Cheapo: An algorithm for runtime adaption of time intervals applied in 5G networks

The explosion of mobile devices and content, server visualization, and advent of cloud services are among the trends driving the networking industry to re-examine traditional network architectures. Software-Defined Networking (SDN) is an emerging architecture that is dynamic, manageable, cost-effective, and adaptable, making it ideal for the high-bandwidth, dynamic nature of today's applications. Currently, it continues to be crucial for businesses to monitor their networks in order to be productive and avoid serious threats from network failures and server downtime, demanding accuracy and timeliness. In this paper, we focus on this trade-off between monitoring accuracy and timeliness. We propose an adaptive algorithm for monitoring frameworks in SDN, which aims to provide highly accurate information without producing unnecessary traffic to the network. We also thought to name our algorithm “Cheapo”, due to the fact that we will be directed to an overall cheap network regarding the overhead produced by collecting monitoring data.

[1]  Lionel Sacks,et al.  Adaptive Sampling Mechanisms in Sensor Networks , 2003 .

[2]  Giuseppe Anastasi,et al.  Energy management in wireless sensor networks with energy-hungry sensors , 2009, IEEE Instrumentation & Measurement Magazine.

[3]  Raouf Boutaba,et al.  PayLess: A low cost network monitoring framework for Software Defined Networks , 2014, 2014 IEEE Network Operations and Management Symposium (NOMS).

[4]  Ying Zhang,et al.  An adaptive flow counting method for anomaly detection in SDN , 2013, CoNEXT.

[5]  Minlan Yu,et al.  Software Defined Traffic Measurement with OpenSketch , 2013, NSDI.

[6]  Andrew C. Myers,et al.  JFlow: practical mostly-static information flow control , 1999, POPL '99.

[7]  Abdulsalam Yassine,et al.  Software defined network traffic measurement: Current trends and challenges , 2015, IEEE Instrumentation & Measurement Magazine.

[8]  Robert D. Nowak,et al.  Backcasting: adaptive sampling for sensor networks , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[9]  Philip S. Yu,et al.  ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks , 2007, IEEE Transactions on Parallel and Distributed Systems.

[10]  Shenghui Zhao,et al.  Context-based adaptive arithmetic coding in time and frequency domain for the lossless compression of audio coding parameters at variable rate , 2013, EURASIP J. Audio Speech Music. Process..

[11]  Edward Y. Chang,et al.  Adaptive sampling for sensor networks , 2004, DMSN '04.

[12]  Ramesh Govindan,et al.  Resource/accuracy tradeoffs in software-defined measurement , 2013, HotSDN '13.

[13]  Nicholas R. Jennings,et al.  Decentralized control of adaptive sampling in wireless sensor networks , 2009, TOSN.