1. Slide 1 ISTORE Update David Patterson University of California at Berkeley Patterson@cs.berkeley.edu UC Berkeley IRAM Group UC Berkeley ISTORE Group istore-group@cs.berkeley.edu May 2000

2. Slide 2 Lampson: Systems Challenges Systems that work –Meeting their specs –Always available –Adapting to changing environment –Evolving while they run –Made from unreliable components –Growing without practical limit Credible simulations or analysis Writing good specs Testing Performance –Understanding when it doesn’t matter “Computer Systems Research -Past and Future” Keynote address, 17th SOSP, Dec. 1999 Butler Lampson Microsoft

3. Slide 3 Hennessy: What Should the “New World” Focus Be? Availability –Both appliance & service Maintainability –Two functions: »Enhancing availability by preventing failure »Ease of SW and HW upgrades Scalability –Especially of service Cost –per device and per service transaction Performance –Remains important, but its not SPECint “Back to the Future: Time to Return to Longstanding Problems in Computer Systems?” Keynote address, FCRC, May 1999 John Hennessy Stanford

4. Slide 4 The real scalability problems: AME Availability –systems should continue to meet quality of service goals despite hardware and software failures Maintainability –systems should require only minimal ongoing human administration, regardless of scale or complexity Evolutionary Growth –systems should evolve gracefully in terms of performance, maintainability, and availability as they are grown/upgraded/expanded These are problems at today’s scales, and will only get worse as systems grow

5. Slide 5 Principles for achieving AME (1) No single points of failure Redundancy everywhere Performance robustness is more important than peak performance –“performance robustness” implies that real-world performance is comparable to best-case performance Performance can be sacrificed for improvements in AME –resources should be dedicated to AME »compare: biological systems spend > 50% of resources on maintenance –can make up performance by scaling system

6. Slide 6 Principles for achieving AME (2) Introspection –reactive techniques to detect and adapt to failures, workload variations, and system evolution –proactive techniques to anticipate and avert problems before they happen

7. Slide 7 ISTORE-1 hardware platform 80-node x86-based cluster, 1.4TB storage –cluster nodes are plug-and-play, intelligent, network- attached storage “bricks” »a single field-replaceable unit to simplify maintenance –each node is a full x86 PC w/256MB DRAM, 18GB disk –more CPU than NAS; fewer disks/node than cluster ISTORE Chassis 80 nodes, 8 per tray 2 levels of switches 20 100 Mbit/s 2 1 Gbit/s Environment Monitoring: UPS, redundant PS, fans, heat and vibration sensors... Intelligent Disk “Brick” Portable PC CPU: Pentium II/266 + DRAM Redundant NICs (4 100 Mb/s links) Diagnostic Processor Disk Half-height canister

8. Slide 8 ISTORE-1 Status 10 Nodes manufactured; 45 board fabbed, 40 to go Boots OS Diagnostic Processor Interface SW complete PCB backplane: not yet designed Finish 80 node system: Summer 2000

9. Slide 9 Hardware techniques Fully shared-nothing cluster organization –truly scalable architecture –architecture that tolerates partial failure –automatic hardware redundancy

10. Slide 10 Hardware techniques (2) No Central Processor Unit: distribute processing with storage –Serial lines, switches also growing with Moore’s Law; less need today to centralize vs. bus oriented systems –Most storage servers limited by speed of CPUs; why does this make sense? –Why not amortize sheet metal, power, cooling infrastructure for disk to add processor, memory, and network? –If AME is important, must provide resources to be used to help AME: local processors responsible for health and maintenance of their storage

11. Slide 11 Hardware techniques (3) Heavily instrumented hardware –sensors for temp, vibration, humidity, power, intrusion –helps detect environmental problems before they can affect system integrity Independent diagnostic processor on each node –provides remote control of power, remote console access to the node, selection of node boot code –collects, stores, processes environmental data for abnormalities –non-volatile “flight recorder” functionality –all diagnostic processors connected via independent diagnostic network

12. Slide 12 Hardware techniques (4) On-demand network partitioning/isolation –Internet applications must remain available despite failures of components, therefore can isolate a subset for preventative maintenance –Allows testing, repair of online system –Managed by diagnostic processor and network switches via diagnostic network

13. Slide 13 Hardware techniques (5) Built-in fault injection capabilities –Power control to individual node components –Injectable glitches into I/O and memory busses –Managed by diagnostic processor –Used for proactive hardware introspection »automated detection of flaky components »controlled testing of error-recovery mechanisms –Important for AME benchmarking (see next slide)

14. Slide 14 “Hardware” techniques (6) Benchmarking –One reason for 1000X processor performance was ability to measure (vs. debate) which is better »e.g., Which most important to improve: clock rate, clocks per instruction, or instructions executed? –Need AME benchmarks “what gets measured gets done” “benchmarks shape a field” “quantification brings rigor”

15. Slide 15 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

16. Slide 16 Results are most accessible graphically –plot change in QoS metrics over time –compare to “normal” behavior? »99% confidence intervals calculated from no-fault runs Benchmark Availability? Methodology for reporting results

17. Slide 17 Example single-fault result Compares Linux and Solaris reconstruction –Linux: minimal performance impact but longer window of vulnerability to second fault –Solaris: large perf. impact but restores redundancy fast Linux Solaris

18. Slide 18 Reconstruction Policy Linux: favors performance over data availability –automatically-initiated reconstruction, idle bandwidth –virtually no performance impact on application –very long window of vulnerability (>1hr for 3GB RAID) Solaris: favors data availability over app. perf. –automatically-initiated reconstruction at high BW –as much as 34% drop in application performance –short window of vulnerability (10 minutes for 3GB) Windows: favors neither! –manually-initiated reconstruction at moderate BW –as much as 18% app. performance drop –somewhat short window of vulnerability (23 min/3GB)

19. Slide 19 Transient error handling Policy Linux is paranoid with respect to transients –stops using affected disk (and reconstructs) on any error, transient or not »fragile: system is more vulnerable to multiple faults »disk-inefficient: wastes two disks per transient »but no chance of slowly-failing disk impacting perf. Solaris and Windows are more forgiving –both ignore 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 that detects streams of transients

20. Slide 20 Software techniques Fully-distributed, shared-nothing code –centralization breaks as systems scale up O(10000) –avoids single-point-of-failure front ends Redundant data storage –required for high availability, simplifies self-testing –replication at the level of application objects »application can control consistency policy »more opportunity for data placement optimization

21. Slide 21 Software techniques (2) “River” storage interfaces –NOW Sort experience: performance heterogeneity is the norm »e.g., disks: outer vs. inner track (1.5X), fragmentation »e.g., processors: load (1.5-5x) –So demand-driven delivery of data to apps »via distributed queues and graduated declustering »for apps that can handle unordered data delivery –Automatically adapts to variations in performance of producers and consumers –Also helps with evolutionary growth of cluster

22. Slide 22 Software techniques (3) Reactive introspection –Use statistical techniques to identify normal behavior and detect deviations from it –Policy-driven automatic adaptation to abnormal behavior once detected »initially, rely on human administrator to specify policy »eventually, system learns to solve problems on its own by experimenting on isolated subsets of the nodes one candidate: reinforcement learning

23. Slide 23 Software techniques (4) Proactive introspection –Continuous online self-testing of HW and SW »in deployed systems! »goal is to shake out “Heisenbugs” before they’re encountered in normal operation »needs data redundancy, node isolation, fault injection –Techniques: »fault injection: triggering hardware and software error handling paths to verify their integrity/existence »stress testing: push HW/SW to their limits »scrubbing: periodic restoration of potentially “decaying” hardware or software state self-scrubbing data structures (like MVS) ECC scrubbing for disks and memory

24. Slide 24 Initial Applications ISTORE is not one super-system that demonstrates all these techniques! –Initially provide middleware, library to support AME goals Initial application targets –cluster web/email servers »self-scrubbing data structures, online self-testing »statistical identification of normal behavior –information retrieval for multimedia data »self-scrubbing data structures, structuring performance-robust distributed computation

25. Slide 25 A glimpse into the future? System-on-a-chip enables computer, memory, redundant network interfaces without significantly increasing size of disk ISTORE HW in 5-7 years: –building block: 2006 MicroDrive integrated with IRAM »9GB disk, 50 MB/sec from disk »connected via crossbar switch –If low power, 10,000 nodes fit into one rack! O(10,000) scale is our ultimate design point

26. Slide 26 Future Targets Maintenance in DoD application Security in Computer Systems Computer Vision

27. Slide 27 Maintenance in DoD systems Introspective Middleware, Builtin Fault Injection, Diagnostic Computer, Isolatable Subsystems... should reduce Maintenance of DoD Hardware and Software systems Is Maintenance a major concern of DoD? Does Improved Maintenance fit within Goals of Polymorphous Computing Architecture?

28. Slide 28 Security in DoD Systems? Separate Diagnostic Processor and Network gives interesting Security possibilities –Monitoring of behavior by separate computer –Isolation of portion of cluster from rest of network –Remote reboot, software installation

29. Slide 29 Attacking Computer Vision Analogy: Computer Vision Recognition in 2000 like Computer Speech Recognition in 1985 –Pre 1985 community searching for good algorithms: classic AI vs. statistics? –By 1985 reached consensus on statistics –Field focuses and makes progress, uses special hardware –Systems become fast enough that can train systems rather than preload information, which accelerates progress –By 1995 speech regonition systems starting to deploy –By 2000 widely used, available on PCs

30. Slide 30 Computer Vision at Berkeley Jitendra Malik believes has an approach that is very promising 2 step process: 1) Segmentation: Divide image into regions of coherent color, texture and motion 2) Recognition: combine regions and search image database to find a match Algorithms for 1) work well, just slowly (300 seconds per image using PC) Algorithms for 2) being tested this summer using hundreds of PCs; will determine accuracy

31. Slide 31 Human Quality Computer Vision Suppose Algorithms Work: What would it take to match Human Vision? At 30 images per second: segmentation –Convolution and Vector-Matrix Multiply of Sparse Matrices (10,000 x 10,000, 10% nonzero/row) –32-bit Floating Point –300 seconds on PC (assuming 333 MFLOPS) => 100G FL Ops/image –30 Hz => 3000 GFLOPs machine to do segmentation

32. Slide 32 Human Quality Computer Vision At 1 / second: object recognition –Human can remember 10,000 to 100,000 objects per category (e.g., 10k faces, 10k Chinese characters, high school vocabulary of 50k words,..) –To recognize a 3D object, need ~10 2D views –100 x 100 x 8 bit (or fewer bits) per view => 10,000 x 10 x 100 x 100 bytes or 10 9 bytes –Pruning using color and texture and by organizing shapes into an index reduce shape matches to 1000 –Compare 1000 candidate merged regions with 1000 candidate object images –If 10 hours on PC (333 MFLOPS) => 12000 GFLOPS

33. Slide 33 ISTORE Successor does Human Quality Vision? 10,000 nodes with System-On-A-Chip + Microdrive + network –1 to 10 GFLOPS/node => 10,000 to 100,000 GFLOPS –High Bandwidth Network –1 to 10 GB of Disk Storage per Node => can replicate images per node – Need Dependability, Maintainability advances to keep 10,000 nodes useful Human quality vision useful for DoD Apps? Retrainable recognition?

34. Slide 34 Conclusions (1): ISTORE Availability, Maintainability, and Evolutionary growth are key challenges for server systems –more important even than performance ISTORE is investigating ways to bring AME to large-scale, storage-intensive servers –via clusters of network-attached, computationally- enhanced storage nodes running distributed code –via hardware and software introspection –we are currently performing application studies to investigate and compare techniques Availability benchmarks a powerful tool? –revealed undocumented design decisions affecting SW RAID availability on Linux and Windows 2000