Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/SEAMS.2017.20 | SEAMS@ICSE |
Keywords | Field | DocType |
self-adaptation,Big Data analytics,model problem | Data mining,Computer science,Reference implementation,Input/output,Self adaptation,Analytics,Extensibility,Big data,System model | Conference |
ISBN | Citations | PageRank |
978-1-5386-1551-5 | 9 | 0.57 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sanny Schmid | 1 | 9 | 0.57 |
| Ilias Gerostathopoulos | 2 | 254 | 26.55 |
| Christian Prehofer | 3 | 9 | 0.57 |
| Tomás Bures | 4 | 187 | 28.93 |