Treinamento em Análise Quantitativa & Planejamento de Capacidade. Virgilio A. F. Almeida

Transcription

1 Treinamento em Análise Quantitativa & Planejamento de Capacidade Virgilio A. F. Almeida DATAPREV Rio de Janeiro 16 Dezembro de 2009 Módulo Caracterização de Carga Departamento de Ciência da Computação Universidade Federal de Minas Gerais - UFMG

2 Planejamento da infraestrutura de datacenters: desafios Em exces sso Adquirir o suficiente agora PerformanceP f Disponibilidade Custo ROI Energia SLA!!! in nsufici iente Muito cedo Muito tarde

3 Alguns pontos importantes

4 Alguns pontos de SLA Setting Realistic and Appropriate SLAs Consensus on performance from all stakeholders The business level decision makers:cio, CTO,CFO CFO and Dept heads The rest: developers, testers, infrastructure team and end users To build a business case to justify investments in staff, construction ti of (expensive) )test tenvironment, purchase of automated performance testing solution

5 Alguns pontos de SLA Web < 8 seconds Source: Scott Barber

6 Alguns pontos de SLA

7 Alguns pontos de SLA

8 Methodologies for planning the capacity of datacenter infrastructure

9 Capacity Planning Process Business Models & Measurable Goals Cost-Performance Analysis & Actions Understand Service Architecture Predict Service Performance Performance Model Test environment Characterize the Workload Model Validation And Calibration Workload Model Obtain Model Parameters Develop a Performance Model Forecast Workload Evolution 9

10 Cost Model Development Cost Model Cost Prediction Understanding the Environment Workload Characterization Wkl Model Validation Workload Forecasting P&A Model Development P&A Model Validation P&A Prediction Workload Model Performance Avail. Model Configuration Plan P&A = performance and availability Cost, Performance & Availability Personnel Plan 10

11 Capacity Planning from the production viewpoint Resource oriented approach: 1. Planning 1. what? why? when? 2. Deployment 1. install, configure and manage 3. Measurement 1. generate resource usage graphs Virgili o

12 Planning 1. What is the current capacity of server class X? Definition of what is acceptable? E.g.: X can do at least 1500 qps (peak) with acceptable average response time (SLA) experimental process based on production tests to set the limits of server class X (ceiling) 95% of MySQL should take less than X milliseconds 2. Why is server X important? Bottleneck? 3. When should capacity be upgraded? Procurement time

13 Model based capacity planning process Speed, around the clock availability, and security are the most common indicators of quality of service on the Internet. Management faces a twofold challenge meet customer expectations in terms of quality of service. To control infrastructure costs to stay competitive. Holistic approach integrating different areas: sales, technology, development and production model based methodology

14 Workload Characterization Process of understanding, quantifying, modeling, and searching for invariants in the workload. A workload model is a representation of the actual workload. Model representativeness vs. complexity Workload models are useful for: Performance modeling and performance tuning Capacity planning Stress testing System design Understanding user behavior Design more efficient systems 14

15 Workload The workload of a system is the set of all inputs the system receives from its environment during any given period of time. W O R K L O A D HTTP requests 15

16 Workload Basic Workload Concepts submitted load: Computer computer type of requests arrival process Computer Computer load levels Type of requests HTTP requests, e business functions, e mail messages, database transactions, SOAP messages, OSN requests Arrival process: Inter arrival time distribution for each type of request Load levels: Average inter arrival time for each type of request 16

17 Exampleof BasicWorkloadConcepts A Web server receives three types of requests: HTTP requests for static HTML pages HTTP requests for images HTTP requests for the execution of applications (e.g., search, login) The arrival process for all types of requests is Poisson The average arrival rates are: 12 HTTP requests/sec for static HTML pages 20 HTTP requests/sec for image files 50 request/sec for the execution of applications 17

18 Workload Characterization Process of understanding, quantifying, modeling, and searching for invariants in the workload. A workload model is a representation of the actual workload. Model representativeness vs. complexity Workload models areuseful for: Capacity planning System selection System design Performance tuning Understanding user behavior Designing more efficient systems 18

19 Representativeness of a Workload Model Real Workload Workload Model System System Performance Measures P real Performance Measures P model 19

20 Workload Description Business Description User Functional Description Software Resource-orientedoriented Description Hardware 20

21 Workload ddescription Business characterization: a user oriented description that describes the load in terms such as number of employees, invoices per customer, etc. Functional characterization: describes programs, commands and requests that make up the workload Resource oriented characterization: describes the consumption of system resources by the workload, such as processor time, disk operations, memory, etc. 21

22 A Web Server Example Request No. CPU time (sec) I/O time (sec) Elapsed time (sec) Average Question: Is the pair (CPU time= sec, IO time = sec) a good model for this workload? 22

23 A Refinement in the Workload Model The average response time of 0.55 sec does not reflect the behavior of the actual server. Due to the heterogeneity of its components, it is difficult to view the workload as a single collection of requests. Three workload classes small documents medium documents large documents 23

24 Execution of HTTP Requests (sec) Request No. CPU time (sec) I/O time (sec) Elapsed time (sec) 1small medium medium small medium medium large medium small medium

25 Three Class Characterization ti Type CPU time (sec) I/O time (sec) No of omponents Small Docs Medium Docs Large Docs Total

26 Basic Steps in Workload Characterization Choice of an analysis standpoint Identification of the basic component Selection of the characterizing parameters Data collection Partitioning the workload Cl Calculating l the class parameters 26

27 Parameter Selection Selected parameters have to be: Useful: e.g., to be used in performance modeling Easy to obtain and measure Complete: provide an overall understanding and quantification of the workload Cover all relevant levels of the system: user level, functional level, protocol level, resource level. Examples of parameters (all related to a given measurement interval) Number of arriving HTTP requests Number of invocations of the Search function Size of the files retrieved from the Web server Number of each type of object (e.g., HTML, jpeg, pdf,.ps) retrieved from the Web server Number of SSL connections opened at the e commerce site Numberof e mailmessages messages received bythe mailserver 27

28 Data Collection This step assigns values to each component of the model. Identify the time windows that define the measurement sessions. Monitor and measure the system activities during the defined ed time windows. From the collected data, assign values to each characterizing parameters of every component of the workload. 28

29 Partitioning i the workload Motivation: real workloads can be viewed as a collection of heterogeneous components. Partitioning techniques divide the workload into a series of classes such that their populations are composed of relatively homogeneous components. What attributes can be used for partitioning a workload into classes of similar components? 29

30 Partitioning the Workload Resource usage Applications Objects Geographical orientation Functional Organizational units Mode 30

31 Workload Partitioning: ii i Resource Usage Transaction Classes Frequency Maximum CPU time (msec) Maximum I/O time (msec) Trivial 40% Light 30% Medium 20% Heavy 10%

32 Workload Partitioning: Internet Applications Application Classes KB Transmitted WWW 4,216 ftp 378 telnet 97 Mbone 595 Others 63 32

33 Workload Partitioning: ii i Document Types Document Class Percentage of Access (%) HTML (html file types) 30 Images (e.g., gif or jpeg) 40 Sound (e.g., au or wav) Video (e.g., mpeg, avi or mov) 7.3 Dynamic (e.g., cgi or perl) 12.0 Formatted (e.g., ps, dvi or doc) 5.4 Others

34 Workload Partitioning: Geographical Orientation Classes Percentage of Total Requests East Coast 32 West Coast 38 Midwest 20 Others 10 34

35 Calculating acua the workload oadcasspa class parametersa e How should one calculate the parameter values that represent a class of components? Averaging: when a class consists of homogeneous components concerning service demands, an average of the parameter values of all components may be used. Clustering of workloads is a process in which a large number of components are grouped into clusters of similar components. 35

36 Cl Calculating lti Class Parameters Heterogeneous Workload: Use clustering analysis to determine groups of similar workloads. Use averaging within each group. Clustering analysis algorithms: minimal spanning tree and k means. Canuse Weka toolsfor clustering: 36

37 Parameter Transformation To prevent extreme values of parameters from distorting i distribution ib i use linear transformation: Dt = (measured D minimum{di}) m{di}) / (maximum{di} minimum{di}) 37

38 Example of Linear Transformation File Size (KB) Transformed

39 Workload Sample for k means Clustering Example Document Size (KB) No. Accesses

40 Logarithmic transformation of parameters Document Size (KB) No. Accesses

41 3 C3 C1 2.5 C6 C5 C4 2 Numbe er Accesses s 1.5 C7 C Size (KB) 41

42 k-means example: initial allocation 3 C3 C1 2.5 C6 C5 C4 2 Numbe er Accesses s 1.5 C7 C Size (KB) 42

43 k-means example: initial clusters 3 Ca C3 C C6 C5 C4 Cb Numbe er Accesses s 1.5 C7 C2 Cc Size (KB) 43

44 k-means example: C1 joins Ca 3 Ca C3 C C6 C5 C4 Cb Numbe er Accesses s 1.5 C7 C2 Cc Size (KB) 44

45 k-means example: C1 joins Ca 3 Ca C3 C C6 C5 C4 Cb Numbe er Accesses s 1.5 C7 C2 Cc Size (KB) 45

46 k-means example: C5 joins Ca 3 Ca C3 C C6 C5 C4 Cb Numbe er Accesses s 1.5 C7 C2 Cc Size (KB) 46

47 k-means example: C6 joins Ca 3 Ca C3 C C6 C5 C4 Cb Numbe er Accesses s 1.5 C7 C2 Cc Size (KB) 47

48 k-means example: C7 joins Cb 3 Ca C3 C C6 C5 C4 Cb Numbe er Accesses s 1.5 C7 C2 Cc Size (KB) 48

49 k-means example: C2 is the only point in Cc 3 Ca C3 C C6 C5 C4 Cb Numbe er Accesses s 1.5 C7 C2 Cc Size (KB) 2007 V A F Almeida and D A 49

50 k-means example: final clusters 3 Ca C3 C C6 C5 C4 Cb Numbe er Accesses s 1.5 C7 C2 Cc Size (KB) 2007 V A F Almeida and D A 50

51 Result of Workload dcharacterization ti Type Class Size (KB) No. Accesses No. Components Small C Medium C Large C

52 Clustering Analysis The Euclidean distance between points is w i = ( Di 1,..., DiM w = ( D,..., D j ) ( j1 jm ) d ij = M n=1 ( D in D ) 2 jn 52

53 k means Clustering 1. Set the number of clusters to k. 2. Choose k starting points as initial estimates of the k clusters. 3. Examine each point and allocate it to the closest centroid. Recompute the centroid s coordinates (avg. of all cluster s points coordinates). 4. Repeat step 3 until no points change allocation or until a max number of passes is performed. 53

54 Advanced Concepts in Web Workload Characterization Hierarchical characterization Multiple time scale analysis Popularity Analysis Power laws Fractal workload characterization 54

55 Hierarchical Analysis Business Level User Level Session Layer Application Level Protocol Level Function Layer Request Layer Resource Level 55

56 Multiple Time Scale Analysis As the time scales change from coarse to finer time scales, burstiness and different patterns reveal themselves. 56

57 Popularity Analysis Popularity analysis ranks objects in the order of number of accesses. The one with the largest number of accesses (most popular) is ranked number 1, the second number 2, etc. Question: what is therelationship between the rank and the frequency of access of each object? 57

58 Popularity Analysis: Zipf s Law Example 700 Nu umber of Acesses (P) Zipf s Law: P = 580 r -1 f ~ 1 r Document Rank (r) 58