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Experiment-driven System Management

Experiment-driven System Management. Shivnath Babu Duke University Joint work with Songyun Duan, Herodotos Herodotou, and Vamsidhar Thummala. Managing DBs in Small to Medium Business Enterprises (SMBs). Peter is a system admin in an SMB Manages the database (DB)

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Experiment-driven System Management

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  1. Experiment-driven System Management Shivnath Babu Duke University Joint work with Songyun Duan, Herodotos Herodotou, and Vamsidhar Thummala

  2. Managing DBs in Small to Medium Business Enterprises (SMBs) • Peter is a system admin in an SMB • Manages the database (DB) • SMB cannot afford a DBA • Suppose Peter has to tune a poorly-performing DB • Design advisor may not help • Maybe the problem is with DB configuration parameters Database (DB)

  3. Tuning DB Configuration Parameters • Parameters that control • Memory distribution • I/O optimization • Parallelism • Optimizer’s cost model • Number of parameters ~ 100 • 15-25 critical params depending on OLAP Vs. OLTP • Few holistic parameter tuning tools available • Peter may have to resort to 1000+ page tuning manuals or rules of thumb from experts • Can be a frustrating experience

  4. Response Surfaces 2-dim Projection of a 11-dim Surface TPC-H 4 GB DB size, 1 GB memory, Query 18

  5. DBA’s Approach to Parameter Tuning • DBAs run experiments • Here, an experiment is a run of the DB workload with a specific parameter configuration • Common strategy: vary one DB parameter at a time

  6. Experiment-driven Management Result Mgmt. task Are more experiments needed? Yes Process output to extract information Plan next set of experiments Conduct experiments on workbench Goal: Automate this process

  7. Roadmap • Use cases of experiment-driven mgmt. • Query tuning, benchmarking, Hadoop, testing, … • iTuned: Tool for DB conf parameter tuning • End-to-end application of experiment-driven mgmt. • .eX: Language and run-time system that brings experiment-driven mgmt. to users & tuning tools

  8. What is an Experiment? • Depends on the management task • Pay some extra cost, get new information in return • Even for a specific management task, there can be spectrum of possible experiments

  9. Uses of Experiment-driven Mgmt. • DB conf parameter tuning

  10. Uses of Experiment-driven Mgmt. DB conf parameter tuning MapReduce job tuning in Hadoop

  11. Uses of Experiment-driven Mgmt. • DB conf parameter tuning • MapReduce job tuning in Hadoop • Server benchmarking • Capacity planning • Cost/perf modeling

  12. Uses of Experiment-driven Mgmt. • Tuning “problem queries” <100, 187> <100, 187> <100, 436> <2473, 7496> <2473, 7496> <1, 0.6> <65, 309> <380459, 229739> <65, 309> <1629, 1615> <1, 1> <1, 1> <Estimated, Actual> Cardinality

  13. Uses of Experiment-driven Mgmt. • Tuning “problem queries”

  14. Uses of Experiment-driven Mgmt. • DB conf parameter tuning • MapReduce job tuning in Hadoop • Server benchmarking • Capacity planning • Cost/perf modeling • Tuning “problem queries” • Troubleshooting • Testing • Canary in the server farm (James Hamilton, Amazon) • …

  15. Roadmap • Use cases of experiment-driven mgmt. • Query tuning, benchmarking, Hadoop, testing, … • iTuned: Tool for DB conf parameter tuning • End-to-end application of experiment-driven mgmt. • .eX: Language and run-time system that brings experiment-driven mgmt. to users & tuning tools

  16. Problem Abstraction • Unknown response surface: y = F(X) • X = Parameters x1, x2, …, xm • Each experiment gives a <Xi,yi> sample • Set DB to conf Xi • Run workload that needs tuning • Measure performance yi at Xi • Goal: Find high performance setting with low total cost of running experiments

  17. Utility(X) Example • Goal: Compute the potential utility of candidate experiments Where to do the next experiment?

  18. iTuned’s Adaptive Sampling Algorithm for Experiment Planning // Phase I: Bootstrapping • Conduct some initial experiments // Phase II: Sequential Sampling • Loop: Until stopping condition is reached • Identify candidate experiments to do next • Based on current samples, estimate the utility of each candidate experiment • Conduct the next experiment at the candidate with highest utility

  19. Utility of an Experiment • Let <X1,y1>--<Xn,yn> be the samples from n experiments done so far • Let <X*,y*> be the best setting so far (i.e., y* = mini yi) • wlg assuming minimization • U(X), Utility of experiment at X is // y = F(X) • y* - y if y* > y • 0 otherwise • However, U(X) poses a chicken-and-egg problem • y will be known only after experiment is run at X • Goal: Compute expected utility EU(X)

  20. Expected Utility of an Experiment • Suppose we have the probability density function of y (y is the perf at X) • Prob(y = v | <Xi,yi> for i=1,…,n) • Then, EU(X) = sv=-1 U(X) Prob(y = v) dv EU(X) = sv=-1 (y* - v) Prob(y = v) dv • Goal: Compute Prob(y = v | <Xi,yi> for i=1,…,n) v=+1 v=y*

  21. Model: Gaussian Process Representation (GRS) of a Response Surface • GRS models the response surface as: y(X) = g(X) + Z(X) (+ (X) for measurement error) • E.g., g(X) = x1 – 2x2 + 0.1x12 (Learned using common techniques) • Z: Gaussian Process to capture regression residual

  22. Primer on Gaussian Process • Univariate Gaussian distribution • G = N(,) • Described by mean , variance  • Multivariate Gaussian distribution • [G1, G2, …, Gn] • Described by mean vector and covariance matrix • Gaussian Process • Generalizes multivariate Gaussian to arbitrary number of dimensions • Described by mean and covariance functions

  23. Model: Gaussian Process Representation (GRS) of a Response Surface • GRS captures the response surface as: y(X) = g(X) + Z(X) (+ (X) for measurement error) • If Z is a Gaussian process, then:  [Z(X1),…,Z(Xn),Z(X)] is multivariate Gaussian Z(X) | Z(X1),…,Z(Xn) is a univariate Gaussian  y(X) is a univariate Gaussian

  24. Parameters of the GRS Model • [Z(X1),…,Z(Xn)] is multivariate Gaussian • Z(Xi) has zero mean • Covariance(Z(Xi),Z(Xj)) / exp(k–k |xik – xjk|k) • Residuals at nearby points have higher correlation • k, ½k learned from <X1,y1>--<Xn,yn>

  25. Use of the GRS Model v=y* • Recall our goals to compute • EU(X) = sv=-1 (y* - v) Prob(y = v) dv • Prob(y = v | <Xi,yi> for i=1,…,n) • Lemma: Using the GRS, we can compute the mean (X) and variance 2(X) of the Gaussian y(X) • Theorem: EU(X) has a closed form that is a product of: • Term that depends on (y* - (X)) • Term that depends on (X) • It follows that settings X with high EU are either: • Close to known good settings (for exploitation) • In highly uncertain regions (for exploration)

  26. Example • Settings X with high EU are either: • Close to known good settings (high y*-(X)) • In highly uncertain regions (high (X)) Unknown actual surface (X) 4(X) y* EU(X)

  27. Where to Conduct Experiments? Clients Clients Clients TestPlatform Middle Tier DBMS TestData ProductionPlatform DBMS DBMS Data Data StandbyPlatform Write Ahead Log (WAL) shipping

  28. iTuned’s Solution • Exploit underutilizedresources with minimal impact on production workload • DBA/User designates resources where experiments can be run • E.g., production/standby/test • DBA/User specifies policies that dictate when experiments can be run • Separate regular use (home) from experiments (garage) • Example: If CPU, mem, & disk utilization < 10% for past 15mins, then resource can be used for experiments

  29. One Implementation of Home/Garage Home Home Garage Workbench for experiments Apply WAL Apply WAL DBMS DBMS DBMS Clients Clients Clients Standby Machine Middle Tier ProductionPlatform WAL shipping DBMS Data Copy on Write Data iTuned Interface Engine Experiment Planner & Scheduler

  30. Overheads are Low

  31. Empirical Evaluation (1) • Cluster of machines with 2GHz processors and 3GB memory • Two database systems: PostgreSQL & MySQL • Various workloads • OLAP: Mixes of heavy-weight TPC-H queries • Varying #queries, #query_types, and MPL • Scale factors 1 and 10 • OLTP: TPC-W and RUBiS • Tuning of up to 30 configuration parameters

  32. Empirical Evaluation (2) • Techniques compared • Default parameter settings shipped (D) • Manual rule-based tuning (M) • Smart Hill Climbing (S): State-of-the-art technique • Brute-Force search (B): Run many experiments to find approximation to optimal setting • iTuned (I) • Evaluation metrics • Quality: workload running time after tuning • Efficiency: time needed for tuning

  33. Comparison of Tuning Quality

  34. iTuned’s Scalability Features (1) • Identify important parameters quickly • Run experiments in parallel • Stop low-utility experiments early • Compress the workload • Work in progress: • Apply database-specific knowledge • Incremental tuning • Interactive tuning

  35. #Parameters = 9, #Experiments = 10 iTuned’s Scalability Features (2) • Identify important parameters quickly • Using sensitivity analysis with a few experiments

  36. iTuned’s Scalability Features (3)

  37. Roadmap • Use cases of experiment-driven mgmt. • Query tuning, benchmarking, Hadoop, testing, … • iTuned: Tool for DB conf parameter tuning • End-to-end application of experiment-driven mgmt. • .eX: Language and run-time system that brings experiment-driven mgmt. to users & tuning tools

  38. Back of the Envelope Calculation • Cost of running these experiments for 1 day on Amazon Web Serv. • Server: $10/day • Storage: $0.4/day • I/O: $5/day • TOTAL: $15/day • DBAs cost $300/day; Consultants cost $100/hr • 1 Day of experiments gives a wealth of info. • TPC-H, TPC-W, RUBiS workloads; 10-30 conf. params

  39. Run-time engine .eX: Power of Experiments to the People • Users & tools express needs as scripts in eXL (eXperiment Language) • .eX engine plans and conducts experiments on designated resources • Intuitive visualization of results eXL script Language processor .eX Resources

  40. Result Are more experiments needed? Yes Process output to extract information Plan next set of experiments Conduct experiments on workbench Current Focus of .eX • Parts of an eXL script • Query: (approx.) response surface mapping, search • Expt. setup & monitoring • Constraints & optimization: resources, cost, time Automatically generate the experiment-driven workflow

  41. Summary • Automated expt-driven mgmt: The time has come • Need, infrastructure, & promise are all there • We have built many tools around this paradigm • http://www.cs.duke.edu/~shivnath/dotex.html • Poses interesting questions and challenges • Make it easy for users/admins to do expts • Make experiments first-class citizens in systems

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