Big data applications with their high-volume and dynamically changing data streams impose new challenges to application performance management. Efficient and effective solutions must balance performance versus result precision and cope with dramatic changes in real-time load and needs without over-provisioning resources. Moreover, a developer should not be burdened too much with addressing performance management issues, so he can focus on the functional perspective of the system For addressing these challenges, we present a novel comprehensive approach, which combines software configuration, model-based development, application performance management and runtime adaptation.
Описание
Special part: Proceedings of the Symposium on Software Performance (SSP) 2015, 4.-6. November, 2015, Munich, Germany
%0 Journal Article
%1 eichelberger2015adaptive
%A Eichelberger, Holger
%A Qin, Cui
%A Schmid, Klaus
%A Niederée, Claudia
%D 2015
%J Softwaretechnik-Trends
%K adaptation myown qualimaster
%N 3
%P 35-37
%T Adaptive Application Performance Management for Big Data Stream Processing
%V 35
%X Big data applications with their high-volume and dynamically changing data streams impose new challenges to application performance management. Efficient and effective solutions must balance performance versus result precision and cope with dramatic changes in real-time load and needs without over-provisioning resources. Moreover, a developer should not be burdened too much with addressing performance management issues, so he can focus on the functional perspective of the system For addressing these challenges, we present a novel comprehensive approach, which combines software configuration, model-based development, application performance management and runtime adaptation.
@article{eichelberger2015adaptive,
abstract = {Big data applications with their high-volume and dynamically changing data streams impose new challenges to application performance management. Efficient and effective solutions must balance performance versus result precision and cope with dramatic changes in real-time load and needs without over-provisioning resources. Moreover, a developer should not be burdened too much with addressing performance management issues, so he can focus on the functional perspective of the system For addressing these challenges, we present a novel comprehensive approach, which combines software configuration, model-based development, application performance management and runtime adaptation.},
added-at = {2016-03-08T09:47:48.000+0100},
author = {Eichelberger, Holger and Qin, Cui and Schmid, Klaus and Niederée, Claudia},
biburl = {https://www.bibsonomy.org/bibtex/2275b302cbcb3dd463ac687357f5d7ec4/eichelbe},
description = {Special part: Proceedings of the Symposium on Software Performance (SSP) 2015, 4.-6. November, 2015, Munich, Germany},
interhash = {3fc2f9bd2595e65f4f558910d078b8f2},
intrahash = {275b302cbcb3dd463ac687357f5d7ec4},
journal = {Softwaretechnik-Trends},
keywords = {adaptation myown qualimaster},
month = nov,
number = 3,
pages = {35-37},
timestamp = {2016-03-09T16:22:16.000+0100},
title = {Adaptive Application Performance Management for Big Data Stream Processing},
volume = 35,
year = 2015
}