hadoop分布式系统存储数据库云计算(6).pptVIP

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 Empirical Evaluation (2) Comparison of Tuning Quality 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 iTuned’s Scalability Features (2) Identify important parameters quickly Using sensitivity analysis with a few experiments #Parameters = 9, #Experiments = 10 iTuned’s Scalability Features (3) 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 Back of the Envelope Calculation 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 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 .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 Resources eXL script Language processor Run-time engine .eX Current Focus of .eX Parts of an eXL script Query: (approx.) response surface mapping, search Expt. setup monitoring Constraints optimization: resources, cost, time I1 I2 O1 … … Are more experiments needed? Process output to extract information Plan next set of experiments Conduct experiments on workbench Yes Result Au