system failure (hang or crash)

18. Slide 18 (D) system failure System behavior: single-fault (A) no effect(B) enter degraded mode (C) begin reconstruction

19. Slide 19 System behavior: single-fault (2) –Windows ignores benign faults –Windows can’t automatically rebuild –Linux reconstructs on all errors –Both systems fail when disk hangs

20. Slide 20 Interpretation: single-fault exp’ts Linux and Windows take opposite approaches to managing benign and transient faults –these faults do not necessarily imply a failing disk »Tertiary Disk: 368/368 disks had transient SCSI errors; 13/368 disks had transient hardware errors, only 2/368 needed replacing. –Linux is paranoid and stops using a disk on any error »fragile: system is more vulnerable to multiple faults »but no chance of slowly-failing disk impacting perf. –Windows ignores most benign/transient faults »robust: less likely to lose data, more disk-efficient »less likely to catch slowly-failing disks and remove them Neither policy is ideal! –need a hybrid?

21. Slide 21 Results: multiple-fault experiments Scenario (1) disk fails (2) data is reconstructed onto spare (3) spare fails (4) administrator replaces both failed disks (5) data is reconstructed onto new disks Requires human intervention –to initiate reconstruction on Windows 2000 »simulate 6 minute sysadmin response time –to replace disks »simulate 90 seconds of time to replace hot-swap disks

22. Slide 22 System behavior: multiple-fault Windows reconstructs ~3x faster than Linux Windows reconstruction noticeably affects application performance, while Linux reconstruction does not Windows 2000/IIS Linux/ Apache

23. Slide 23 Interpretation: multi-fault exp’ts Linux and Windows have different reconstruction philosophies –Linux uses idle bandwidth for reconstruction »little impact on application performance »increases length of time system is vulnerable to faults –Windows steals app. bandwidth for reconstruction »reduces application performance »minimizes system vulnerability »but must be manually initiated (or scripted) –Windows favors fault-tolerance over performance; Linux favors performance over fault-tolerance »the same design philosophies seen in the single-fault experiments

24. Slide 24 Maintainability Observations Scenario: administrator accidentally removes and replaces live disk in degraded mode –double failure; no guarantee on data integrity –theoretically, can recover if writes are queued –Windows recovers, but loses active writes »journalling NTFS is not corrupted »all data not being actively written is intact –Linux will not allow removed disk to be reintegrated »total loss of all data on RAID volume!

25. Slide 25 Maintainability Observations (2) Scenario: administrator adds a new spare –a common task that can be done with hot-swap drive trays –Linux requires a reboot for the disk to be recognized –Windows can dynamically detect the new disk Windows 2000 RAID is easier to maintain –easier GUI configuration –more flexible in adding disks –SCSI rescan and NTFS deal with administrator goofs –less likely to require administration due to transient errors »BUT must manually initiate reconstruction when needed

26. Slide 26 Outline Motivation: why new benchmarks, why AME? Availability benchmarks: a general approach Case study: availability of Software RAID Conclusions and future directions

27. Slide 27 Conclusions AME benchmarks are needed to direct research toward today’s pressing problems Our availability benchmark methodology is powerful –revealed undocumented design decisions in Linux and Windows 2000 software RAID implementations »transient error handling »reconstruction priorities Windows & Linux SW RAIDs are imperfect –but Windows is easier to maintain, less likely to fail due to double faults, and less likely to waste disks –if spares are cheap and plentiful, Linux auto- reconstruction gives it the edge

28. Slide 28 Future Directions Add maintainability –use methodology from availability benchmark –but include administrator’s response to faults –must develop model of typical administrator behavior –can we quantify administrative work needed to maintain a certain level of availability? Expand availability benchmarks –apply to other systems: DBMSs, mail, distributed apps –use ISTORE-1 prototype »80-node x86 cluster with built-in fault injection, diags. Add evolutionary growth –just extend maintainability/availability techniques?

29. Slide 29 Backup Slides

30. Slide 30 Availability Example: SW RAID Win2k/IIS, Linux/Apache on software RAID-5 volumes Windows gives more bandwidth to reconstruction, minimizing fault vulnerability at cost of app performance –compare to Linux, which does the opposite Windows 2000/IIS Linux/ Apache

31. Slide 31 System behavior: multiple-fault Windows reconstructs ~3x faster than Linux Windows reconstruction noticeably affects application performance, while Linux reconstruction does not (1) data disk faulted (2) reconstruction (3) spare faulted (4) disks replaced (5) reconstruction Windows 2000/IIS Linux/ Apache