Using control theory to guide load shedding in medical data stream management system

Authors:
Zijing Hu;Hongyan Li;Baojun Qiu;Lv-an Tang;Yu Fan;Haibin Liu;Jianlong Gao;Xinbiao Zhou
Affiliations:
National Laboratory on Machine Perception, School of Electronics Engineering and Computer Science, Peking University, Beijing, P. R. China;National Laboratory on Machine Perception, School of Electronics Engineering and Computer Science, Peking University, Beijing, P. R. China;National Laboratory on Machine Perception, School of Electronics Engineering and Computer Science, Peking University, Beijing, P. R. China;National Laboratory on Machine Perception, School of Electronics Engineering and Computer Science, Peking University, Beijing, P. R. China;National Laboratory on Machine Perception, School of Electronics Engineering and Computer Science, Peking University, Beijing, P. R. China;National Laboratory on Machine Perception, School of Electronics Engineering and Computer Science, Peking University, Beijing, P. R. China;National Laboratory on Machine Perception, School of Electronics Engineering and Computer Science, Peking University, Beijing, P. R. China;National Laboratory on Machine Perception, School of Electronics Engineering and Computer Science, Peking University, Beijing, P. R. China
Venue:
ASIAN'05 Proceedings of the 10th Asian Computing Science conference on Advances in computer science: data management on the web
Year:
2005

Citing 11
Cited 2

Models and issues in data stream systems

Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Digital Control of Dynamic Systems

Digital Control of Dynamic Systems
Chain: operator scheduling for memory minimization in data stream systems

Proceedings of the 2003 ACM SIGMOD international conference on Management of data
A Feedback Control Approach for Guaranteeing Relative Delays in Web Servers

RTAS '01 Proceedings of the Seventh Real-Time Technology and Applications Symposium (RTAS '01)
Aurora: a new model and architecture for data stream management

The VLDB Journal — The International Journal on Very Large Data Bases
Data stream management system for MavHome

Proceedings of the 2004 ACM symposium on Applied computing
Load Shedding for Aggregation Queries over Data Streams

ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Adaptive stream resource management using Kalman Filters

SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Data Triage: An Adaptive Architecture for Load Shedding in TelegraphCQ

ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Monitoring streams: a new class of data management applications

VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Load shedding in a data stream manager

VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29

PGG: an online pattern based approach for stream variation management

Journal of Computer Science and Technology
Monitoring abnormal patterns with complex semantics over ICU data streams

IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis

Quantified Score

Hi-index	0.01

Visualization

Abstract

The load shedding problem is vital to a Data Stream Management System (DSMS). This paper presents the design, implementation, and evaluation of a load shedding method under the guide of the feedback control theory, in order to solve practical problems in medical environment. Thus, the using of operator selectivity, which has been proven not stable enough, is avoided. This paper focuses on the restriction of memory resource, this prevents the overflow of both CUP and memory resource. Our method can well support ad-hoc queries, while it is not so in a DSMS using current load shedding method because of the instability of operator selectivity. Our method also ensures a higher query precision when the system is over loaded and is easy to be implemented. The analytical and experimental results show that our method can be applied to medical data stream systems efficiently.