TY - GEN
T1 - A tool for online experiment-driven adaptation
AU - Gerostathopoulos, Ilias
AU - Uysal, Ali Naci
AU - Prehofer, Christian
AU - Bures, Tomas
PY - 2019/1/2
Y1 - 2019/1/2
N2 - In this paper, we present Online Experiment-Driven Adaptation (OEDA), a tool for performing end-to-end optimization of a target system abstracted as a black-box by combining statistical and optimization methods and providing statistical guarantees along the optimization process. We present the requirements and architecture of OEDA and describe its built-in optimization process that chains together factorial design, Bayesian optimization, and t-test. OEDA allows the user to create reusable abstractions of systems-to-be-optimized and specify, run and observe the execution of end-to-end experiments. For instance, we support data exchange with common tools like Kafka, MQTT and HTTP. We show the benefits of OEDA in a web server application example. OEDA can be a useful vehicle for research in the area of automated experimentation, an emerging challenge where systems are capable of performing experiments (akin to A/B testing) to themselves in order to self-optimize.
AB - In this paper, we present Online Experiment-Driven Adaptation (OEDA), a tool for performing end-to-end optimization of a target system abstracted as a black-box by combining statistical and optimization methods and providing statistical guarantees along the optimization process. We present the requirements and architecture of OEDA and describe its built-in optimization process that chains together factorial design, Bayesian optimization, and t-test. OEDA allows the user to create reusable abstractions of systems-to-be-optimized and specify, run and observe the execution of end-to-end experiments. For instance, we support data exchange with common tools like Kafka, MQTT and HTTP. We show the benefits of OEDA in a web server application example. OEDA can be a useful vehicle for research in the area of automated experimentation, an emerging challenge where systems are capable of performing experiments (akin to A/B testing) to themselves in order to self-optimize.
KW - Automated experimentation
KW - Data-driven runtime decision-making
KW - Statistical guarantees
KW - Tool
UR - http://www.scopus.com/inward/record.url?scp=85061558741&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061558741&partnerID=8YFLogxK
U2 - 10.1109/FAS-W.2018.00032
DO - 10.1109/FAS-W.2018.00032
M3 - Conference contribution
AN - SCOPUS:85061558741
T3 - Proceedings - 2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2018
SP - 100
EP - 105
BT - Proceedings - 2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2018
Y2 - 3 September 2018 through 7 September 2018
ER -