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Metrics for Performance Evaluation of Distributed Application Execution in Ubiquitous Computing Environments Prithwish Basu ECE Department, Boston University.

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Presentation on theme: "Metrics for Performance Evaluation of Distributed Application Execution in Ubiquitous Computing Environments Prithwish Basu ECE Department, Boston University."— Presentation transcript:

1 Metrics for Performance Evaluation of Distributed Application Execution in Ubiquitous 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 ACM UbiComp’01 Workshop on Evaluation Methods for Ubiquitous Computing

2 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 FocusFocus: Distributed protocols that are adaptive to network partitions due to user/device mobility

3 A Smart Presentation Application Keyboard Mouse Smart Storage/CPU (possibly mobile) Overhead Display Local Screen Wireless PDA (mobile user) auxiliary devices to control presentation (can be moved) presentation data summary data-flow edge proximity edge The user does not care which particular devices perform the presentation

4 Task Graphs and Embedding U A B C Task Graph a1 d2 a2 b1 b2 c1 c2 u d1 a1 d2 a2 b1 b2 c1 c2 u d1 Embedding 1 A  a1 B  b2 C  c2 Embedding 2 A  a2 B  b1 C  c1 nodes (colors indicate distinct device categories) non-tree edge tree edges (Paths in G)

5 Metrics to Evaluate Performance CategorySymbolUnitDescription SpeedT-embedsec. Time taken to discover a mapping Quality Dilationhops Avg. stretch of TG when mapped onto G Node Congestion +ve Real number Avg. number of mapped paths passing through a node in G EfficiencyOverhead #pkts Messaging overhead to find an embedding Resilience to Mobility FrqDisrupt/min. Frequency of application disruption T-recovsec. Time to recover from an app. disruption Application perf. after embedding EffThrptin [0,1] #ADUs recvd. at sink / #ADUs that should have been recvd. under ideal conditions Delaymsec. Time taken by an ADU to reach destn. NumRe-Tx/flow #Re-Tx at source needed for a data flow

6 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


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