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ACM UbiComp’01 Workshop on Evaluation Methods for Ubiquitous Computing. Metrics for Performance Evaluation of Distributed Application Execution in Ubiquitous Computing Environments. Prithwish Basu ECE Department, Boston University pbasu@bu.edu

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ACM UbiComp’01 Workshop on

Evaluation Methods for Ubiquitous Computing

Metrics for Performance Evaluation of Distributed Application Execution inUbiquitous Computing Environments

Prithwish Basu

ECE Department, Boston University

pbasu@bu.edu

(Joint work with Wang Ke and Thomas D.C. Little)

Project URL: http://hulk.bu.edu/projects/adhoc/summary.html

This work was supported in part by NSF under Grant #ANI-0073843

overview of our project
Overview of our Project
  • We are investigating techniques for modeling distributed UbiComp applications
  • Logical representation of applications in terms of component sub-tasks as resource dependency graphs or task graphs
  • Physical resources are selected on-the-fly just before actual execution of task
  • Focus: Distributed protocols that are adaptive to network partitions due to user/device mobility
a smart presentation application

data-flow edge

proximity edge

A Smart Presentation Application

The user does not care which particular

devices perform the presentation

Overhead

Display

summary

presentation data

Smart Storage/CPU

(possibly mobile)

Wireless PDA

(mobile user)

auxiliary devices

to control presentation

(can be moved)

Mouse

Local

Screen

Keyboard

task graphs and embedding

(Paths in G)

Task Graphs and Embedding

nodes (colors indicate

distinct device categories)

d2

a1

b2

Embedding 1

Aa1

Bb2

Cc2

a2

d1

b1

U

u

A

c1

c2

tree edges

d2

a1

b2

B

C

Embedding 2

Aa2

Bb1

Cc1

a2

non-tree edge

d1

b1

Task Graph

u

c1

c2

variable system parameters
Variable System Parameters
  • Topology related:
    • #Devices in network ~ richness of network connectivity etc.
    • Fraction of devices that rely on wireless AP / ad hoc routing
    • Mobility patterns: random  highly predictable
  • Task Graph related:
    • #nodes in a task/resource graph
    • Complexity of relationships between nodes of a task graph
    • #Instances of resources in a network with similar capabilities
  • Traffic related
    • Average data rates of applications
    • Background traffic patterns: low load  heavy load
    • #Instances of simultaneously running tasks