TY - GEN
T1 - Self-Adaptation Based on Big Data Analytics
T2 - 12th IEEE/ACM International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2017
AU - Schmid, Sanny
AU - Gerostathopoulos, Ilias
AU - Prehofer, Christian
AU - Bures, Tomas
PY - 2017/7/3
Y1 - 2017/7/3
N2 - In this paper, we focus on self-adaptation in large-scale software-intensive distributed systems. The main problem in making such systems self-adaptive is that their adaptation needs to consider the current situation in the whole system. However, developing a complete and accurate model of such systems at design time is very challenging. To address this, we present a novel approach where the system model consists only of the essential input and output parameters. Furthermore, Big Data analytics is used to guide self-adaptation based on a continuous stream of operational data. We provide a concrete model problem and a reference implementation of it that can be used as a case study for evaluating different self-adaptation techniques pertinent to complex large-scale distributed systems. We also provide an extensible tool for endorsing an arbitrary system with self-adaptation based on analysis of operational data coming from the system. To illustrate the tool, we apply it on the model problem.
AB - In this paper, we focus on self-adaptation in large-scale software-intensive distributed systems. The main problem in making such systems self-adaptive is that their adaptation needs to consider the current situation in the whole system. However, developing a complete and accurate model of such systems at design time is very challenging. To address this, we present a novel approach where the system model consists only of the essential input and output parameters. Furthermore, Big Data analytics is used to guide self-adaptation based on a continuous stream of operational data. We provide a concrete model problem and a reference implementation of it that can be used as a case study for evaluating different self-adaptation techniques pertinent to complex large-scale distributed systems. We also provide an extensible tool for endorsing an arbitrary system with self-adaptation based on analysis of operational data coming from the system. To illustrate the tool, we apply it on the model problem.
KW - Big Data analytics
KW - model problem
KW - self-adaptation
UR - http://www.scopus.com/inward/record.url?scp=85027142588&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027142588&partnerID=8YFLogxK
U2 - 10.1109/SEAMS.2017.20
DO - 10.1109/SEAMS.2017.20
M3 - Conference contribution
AN - SCOPUS:85027142588
T3 - Proceedings - 2017 IEEE/ACM 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2017
SP - 102
EP - 108
BT - Proceedings - 2017 IEEE/ACM 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 May 2017 through 23 May 2017
ER -