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ff@8; ʚ;J%5ʚ;g4ddddm 0zppp@ <4dddd@k 0t1 <4BdBd@l 0tg4<d<d@o 0p p %0___PPT10 ___PPT9nnIt>"ՌPNG  IHDR +tsRGB PLTEfffm cmPPJCmp0712 7tRNS@fIDATWc`  SP1H $XLIENDB`? -O  =k>_FUnderstanding and dealing with operator mistakes in Internet servicesxK. Nagaraja, F. Oliveira, R. Bianchini, R. Martin, T. Nguyen, Rutgers University OSDI 2003 Vivo Project http://vivo.cs.rutgers.edu (based on slides from the authors OSDI presentation):< B; 0h` Motivation  Internet services are ubiquitous, e.g., Google, Yahoo!, Ebay, etc. Expect 24 x 7 availability, but service outages still happen! A significant number of outages in Internet services are result of operator actions 1: Architecture is complex 2: Systems are constantly evolving 3: Lack of tools for operators to reason about the impact of their actions: Offline testing, emulation, simulation Very little detail on operator mistakes Details strongly guarded by companies and administratorsC>T(9C>T  (9b This workzUnderstanding: Gather detailed data on operators mistakes What categories of mistakes? What s the impact on the service? How do mistakes correlate with experience, impact? Caveat: this is not a complete study of operator behavior Approaches to deal with operator mistakes: prevention, recovery, automation Validation: Allow operators to evaluate the correctness of their actions prior to exposing them to the service Like offline testing, but: Virtual environment (extension of online environment) Real workload Migration back and forth with minimal operator involvementx<<r:  c ContributionsDetailed information on operator tasks and mistakes 43 exp. - detailed data on operator behavior inc. 42 mistakes 64% immediately degraded throughput 57% were software configuration mistakes Human experiments are possible and valuable! Designed and prototyped a validation infrastructure Implemented on 2 cluster-based services: cooperative Web server (PRESS) and a multi-tier auction service 2 techniques to allow operators to validate their actions Demonstrated validation is a promising technique for reducing impact of operator mistakes 66% of all mistakes observed in operator study caught 6/9 mistakes caught in live operator exp. w/ validation Successfully tested with synthetically injected mistakes44Z4#"  4Zd Talk outline Approach and contributions Operator study: Understanding the mistakes Representative environment Choice of human subjects and experiments Results Validation: Preventing exposure of mistakes Conclusion and future workJFLH+LHeMulti-tiered Internet services fTasks, operators & trainingTasks  two categories Scheduled maintenance tasks (proactive), e.g. upgrade sw Diagnose-and-repair tasks (reactive), e.g. disk failure Operator composition 14 computer science graduate students 5 professional programmers (Ask Jeeves) 2 sysadmins from our department Categorization of operators  w/ filled in questionnaire 11 novices  some familiarity with set up 5 intermediates  experience with a similar service 5 experts - in-charge of a service requiring high uptime Operator training Novice operators given warm-up tasks Material describing service, and detailed steps for tasksqn9_qn  9_>M WgExperimental setup:Service 3-tier auction service, and client emulator from Rice University s DynaServer Project Loaded at 35% of capacity Machines 2 Web servers (Apache), 5 application servers (Tomcat), 1 database machine (MYSQL) Operator assistance & data capture Monitor service throughput Modified bash shell for command and result trace Manual observation Noting anomalies in operator behavior Bailing out  lost operatorsq U$LDq U  $LDK Ih Example trace]Task: Add an application server Mistake: Apache misconfiguration Impact: Degraded throughput : = =iSampling of other mistakesNAdding a new application server Omission of new application server from backend member list Syntax errors, duplicate entries, wrong hostnames Launching the wrong version of software Migrating the database for performance upgrade Incorrect privileges for accessing the database Security vulnerability Database installed on wrong disk  /0! / 0!j&Operator mistakes: Category vs. impact 64% of all mistakes had immediate impact on service performance 36% resulted in latent faults Obs. #1: Significant no. of mistakes can be checked by testing with a realistic environment Obs. #2: Undetectable latent errors will still require online-recovery techniques@@@,^YOkOperator mistakesMisconfigurations account for 57% of all errors Config. mistakes spanning multiple components are more likely (global misconfigurations) Obs. #1: Tools to manipulate & check configs are crucial Obs. #2: Careful maintaining multiple versions of s/w@0Yo0Yol@" 3lOperator categoriesOExperts also made mistakes! Complexity of tasks executed by experts were higher*44mSummary of operator studyT43 experiments 42 mistakes 27 (64%) mistakes caused immediate impact on service performance 24 (57%) were software configuration mistakes Mistakes were made across all operator categories Trace of operator commands & service performance for all experiments Available at http://vivo.cs.rutgers.eduV(I%x(n Talk outline Approach and contributions Operator study: Understanding the mistakes Validation: Preventing exposure of mistakes Technique Experimental evaluation Conclusion and future workJr"F,"o@Validation of operator s actionsValidation Allow operator to check correctness of his/her actions prior to exposing their impact to the service interface (clients) Correctness is tested by: Migrate the component(s) to virtual sand-box environment, Subject to a real load, Compare behavior to a known correct one, and Migrate back to online environment Types of validation: Replica-based: Compare with online replica (real time) Trace-based: Compare with logged behavior ZZZ#ZZaZ #  ap%Validating a component: Replica-based q#Validating a component: Trace-based rImplementation detailsShunting performed in middleware layer Each request tagged with a unique ID all along the request path Component proxies can be constructed with little effort (mySQL proxy is ~ 384NCSL (402kNCSL) Reuse discovery and communication interfaces, common messaging core State management requires well-defined export and import API Stateful servers often support such API Comparator functions to detect errors Simple throughput, flow, and content comparators'Z@Z]ZEZ=Z(Z&Z1Z'@]E  =(&1 s!Validating our prototype: resultsYLive operator experiments Operator given option of type of validation, duration, and to skip validation Validation caught 6 out of 9 mistakes from 8 experiments with validation Mistake-injection experiments Validation caught errors in data content (inaccessible files, corrupted files) and configuration mistakes (incorrect # of workers in Web Server degraded throughput) Operator-emulation experiments Operator command scripts derived from the 42 operator mistakes Both trace-based and replica validation caught 22 mistakes Multi-component validation caught 4 latent (component interaction) mistakes{L  {Lt#Reduction in impact with validation uFewer mistakes with validation vShunting & buffering overheadsShunting overhead for replica-based validation 39% additional CPU All requests and responses are captured and forwarded to validation slice Trace-based validation is slightly better 32 % additional CPU Overhead is incurred on single component, and only during validation Various optimizations can reduce overhead to 13-22% Examples: response summary (64byte), sampling (session boundaries) Buffering capacity during state check pointing and duplication Required to buffer only about 150 requests for small state sizesE4C?A/tF4C  ?Aw"Caveats, limitations & open IssuesNon-determinism increases complexity of comparators and proxies E.g., choice of back-end server, remote cache vs. local disk, pseudo-random session-id, time stamps Hard state management may require operator intervention Component requires initialization prior to online migration Bootstrapping the validation Validating an intended modification of service behavior  nothing to compare with! 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