1 / 105

Capacity Planning and Workload Forecasting

Capacity Planning and Workload Forecasting. Definitions (1). Capacity – the (maximum) amount that a device is able to tolerate Work Throughput Storage Capacity Plan – a strategy to effectively utilize the capacity of a device

giorgio-lunney
Download Presentation

Capacity Planning and Workload Forecasting

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Capacity Planning and Workload Forecasting

  2. Definitions (1) • Capacity – the (maximum) amount that a device is able to tolerate • Work • Throughput • Storage • Capacity Plan – a strategy to effectively utilize the capacity of a device • Capacity Planning – the determination of the predicted future time when system saturation will occur • Saturation – that point in time when a computer system is asked to do more work than it is capable of • Device Saturation – that point in time when a particular device within a computer system is asked to do more work than it is capable of • Work – the transfer of one (1) byte (of data) between a processor and a storage device • Workload – the amount of work that is being performed by a device or system • Workload Management – the strategy for effectively utilizing the performance of work in a computer system Capacity Planning and Workload Management

  3. Definitions (2) • Background – processing that occurs “behind the scenes” and (normally) does not involve direct (human) user interaction • Foreground – processing that involves direct interaction between (human) users and computers • Planning Horizon – a period of time for which a strategy and associated objectives are established • Long range • Medium range • Short range • Initial Load – the amount of resources that are required at the time an information solution is deployed • Growth Factor – the rate of change (Δr/Δt) in the amount of resource used over a planning horizon Capacity Planning and Workload Management

  4. Definitions (3) • Performance Constraints – business rules that are imposed upon the responsiveness of an information solution • Simulation – a(n) (parameter- driven,) algorithmic approach to modeling the (expected) performance of an information solution • Priority Level – the level that an executing program is serviced by the computing environment Capacity Planning and Workload Management

  5. Capacity Planning Schematic Desired Service Levels Workload Evolution System Parameters Capacity Planning Cost-effective Alternatives Saturation Points Capacity Planning and Workload Management Source :Menasce, Almeida, & Dowdy, Capacity and Performance Modeling, ISBN 0-13-789546-1

  6. Networks – communications paths Processor – instructions executed Memory – primary storage Files – secondary storage Work Sort Storage Libraries Databases – structured permanent storage Topology – nodes in the network Security – information protection Software Executive Applications Foreground Background Information Technology Components to Consider Capacity Planning and Workload Management

  7. Critical Questions • What is the overall architecture of the current environment? • What is in place? • What is the this environment capable of? • What can it do? • What architecture is needed? • What will it cost? Capacity Planning and Workload Management

  8. Importance of Capacity Planning • User satisfaction • Image of the enterprise • Productivity considerations • Budgetary considerations • Risk • Control Capacity Planning and Workload Management

  9. Capacity Planning and Workload Management - Relationships Strategic Plan Technology Plan Capacity Plan Business Vision and Mission Tactical Plan Operational Plan Workload Management Capacity Planning and Workload Management

  10. λ λ λ λ user1 user1 user1 user1 λ λ λ λ user2 user2 user2 user2 Centralized versus Decentralized IS Facilities Batch Central Facility Centralized Decentralized Capacity Planning and Workload Management Source :Menasce, Almeida, & Dowdy, Capacity and Performance Modeling, ISBN 0-13-789546-1

  11. Capacity Planning Methodology • What is the current installed capacity? • What future services should be provided? • What are the planned quality goals? • What is the most cost effective configuration to meet the state goals? Capacity Planning and Workload Management

  12. Types of Environments • OLTP – Online Transaction Processing • Transactional activities • Traditional “business” transactions • OLAP – Online Analytical Processing • Algorithmic activities • Search and/or computation intensive • Centralized processing • Single node • Distributed processing • Multi-node Capacity Planning and Workload Management

  13. Capacity Planning Process IT Environment A Alternative A Alternative B Alternative C Capacity Planning Process Business Plans B C Product Lines Capacity Planning and Workload Management

  14. Model-based Capacity Planning Workload Model Corporate Plans Understand Environment Characterize Workload Validate and Calibrate Performance Model System Alternatives Predict Performance Forecast Workload Validated Model Capacity Planning and Workload Management

  15. Hardware One or more computers Share common resources Networks Interconnect different computers Operating System Interacts directly with hardware Provides services to other software Isolates software from hardware anomalies Support Software Application Programs Operational Developmental Application Application Development DBMS Transaction Command Support Software Building the Capacity Plan1. Understand the Current Environment Operating System Service Request Hardware Capacity Planning and Workload Management

  16. Building the Capacity Plan2. Understanding “capacity” (a) • Theoretical Capacity (of a computing system) – maximum rate at which such a system can perform work • Throughput (of a computing system) – the rate at which such a system services requests made of it • Effective Capacity (of a computing system) – the largest throughput at which response time remains acceptable • Responsiveness limits the amount of effective work processed by a computing system Capacity Planning and Workload Management

  17. Theoretical Capacity 1.0 0.8 0.6 0.4 0.2 Response Time ( R ) Acceptable Level of Response Effective Capacity 2 4 6 8 10 12 Throughput (tps) Building the Capacity Plan2. Understanding “capacity” (b) Capacity Planning and Workload Management

  18. Building the Capacity Plan3. Computing System Components NETWORK Storage Memory I/O Processor(s) Capacity Planning and Workload Management

  19. Building the Capacity Plan4. Computing System Metrics • Cycle time – the speed of a processor • Length  10-9 seconds • Rate  MHz or GHz • MIPS – millions of instructions/second = instructions/cycle * cycles /sec * 10-6 • Benchmarks – standard programs used to measure component performance • TPA – network performance • TPB – database system performance • TPC – OLTP programs Capacity Planning and Workload Management

  20. Building the Capacity Plan5. Estimating Magnetic Disk Performance • Consists of several elements • Seek time – time required to move the disk arm to the destination track • Rotational delay – time required to align the target coordinate with the read/write head • Transfer time – the time required to move the requested data bits • Controller time – the amount of time (overhead) used by the controller • Average Access Time = average seek + average rotational delay+ (block size/transfer rate) + controller time Capacity Planning and Workload Management

  21. Peak Valley Activity Volume Processing Time Period Building the Capacity Plan6. Time Windows (a) • Consideration of when work occurs • Peaks • Valleys Capacity Planning and Workload Management

  22. Building the Capacity Plan6. Time Windows (b) What is this graph telling ? Activity Volume     S M T W R F S S M T W R F S S M T W R F S S M T W R F S Week 1 Week 2 Week 3 Week 4 Capacity Planning and Workload Management

  23. Building the Capacity Plan6. Time Windows (c) • Granularity • Determining the smallest meaningful unit of time Capacity Planning and Workload Management

  24. Building the Capacity Plan6. Distribution Considerations • Every node in the network must be evaluated in a like manner Activity Volume Time Units Node Capacity Planning and Workload Management

  25. Building the Capacity Plan7. Service Levels • Quality of the computing services • Contractual • Cost versus benefit • Competition/business conditions • Mission criticality • Previous experience • Perception • Generally accepted measures • MTBF – mean time between failures • MTTR – mean time to repair • Availability =(interval length – downtime)*interval length-1 Capacity Planning and Workload Management

  26. Application Application Development DBMS Transaction Command Support Software Building the Capacity Plan8. Components • Everything that executes on a computer does work and contributes to total workload • When executing • Where executing • So, at any moment in time • The workload varies • Capacity plan must project Operating System Service Request Hardware Capacity Planning and Workload Management

  27. Building the Capacity Plan9. Component and calculation • Calculate on an application by application basis • Consider each of the supporting services used • Consider nodal implications • Aggregate and plot • Adjust to account for unanticipated situations • At any point, does the estimated aggregate exceed the root-mean-square of total capacity (0.707)? • What risks have been identified Capacity Planning and Workload Management

  28. The Workload Model • Representation of actual workload being studied • Two categories • Synthetic models • Combination of actual and specially developed components • Artificial models • Special purpose programs • Descriptive parameters Capacity Planning and Workload Management

  29. Synthetic Models • Experientially based • Elements/components • Kernels – key programs from existing workload • Synthetic Programs – special purpose programs intended to develop critical measurands Capacity Planning and Workload Management

  30. Artificial Model • Executable • Suite of computer programs • Instruction mixes – • Hardware demonstration programs • Test computational and I/O components • Kernels – • Code elements from computationally-intensive programs • Measure processor without I/O components • Synthetic Programs • Devised code – places demand on different computing system resources • Do not resemble real workload • Different from benchmarks • Non-executable • Parametric representation of workload resource components Capacity Planning and Workload Management

  31. Artificial Model Parameters • Typical parameters • Program interarrival time • Service demand • Program size • Execution mix • Classes of programs • Corresponding levels of multiprogramming • Processor factors • System load • Device capability • File reference location • Derived parameters • Frequency distribution of requests • Request interarrival time distribution • File referencing behavior • Size of read and write requests Capacity Planning and Workload Management

  32. Using the Artificial Model • Capture key aspects of a computer system • Relate key aspects • Mathematical formulae • Computational algorithms • Require • Workload intensity • Service demand • Limited representation requires simplifying assumptions Capacity Planning and Workload Management

  33. Workload Characterization Methodology • Identification of basic components • Choice of Operating Parameters • Data collection • Partitioning the workload • Resource usage • Applications • Geographical orientation • Functional classes • Organizational units • Class type • Calculating class parameters • Averaging • Clustering • Data analysis • Scaling techniques • Clustering algorithms Capacity Planning and Workload Management

  34. Identifying Basic Component of Work • Job • Transaction • Command • Request • Result of this step  • What does the workload under study consist of? Capacity Planning and Workload Management

  35. Choice of Characterizing Parameters • Component representation • Workload intensity • Arrival rate • Number of terminals • Think time • Number of simultaneously executing programs • Service demands • Specified by k-tuple (Di1, Di2, … , Dij) • K  number of resources considered • Dij  service demand • i basic component identifier • j  resource identifier Capacity Planning and Workload Management

  36. Data Collection • Assigns values to parameters of each model component • Generates same number of characterizing tuples as number of workload components • Included tasks • Identify time windows that define measurement sessions • Monitor and measure system activities during defined sessions • Assign values to all characterizing parameters Capacity Planning and Workload Management

  37. Partitioning the Workload • Actual workloads – collection of heterogeneous component • Single class representation lack accuracy • Goal – translate heterogeneity into homogeneity • Divide workload into classes whose populations are homogeneous components Capacity Planning and Workload Management

  38. Partitioning the Workload • Resource usage – classify components based on common use of critical resources • Applications – classify components based on class of applications • Subjectization of the enterprise • Geographical orientation – classify components based on where they execute • Functional classes – classify components based on the computational functions being serviced • Organizational units – classify components based on the organizational structure of an enterprise • Consider organizational culture • Type – classify components based on processing mode and associated descriptive parameters • Interactive (terminal) • Transaction • Batch (background) Capacity Planning and Workload Management

  39. Calculating Class Parameters - Averaging • Homogeneous components • Represent a specific value by the average for the class • Consider the tuple (Di1, Di2, … , Dik) whose arithmetic mean for each parameter is expressed asDj = p-1Σ Dij j = 1,2, … , k ki= 1 Capacity Planning and Workload Management

  40. Each clear bullet represents a transaction execution Resource oriented Black bullets represent resultant for a class (Ci) Statistical description Calculating Class Parameters - Clustering C1 CPU TIME C2 C3 I/O TIME Capacity Planning and Workload Management

  41. Set of activities Sampling Parameter transformation Outlier removal Distribution options Histogram Logarithmic Sampling Specific Random Parameter transform Representation in a standard model Outlier removal Elimination of extremes Calculating Class Parameters – Data Analysis Capacity Planning and Workload Management

  42. μ -2σ -1σ 1σ 2σ X CPU TIME I/O TIME Calculating Class Parameters – Data Analysis and Distance Distance C1 CPU TIME C2 C3 I/O TIME Data Analysis Capacity Planning and Workload Management

  43. Calculating Class Parameters – Scaling • Used to avoid problems arising from parameters with very different values and ranges • Use a z-distribution for each parameter z = measured value – mean value standard deviationwhere the mean and standard deviation are standard statistical measures Capacity Planning and Workload Management

  44. Mean and Standard Deviation Formulae • Meanx = Σ xi • Standard Deviationσ = (Σ(xi – x)2)/(n – 1))1/2 • Variance  σ2 ni = 1 n i = 1 Capacity Planning and Workload Management

  45. Minimal spanning tree Hierarchical algorithm that starts by considering each component of a workload to be a cluster Algorithm Set initial number of clusters = number of components Determine parameter values of each cluster Calculate the inter-cluster distance matrix Determine the minimum non-zero element Decrement number of clusters Repeat steps 2 – 5 as necessary K-means algorithm Non-hierarchical clustering technique based on finding k points in the workload. Algorithm Set the number of clusters to k Chose k starting points as initial estimates Examine each point in the workload; allocate it to nearest cluster; recalculate cluster’s position Repeat step 3 as needed until no point changes its cluster assignment or a maximum number of passes is performed Calculating Class Parameters – Clustering Algorithms Identifying natural groups of components based on resource requirements Capacity Planning and Workload Management

  46. Workload Forecasting • Computer workloads change as the “business” changes • New applications • Changes to existing applications • Increase in processing volumes by existing applications • Environmental enhancements • Difficulties • Obtaining reliable information • Terminology and communications with users/customers • Needs of the “under construction” applications • Latent demand – deferred processing requests Capacity Planning and Workload Management

  47. The Role of Business • Technical requirements = f (business requirements) • Computing capacity = f (business base, strategic planning) • Critical applications account for majority of computer resource utilization • What are they? • Use to identify • KVI – Key Value Indicator • NBU – Natural Business Unit • FBU – Forecasting Business Unit Capacity Planning and Workload Management

  48. Workload Forecasting Methodology • Select applications to be forecasted • Identify business units associated with applications whose growth will be forecasted • Summarize the statistics • Collect statistics concerning business units being chosen • Translate business units into computer demands • Forecast future resource demand as a function of the business units. Capacity Planning and Workload Management

  49. Forecasting Techniques • Moving average • Exponential smoothing • Regression methods Capacity Planning and Workload Management

  50. Moving Average • Simple forecasting technique • Allows value for next period to equal average of a set of previous observations • High reliability for (near) steady-state situations • Only one forecast value can be generated at a time • Forecast value calculationft+1 = (yt + yt-1 + . . . + yt-n+1 )/n • Error calculation – mean squared error (MSE) MSE = Σ (yt – ft )/n n t = 1 Capacity Planning and Workload Management

More Related