TY - JOUR
T1 - Elasticity management of Streaming Data Analytics Flows on clouds
AU - Khoshkbarforoushha, Alireza
AU - Khosravian, Alireza
AU - Ranjan, Rajiv
N1 - Publisher Copyright:
© 2016 Elsevier Inc.
PY - 2017/11
Y1 - 2017/11
N2 - In this paper, we present a framework for resource management of Streaming Data Analytics Flows (SDAF). Using advanced techniques in control and optimization theory, we design an adaptive control system tailored to the data ingestion, analytics, and storage layers of the SDAF that is able to continuously detect and self-adapt to workload changes for meeting the users’ service level objectives. Our experiments based on a real-world SDAF show that, the proposed control scheme is able to reduce the deviation from desired utilization of resources by up to 48% compared to existing techniques.
AB - In this paper, we present a framework for resource management of Streaming Data Analytics Flows (SDAF). Using advanced techniques in control and optimization theory, we design an adaptive control system tailored to the data ingestion, analytics, and storage layers of the SDAF that is able to continuously detect and self-adapt to workload changes for meeting the users’ service level objectives. Our experiments based on a real-world SDAF show that, the proposed control scheme is able to reduce the deviation from desired utilization of resources by up to 48% compared to existing techniques.
KW - Control theory
KW - Data analytics flow
KW - Data-intensive workloads
KW - Multi-objective optimization
KW - Public clouds
KW - Resource management
UR - http://www.scopus.com/inward/record.url?scp=85007550368&partnerID=8YFLogxK
U2 - 10.1016/j.jcss.2016.11.002
DO - 10.1016/j.jcss.2016.11.002
M3 - Article
SN - 0022-0000
VL - 89
SP - 24
EP - 40
JO - Journal of Computer and System Sciences
JF - Journal of Computer and System Sciences
ER -