TY  - JOUR
IS  - 4
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
SP  - 1277
VL  - 15
A1  - Tangari, G
A1  - Tuncer, D
A1  - Charalambides, M
A1  - Qi, Y
A1  - Pavlou, G
JF  - IEEE Transactions on Network and Service Management
UR  - https://doi.org/10.1109/TNSM.2018.2874813
SN  - 1932-4537
AV  - public
Y1  - 2018/12//
EP  - 1291
TI  - Self-Adaptive Decentralized Monitoring in Software-Defined Networks
KW  - Monitoring
KW  -  Switches
KW  -  Hardware
KW  -  Synchronization
KW  -  Resource management
KW  -  Task analysis
KW  - Frequency measurement
KW  -  Network monitoring
KW  -  Software-Defined Networks
KW  -  Self-adaptation.
N2  - The Software-Defined Networking (SDN) paradigm can allow network management solutions to automatically and frequently reconfigure network resources. When developing SDNbased management architectures, it is of paramount importance to design a monitoring system that can provide timely and consistent updates to heterogeneous management applications. To support such applications operating with low latency requirements, the monitoring system should scale with increasing network size and provide precise network views with minimum overhead on the available resources. In this paper we present a novel, self-adaptive, decentralized framework for resource monitoring in SDN. Our framework enables accurate statistics to be collected with limited burden on the network resources. This is realized through a self-tuning, adaptive monitoring mechanism that automatically adjusts its settings based on the traffic dynamics. We evaluate our proposal based on a realistic use case scenario, where a content distribution service and an on-demand gaming platform are deployed within an ISP network. The results show that reduced monitoring latencies are obtained with the proposed framework, thus enabling shorter reconfiguration control loops. In addition, the proposed adaptive monitoring method achieves significant gain in terms of monitoring overhead, while preserving the performance of the services considered.
ID  - discovery10060070
ER  -