Robocop

1
Prediction of Run-Time Resource Consumption of
Multi-Task Component Based Systems Location
7th (ICSE) CBSE Workshop Edinburgh
International Conference Centre
Date 25-5-2004
Johan Muskens J.Muskens_at_tue.nl System
Architecture and Networking University of
Technologies Eindhoven http//www.win.tue.nl/san/
2
Outline

• Introduction
• Background
• Problem Domain
• Problem
• Approach
• Intermezzo (Terminology)
• Prediction process
• Discussion

3
Background
Research is part of 2 ITEA projects

• Robocop
• Define an open, component-based framework for the
  middle-ware layer in high-volume consumer devices
  (robustness/reliability, upgrading/extension, and
  trading)
• Space4U
• Extend and validate the Architecture
• Fault Management
• Power Management
• Terminal Managment

4
Problem Domain

• High Volume Consumer Electronics
• Embedded systems (resources are expensive and
  fixed)
• Third-party service binding
• Multiple component / service suppliers
• Run-time binding

5
The Problem

• Predict dynamic resource consumption of a
  component based application based on the design
  of the applications and models describing
  resource consumption and behavior of the used
  components.

Behavior Model
Resource Model
6
Approach

• Scenario Based Resource Estimation
• Predict resource consumption for single
  (critical, average, ) case.
• Specify Behavior Resource Consumption at
  Interface Level
• Combine Behavior Resource Consumption for
  specific scenario
• Worst Case
• Average Case

7
Outline

• Introduction
• Background
• Problem Domain
• Problem
• Approach
• Intermezzo (Terminology)
• Prediction process
• Discussion

8
Intermezzo (Terminology)

• A Robocop Component is a set of models

• Execution component contains a set of Services

• Service has set of Interfaces (provides
  requires)

• An Interface has a set of Operations

• At runtime Services are instantiated (Service
  Instance object)

9
Outline

• Introduction
• Background
• Problem Domain
• Problem
• Approach
• Intermezzo (Terminology)
• Prediction process
• Discussion

10
Prediction Process Resource Consumption
Specification

• We distinguish
• Pre-emptive ? Non-pre-emptive
• Processing ? Non-processing

Processing Non-processing
Pre-emptive Require X Require X Release Y
Non-pre-emptive Claim X Claim X Release Y
CPU
Memory
11
Prediction ProcessBehavior Specification

• operation f calls
• I2.g
• I3.h

partial

12
Prediction Process Service Specification

• service s1
• requires I2
• requires I3
• provides I1
• operation f
• uses I2.g
• uses I3.h
• resource claim 100
• release 100
• behaviour
• operation f calls
• I2.g
• I3.h

Service is run-time unit of structuring
Resource consumption is modeled per operation
Resource claims / releases are modeled explicitly
Behaviour of each operation is modeled using a
partial MSC
variable call sequences can be modelled
13
Prediction Process Model Assembly Phase

• A scenario defines a plausible sequence of
  operations of an application
• A model for this scenario is constructed by
• selecting the operations needed for realizing
  this scenario
• composing the resource and behaviour models of
  these operations.
• This yields a structure that contains exactly the
  resource information that is needed for this
  scenario.

(5,5)
(10,8)
(2,2)
(5,5)
(10,8)
(8,10)
(8,10)
(2,2)
14
Prediction Process Model Analysis Phase

• Resources are accumulated by resource-combinators
  
• Different types of analysis are supported by
  defining
• corresponding combinators

(a,b) ? (c,d) (a-bc,d) if b c
(a,b-cd) if b gt c (a,b) ? (c,d) (ac,bd)
(5,5)
(10,8)
(8,10)
(2,2)
((5,5) ? (((10,8) ? (8,10)) ? (2,2)))
15
Dealing with Concurrent Tasks

• Task based accumulation of resource consumption
• Abstract Resource Combinators ? Cannot be
  evaluated
• Accumulation of per task resource consumption
  based on scheduling policy
• Abstract Resource Combinators ? Evaluatable
  Resource Combinators

((5,5) ? (2,2))
((5,5) ? (12,12))
(((10,8) ? (8,10)) ? (2,2))
((2,2) ? (((10,8) ? (8,10)) ? (2,2))
(((10,8) ? (8,10)) ? (2,2)) ((2,2) ?
(((10,8) ? (8,10)) ? (2,2)) ((5,5) ?
(2,2)) ((5,5) ? (12,12)) )
16
Outline

• Introduction
• Background
• Problem Domain
• Problem
• Approach
• Intermezzo (Terminology)
• Prediction process
• Discussion

17
Benefits of Prediction Technique

• Prediction of dynamic memory consumption
• Support third-party binding
• Easy to adopt / apply
• Flexible w.r.t. how to predict and what to
  predict
• General useful (e.g., during design /
  implementation / deployment)
• Trade-off between accuracy and effort

18
Future work

• Make resource claim / release dependent on data
• Model sychronization between tasks
• Part of the Space4U project
• Model interaction between multiple resources
• Trading resources
• Increase accuracy
• Use better resource combinators
• More validation

19

• ?