Paper Info
Title
Exploiting Application Semantics in Monitoring Real-Time Data Streams
Abstract
Real-time stream processing applications must be prepared to operate under overloaded conditions. Existing load shedding techniques are not suitable for processing real-time data streams because their tuple dropping policies may violate application deadlines in an uncontrolled way. We'd argue that a more precise load shedding model, e.g., the (m, k) deadline model adopted in this paper, is much appropriate than the commonly used random dropping policy. Based on the (m, k) load shedding model and a novel load shedding approach, we propose a concrete (m, k) scheduling algorithm called SOSA-DBP by exploiting application semantics. Experimental results show that SOSA-DBP has significant performance gain over the existing (m, k) scheduling algorithm.
Year
DOI
Venue
2008
10.1109/WAIM.2008.26
WAIM
Keywords
Field
DocType
real-time data stream monitoring,random dropping policy,application semantics,performance gain,scheduling,deadline model,monitoring real-time data streams,operator scheduling,precise load,resource allocation,existing load,novel load,real-time data stream,tuple dropping policies,sosa-dbp scheduling algorithm,real-time stream processing application,load shedding,data handling,overloaded condition,load shedding technique,real-time data streams,exploiting application semantics,application deadline,real time systems,concrete,exponential distribution,data models,information management,scheduling algorithm,stream processing,computer science,application software,real time data
Data modeling,Data stream mining,Computer science,Scheduling (computing),Tuple,Real-time computing,Resource allocation,Application software,Stream processing,Group method of data handling,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-0-7695-3185-4
0
0.34
References 
Authors
15
6
Name
Order
Citations
PageRank
Hongya Wang1327.95
Lihchyun Shu213017.32
Zhidong Qin301.01
Xiaoqiang Liu400.34
Jing Cong560.85
Hui Song621.40