Auto-constructing dataflow models from system execution traces

Author

Peiris, Manjula ; Al Hasan, Mohammad ; Hill, James H.

Author_Institution

Dept. of Comput. & Inf. Sci., Indiana Univ.-Purdue Univ. Indianapolis, Indianapolis, IN, USA

fYear

2013

fDate

19-21 June 2013

Firstpage

1

Lastpage

10

Abstract

This paper presents a method and tool named the Dataflow Model Auto-Constructor (DMAC). DMAC uses frequent-sequence mining and Dempster-Shafer theory to mine a system execution trace and reconstruct its corresponding dataflow model. Distributed system testers then use the resultant dataflow model to analyze performance properties (e.g., end-to-end response time, throughput, and service time) captured in the system execution trace. Results from applying DMAC to different case studies show that DMAC can reconstruct dataflow models that cover at most 94% of the events in the original system execution trace. Likewise, more than 2 sources of evidence are needed to reconstruct dataflow models for systems with multiple execution contexts.

Keywords

data flow computing; data mining; distributed processing; inference mechanisms; program diagnostics; program testing; uncertainty handling; DMAC; Dempster-Shafer theory; auto-constructing dataflow models; dataflow model auto-constructor; dataflow model reconstruction; distributed system testers; frequent-sequence mining; performance properties; resultant dataflow model; system execution trace mining; system execution traces; Analytical models; Authentication; Computational modeling; Context; Mathematical model; Time factors; Vectors; DMAC; auto-construction; dataflow models; domain knowledge; evidence theory; frequent-sequence mining; quality-of-service; system execution traces;

fLanguage

English

Publisher

ieee

Conference_Titel

Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC), 2013 IEEE 16th International Symposium on

Conference_Location

Paderborn

Type

conf

DOI

10.1109/ISORC.2013.6913203

Filename

6913203