Index. ADEPT (tool for modelling proposed systerns),

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1 Index A, see Arrivals Abstraction in modelling, 20-22, 217 Accumulated time in system ( w), 42 Accuracy of models, 14, 16, see also Separable models, robustness Active customer (memory constrained system), 185 ADEPT (tool for modelling proposed systerns), Aggregate (hierarchical modelling), , see also Flow equivalent service center Aggregate performance measures for multiple class models, Algorithms asymptotic bounds, 79 balanced system bounds, 93 disk I/O non-rps, 228 RPS, 232 multipathing, 239 hierarchical decomposition, 161 mean value analysis single class exact, 115 single class approximate, 118 multiple class exact, 141 multiple class approximate, 143 multiple class mixed exact, 146 memory constraints single class, 188 multiple class, independent, multiple class, shared, open models single class, 111 multiple class, 137 priority scheduling, 259 swapping dedicated device, 199 shared device, 200 variable overhead, 307 Amdahl (case studies) see IBM IMS capacity, using modelling software, using RMF, Appropriateness of queueing networks, 14 Approximate solution techniques for separable models, see Mean value analysis, approximate for non-separable models, see Disk I/O; Memory; Processors Approximation transformations (modelling software), 356 Arrival instant queue length in separable models, 112, 114, Arrival instant theorem for separable models, 114, 140 and balanced system bounds, 126 Arrival rate (A), 41 Arrivals c/f), 40 Assumptions, testing of, 33-36, see also Validation Assumptions of separable models, see Separable models, assumptions Asymptotic bounds, 72-87, see also Bounds on performance Algorithm 5.1, 79 B, see Busy time Balanced system bounds, 86-93, see also Bounds on performance Algorithm 5.2, 93 Balanced systems, utilizations in, 90 Baseline model, see Existing systems Basic observable quantities, Batch workload, 58, see also Closed models Benchmarking, 3, Block size (I/O), Books on performance, Bottleneck, effect of removing (example using asymptotic bounds), secondary, Bounds on performance (Chapter 5), advantages, 70-71, 94 asymptotic bounds, Algorithm 5.1, 79 examples and case studies,

2 410 Index Bounds on performance (conf d.) balanced system bounds, Algorithm 5.2, 93 and arrival instant theorem, 126 optimistic and pessimistic bounds, 71 Busy 41 C. see Completions; Customer classes Cached I/O devices, CAD/CAM system (performance projection of proposed system), Capacity planning, see Evolving systems Capture ratio (measurement), 285 Carrier sense multiple access with collision detection (CSMA-CD), 340 Case studies and examples Amdahl IMS capacity, using modelling software, using RMF, asymptotic bounds, bottleneck removal (asymptotic bounds), CAD/CAM system (proposed), capacity planning Amdahl 470, IMS, PDP-IO, communication network (proposed), concurrency control (database), CPU replacement, 27-29, CTSS, Cyber 173, database concurrency control, disk I/O non-rps, RPS, disk load balancing asymptotic bounds, Univac 1100, evolving systems, FCFS scheduling class-dependent service times, highly variable service times, FESC for memory constrained system, forced flow law, global balance, hierarchical modelling, hierarchical workload characterization (instructional computing acquisition, Prime, VAX), 30-34, Case studies and examples (cont d.) IBM early virtual memory system, processing complex (workload balancing), , , 8130, 8140 (insurance company), 35-37, IMS (Amdahl 470, capacity planning), instructional computing acquisition (Prime, VAX), 30-34, insurance company (IBM 3790, 8130, 81401, 35-37, Little s law and forced flow law, and memory constrained systems, 49-50, at various levels, loosely-coupled multiprocessor (Cyber 173), memory, memory constrained system FESC, Little s law, modelling software, use of (Amdahl 4701, modification analysis, asymptotic bounds, IBM processing complex, IBM 360, multiple class models, multiple class solution techniques, , 142, , 147 multiprogramming level variability, MVS Amdahl 470 (using RMF), IBM processing complex, SRM, non-rps disks, paging, PDP-10, Prime (instructional computing acquisition), priority scheduling, proposed systems, , RMF usage (Amdahl470), RPS disks, single class models, of heterogeneous workloads, single class solution techniques, 112-l 3, , 119 single service center, 5-8

3 Index 411 Case studies and examples (co&d.) SNA flow control, software resources, swapping moving from drum to disk (Univac 1100), to a shared device, tightly-coupled multiprocessor (Univac 11001, Univac 1100, upgrade from single to dual processor (Univac 11001, variation in multiprogramming level, VAX instructional computing acquisition, 30-34, memory modelling, virtual memory, 202-S workload balancing (IBM processing complex), Center description, 59 for models of existing systems, 283 from RMF, Central subsystem, 45, 58, Channel Activity Report (RMF), Channel contention (I/O) non-rps, RPS, Channel holding time (I/O), 227 Classes, see Customer classes; Customer description Closed classes in mixed models, Closed models, 58, 62, , Communication networks, see Computer communication networks Complementary network (hierarchical modelling), Completions (Cl, 40, 47 Components of modelling software, Composite queueing (FESCd, 157 Computational algorithms, see Algorithms Computer communication networks (case studies) projecting performance of proposed system, SNA, Concurrency control (database) (exampie), Conducting a modelling study (Chapter 21, list of specific points, Conferences on performance, Contention time (I/O), 224 Controller (I/O), Core computational routine for separable models, see Mean value analysis evolution, in modelling software, 354 CPU, see Processors CPU Activity Report (RMF), CPU replacement, parameterization to reflect, 301 case study, 27-29, CRYSTAL (tool for modelling proposed systems), CSMA-CD, 340 CTSS (case study), Customer classes (cl, 62 Customer description, for models of existing systems, from RMF, Cyber 173 (case study), D, see Service demand DASD, see Disk I/O Database modelling approaches, high-level front ends, Decomposition, see Flow equivalent service center; Hierarchical modelling Definition of models, 9-12 Delay service center, 59 Derived observable quantities, Device homogeneity, 120, 147 Direct Access Device Activity Report (RMF), 379, 381 Disk I/O (Chapter 101, , see also Algorithms, disk I/O alternate modelling approaches, inferring parameter values from measurement data, , 249 load balancing asymptotic bounds example, Univac 1100 case study, non-rps example, , RPS example, service time, components of, 224 Domains, in MVS, Dynamic reconnection (I/O), 237

4 412 Index Effective disk service time, 224 End effects in measurement, 43-44, 277 Evaluation of models, closed and mixed, see Mean value analysis open, see Open models Event monitor, 276 Evolution of core computational algorithms for separable models, Evolving systems (Chapter 131, , see also Parameterization case studies and examples simple example, using asymptotic bounds, IBM 360, Univac 1100, using modelling software, primary and secondary effects, Examples, see Case studies and examples Execution graphs (for workload characterization), Existing systems (Chapter 121, , see also Parameterization RMF example, Experimentation, 3 External arrival homogeneity, 120, 149 FCFS, see First-come-first-served FESC, see Flow equivalent service center File placement techniques for modelling, Univac 1100 case study, First-come-first-served scheduling, 128 class-dependent service times, highly variable service times, Flow balance assumption, Flow control (SNA case study), Flow equivalent service center (FESC), , see also Load dependent service center and separability, , and transaction workloads, 157 applications memory constrained system, SNA flow control, tightly-coupled multiprocessor, cost of evaluating a model containing, 157, 159, desired characteristics, evaluating high-level models, obtaining parameters, Flow equivalence and hierarchical modelling (Chapter 81, , see also Flow equivalent service center; Hierarchical modelling; Load dependent service center Forced flow law, Front ends for modelling software, Fundamental laws (Chapter 3), table of. 53 Global balance, cost, details, to evaluate model of SNA, to model priority scheduling, Goal-oriented scheduling, 262 Hardware modifications, see Evolving systems parameterization to reflect, Heads of string (I/O), Hierarchical modelling, , see also Flow equivalent service center; Hybrid modelling Algorithm 8.1, 161 applications memory constrained systems, SNA flow control, detailed example, Hierarchical workload characterization, Homogeneity assumptions, 120, and memory modelling, , Hybrid modelling, 16, of MVS SRM, IBM (case studies), see Amdahl early virtual memory system, processing complex (workload balancing), , , 8130, 8140 (insurance company), 35-37, IMS (Amdahl 470 capacity planning case study), Inflation of service demands, see Load concealment

5 Index 413 Inputs and outputs of models (Chapter 4), 4-14, 49, 57-68, , see also Parameterization Insight, sources of, Installation Performance Specification (IPS) (MVS), Instructional computing acquisition (case study, Prime, VAX), Insurance company case study (IBM 3790, 8130, 81401, 35-37, l/o subsystem, see Disk l/o l/o subsystem modifications, parameterization to reflect, IPS (IBM MVS Installation Performance Specification), Iterative solution techniques for separable models, see Mean value analysis, approximate for non-separable models, see Disk l/o; Memory; Processors Journals on performance, K. see Number of centers Last-come-first-served (LCFS) scheduling, 129 Latency time (l/o), 224 LCFS, 129 Little s law, 40, and flow balance assumption, and forced flow law, and mean value analysis, 112, 139 and memory constrained systems, 49-50, application at various levels, used to derive asymptotic bounds on performance, Load concealment in evaluating mixed models, 145 in modelling shared disks, 243 Load dependent service center (Chapter 20), , 149, , see also Flow equivalent service center evaluating models containing, , MVA algorithms for, use in modelling tightly-coupled multiprocessor, , 310-l 1 Load independent service center, , 149, see also Queueing service center Local area networks, Logical l/o operation, Loosely-coupled multiprocessor, 237, , 253 Cyber 173 case study, M, see Memory constraint MAP (queueing network modelling software package), Mean value analysis (MVA), , , see also Algorithms, mean value analysis; Evaluation of models; Open models approximate, , calculating outputs from, 116, 142 exact, , implementations, key equations, 112, 139 load dependent centers, mixed models, population precedence, , residence time equation, 112, 139 Measurement, 21 choice of interval, event recording, 276 sampling, 276 to parameterize FESCs, 158 useful data items, using RMF, Measurement data, inadequacy of, Measurement interval duration (n, 40 Memory (Chapter 91, , see also Memory constraint; Multiprogramming level; Paging admission policies, hybrid modelling of, assumptions made in separable models, case studies early IBM virtual memory system, VAX/VMS, robustness of separable models, 180 Memory constraint CM), , see also Algorithms, memory constraints Little s law used to evaluate, 49-50, Memory expansion, parameterization to reflect, , Method of stages (global balance),

6 414 Index Mixed models, 62, Algorithm 7.4, 146 Modelling, alternatives to, 4 Modelling cycle, Modelling methodology, see Conducting a modelling study Modification analysis, 12-13, see also Evolving systems Multipathing I/O, Algorithm 10.3, 239 Multiple class models (Chapter 7), , see also Algorithms advantages, 127 case studies illustrating use, contrast with single class models, disadvantages, examples illustrating evaluation, , 142, , 147 inputs and outputs, scheduling disciplines in, Multiprocessor, see Loosely-coupled multiprocessor; Tightly-coupled multiprocessor Multiprogramming level non-integer, in mean value analysis, 144 variability (case study), MVS (case studies), see SRM Amdahl 470 (using modelling software), Amdahl470 (using RMF), IBM processing complex (workload balancing), N, see Number in system Non-integer multiprogramming level in mean value analysis, 144 Non-RPS disks, , see also Disk I/O Algorithm 10.1, 228 Non-separable models, see Disk I/O; Memory; Processors approaches to evaluating, 162, need for, Notation, table of, 53 Number in system (N as input, Q as output), 60-61, -see also Little s law Number of centers (K), 58, 63 Objectives of modelling study, importance of understanding, Observation interval, One step behavior, 120, 147 Open classes in mixed models, Open models, 58, 62, see also Algorithms, open models evaluation, , Operating systems modelling detailed algorithms, modelling upgrades, Operational analysis, xii-xiii, 123, 150 Optimistic bounds on performance, 71 Outputs of models, see Inputs and outputs of models Overhead Algorithm 13.1, 307 attribution of, using RMF, 386, modelling changes in, of paging activity, Packages, queueing network modelling, see Queueing network modelling software Paging, Parameterization, 12-13, see also Evolving Systems; Existing systems; Inputs and outputs of models; Proposed systems RMF example, software assistance, types and sources of information, Path (I/O), , 231 Path busy, estimating probability of (I/O), Path elements, estimating utilizations of (I/O), 238, 240 Path selection (I/O), 237 PDP-10 (case study), Performance database, 279 Performance group (MVS), 377 Performance measures, 22, see also Inputs and outputs of models from RMF, 388, Performance-oriented design, see Proposed systems Performance period (MVS), 377 Performance projection, see Evolving systems; Proposed systems simple example, Pessimistic bounds on performance, 71 Physical I/O operations, Population, see Number in system; Little s law Population precedence (MVA), ,

7 Index 415 Primary and secondary effects of modifications, 12-13, 20-21, 248, Prime (case study, instructional computing acquisition), Priority scheduling, Algorithm 11.1, an approach using global balance, Processing capacity, see Bounds on performance of open models, 109, Processor sharing scheduling, 129 Processors (Chapter 111, , see also CPU replacement; Multiprocessor; Scheduling discipline Program listings single class exact MVA, multiple class exact MVA, Projecting performance, see Evolving systems; Proposed systems Projection phase (modelling cycle), Proposed systems (Chapter 141, case studies, , Q, see Number in system; Queue length Queue length (Q), 60-61, see a/so Little s law at channel (I/O), 227 in open models, 111, 136 Queue length distribution, 62 for load dependent centers, Queueing network modelling software (Chapter 161, approximation transformations, 356 core computational routine, 354 example use, high-level front ends, user interface, Queueing service center, 59 Queueing theory, 16 R, see Residence time; Response time Ready customer (memory constrained system), 185 Reconnect (I/O), 230 algorithms, 237 failure, estimating probability of, 241 Relationships among observed quantities, table of, 54 Remote terminal emulation, 27, see also Benchmarking Residence time (RI, 42, 61, see also Mean value analysis; Open models; Response time Residence time equation (MVA), 112, 139, see also Mean value analysis for load dependent centers, modifications for biased processor sharing, for FCFS with class-dependent service times, for FCFS with highly variable service times, 265 for priority scheduling, 258 Residual service time, 265 Resource Measurement Facility (RMF) (IBM), use of, Response time (RI, 42, 61, see also Mean value analysis; Open models; Residence time Response time law, 46 to estimate think times, 282 to evaluate memory constrained system, 207 Retry (I/O), 230 RMF (IBM Resource Measurement Facility), use of, Rotation time (I/O), 230 Rotational position sensing (RPS), , see also Disk I/O Algorithm 10.2, 232 Routing homogeneity, 120, 147 RPS, see Rotational position sensing S, see Service time per visit Sampling monitor, 276 Saturated system, 72, 109, Scheduling discipline, see First-come-firstserved; Last-come-first-served; Priority; Processors; Separable models, scheduling disciplines in Secondary bottleneck, Secondary effects of modifications, 12-13, , Seek time (I/O), 224 Sensitivity analysis, Separable models, 15 advantages, arrival instant theorem, 114 assumptions, 20-22, 62, 147, 149 inputs and outputs, limitations, 64-66, 121 robustness, 121, 180, , 248, 261, 264

8 416 Index Separable models (cont d.) scheduling disciplines in, 59, 62, , theoretical foundations, , 147, 149 Service center, 4-8, see also Center description Service center flow balance, 120, 147 Service center types, 59 Service demand CD), 48-49, estimation of for models of existing systems, CPU, l/o, difficulties, 284 using RMF, 386, inflation. see Load concealment Service objective (MVS), 377 Service rate Cp) (FESC), , see also Flow equivalent service center; Load dependent service center Service rate of tightly-coupled multiprocessor,/ Service time per visit (S), 41 Service time homogeneity, 120, 149 Shadow CPU technique (priority scheduling), Shared disks, Simulation, in hybrid modelling, to parameterize FESCs, 158 Simultaneous resource possession, 185 Single class models (Chapter 61, , see also Algorithms advantages, 98, 122, case studies illustrating use, examples illustrating evaluation, , , 119 inaccuracy when workload is heterogeneous, inputs and outputs, limitations, SingIe server queue, 4-8 SNA (IBM System Network Architecture), Software modifications, parameterization to reflect, Software resources, Software specifications, refinement of (modelling proposed systems), Solution of models, see Evaluation of models SRM (IBM MVS System Resources Manager) description, hybrid model of, State, see Global balance, details of States of a customer (memory constrained system), 185 Static reconnection (I/O), 237 Stochastic analysis, xii-xiii, 123, 150 Swapping, moving from drum to disk (Univac 1100 case study), to a dedicated device, Algorithm 9.4, 199 to a shared device, Algorithm 9.5, 200 System Network Architecture (SNA) (IBM), System Resources Manager (IBM MVS) see SRM T, see Measurement interval duration Tape l/o, , 386, 387, 391 Terminal workload, 58 Think time (21, 46 estimating, 282, 384 Thrashing, Throughput (xl, 41, 61 bounds on, 72-73, in memory constrained systems, in open models, 109, versus multiprogramming level, 18 l-84 Tightly-coupled multiprocessor, 253, upgrading from single to dual processor (Univac 1100 case study), 310-I 1 Tolerances, in validation, 292 Transaction (database), Transaction workload, 58, see also Open models calculating utilization, Transfer time (l/o), 224 TSO (IBM Time Sharing Option) (case studies), 24-27, ~360-69, U, see Utilization Unattributed busy time, apportioning, Univac 1100 case studies, User interface for modelling software,

9 , Index 417 Utilization (u), 41, 61 Utilization law, 41-42, 45 and flow balance assumption, V, see Visit count Validation, 22-27, Validation phase (modelling cycle), Variability in multiprogramming level, Variance in service time, VAX (case studies) instructional computing acquisition, 30-34, memory modelling, Verification phase (modelling cycle), Virtual memory, Visit count ( V), W, see Accumulated time in system Waiting customer (memory constrained system), 185 Workload Activity Report (RMF), , 382 Workload balancing (IBM processing complex case study), Workload characterization hierarchical, in modelling proposed systems, using RMF, Workload components identification, modifications, parameterization to reflect, Workload intensity, 57-58, 62, 63, determining for existing systems, from RMF, Workload measures, 22 Workload modifications parameterization to reflect, PDP-10 case study, Workload representation in single class models, Workload types, selecting for models of existing systems, from RMF, x, see Throughput 2, see Think time h. see Arrival rate p, see Service rate

Chapter 9. Memory Introduction

Chapter 9. Memory Introduction Chapter 9 Memory 9.1. Introduction Memory and its management affect the performance of computer systems in &o major ways. First, almost every system h.as a memory constraint: a limit on the number of threads