Software Performance Modeling of a Frame Relay Access Device ADRIAN - - PowerPoint PPT Presentation

software performance modeling of a frame relay access
SMART_READER_LITE
LIVE PREVIEW

Software Performance Modeling of a Frame Relay Access Device ADRIAN - - PowerPoint PPT Presentation

Software Performance Modeling of a Frame Relay Access Device ADRIAN CONWAY GTE Internetworking (work carried out at Racal-Datacom ) First International Workshop on Software and Performance Santa Fe, New Mexico Oct. 1998 Frame Relay Network


slide-1
SLIDE 1

Software Performance Modeling of a Frame Relay Access Device

ADRIAN CONWAY GTE Internetworking (work carried out at Racal-Datacom)

First International Workshop on Software and Performance Santa Fe, New Mexico

  • Oct. 1998
slide-2
SLIDE 2

Frame Relay Network Access Device

frame relay network

FRAD FRAD FRAD

ethernet token ring DTE DTE

  • Frame Relay Network

– virtual-circuit service – connects remote sites – economical compared to a private leased-line network

  • FRAD

– interconnects LANs and DTEs to the frame relay network – a multi-protocol multi-function device

slide-3
SLIDE 3

Racal’s FastFrame 600 FRAD Protocol Architecture

token ring driver Ethernet driver PPP mux Frame Relay mux sync/asynch drivers sync drivers BiSync drivers ALC protocol ALC UDP mux SDLC LLC SNA IP protocols WAN user user user user user token ring Ethernet

slide-4
SLIDE 4

FastFrame 600 FRAD Software

  • Protocol Modules

– standardized

  • acquired from various vendors

– proprietary

  • different software development groups
  • Protocol Integration with UNIX STREAMS Facilities

– kernel routines for layered protocol software – modular architecture

  • drivers, modules, multiplexors, queues

– simplifies development – reduces development time

slide-5
SLIDE 5

Need for Software Performance Modeling and Analysis

  • protocol modules developed by different groups
  • performance of integrated system is unknown a priori

– throughput, delay, burst handling

  • shortened time to market

– less time for performance measurement, re-design, tuning

  • performance ‘disasters’ at end of development cycle are costly
  • real-time performance is of increasing importance (e.g. voice/packet)
  • product requirements include performance
  • need UP-FRONT analysis at product specification phase

– architectural choices, verify design for performance requirements, expose potential flaws

  • analysis at testing stage

– tool to help in parameter optimization – evaluate ‘last-minute’ changes

slide-6
SLIDE 6

FRAD Performance Modeling and Analysis

  • We propose a software performance model for data-

networking products based on STREAMS

  • UNIX STREAMS maps naturally into a queueing model

– model focuses on data-transfer phase – exploit structure imposed by STREAMS

  • service times (path-lengths) obtained from code

measurements

  • analysis using simulation or analytical techniques
slide-7
SLIDE 7

STREAMS Modeling

user process user process stream head stream head Multiplexor module module user space driver driver kernel space hardware

slide-8
SLIDE 8

queues in a module

messages (packets)

queues in a multiplexor

slide-9
SLIDE 9
  • Message priorities in a STREAMS queue

– normal messages – expedited messages (levels 1 to 255) – high-priority messages – FIFO scheduling within each priority band

  • Message passing from one queue to another in

STREAMS

– involves kernel routines

  • putnext ( )
  • put ( )
  • putq ( )
  • service ( )
slide-10
SLIDE 10

(1) queue A service calls putnext (2) putnext passes message to queue B put (3) queue B put processes message (4) put passes message to putq (5) putq places message

  • n queue B

(6) putq schedules queue B service

queue A queue B

slide-11
SLIDE 11
  • Scheduling of Service Routines by STREAMS

– service routines called by STREAMS scheduler – STREAMS scheduler is FIFO – STREAMS scheduler processes all messages on a queue when service routine is called

  • Inter-Queue Flow Control in STREAMS

– counter q_count – high and low water marks – service routines “put to sleep” if flow control in force – service routines “woken up” when flow control removed

slide-12
SLIDE 12

State-Dependent FIFO Queueing Model

state of STREAMS queues creation

  • f

service routines data traffic

service routines

STREAMS scheduler

slide-13
SLIDE 13

Model Analysis

  • Analytical

– difficult due to complex state-dependencies – possible to develop a simplified Markov chain model – a challenging performance analysis problem

  • Simulation

– simulation model implemented using OPNET Modeler – could automate building of OPNET model using OPNET EMA interface

slide-14
SLIDE 14

Concluding Remarks

  • Software performance modeling and analysis is an

essential for data-networking product development

  • research needed for SPE of data-networking

products

  • automated tools are needed for SPE
  • we proposed a queueing model for SPE of

STREAMS-based data-networking products