Traffic monitoring is the essential capability for large-scale enterprises, data centers, service providers, and network operators to ensure reliability, availability, fault assurance, and security of their underlying network resources. Currently, most monitoring solutions are standalone dedicated ones. Major drawbacks of these dedicated standalone appliances per-feature are high-cost, lack of flexibility, slow install time and difficulty of maintenance. Network Function Virtualization (NFV) provides an attractive alternative to cope with such limitations by controlling both CAPEX and OPEX. Network traffic monitoring function virtualization brings not only CAPEX/OPEX advantages but also introduces some challenges such as ensuring scalability and performance of single or distributed multiple virtual monitoring functions, utilization of virtual functions, and flexibility and easiness of virtual functions lifecycle management. To address such challenges, we propose a novel architecture and proof-of-concept implementation of a software-defined Virtual TrAffic Monitoring function with resource Auto-Scaling capability built over a multi-core whitebox server (VTAMAS) in this paper. It virtualizes monitoring functions with the capability of auto-scaling resource of virtual functions for efficient resource utilization especially.
[1]
Luca Deri,et al.
High speed network traffic analysis with commodity multi-core systems
,
2010,
IMC '10.
[2]
Andrew C. Myers,et al.
JFlow: practical mostly-static information flow control
,
1999,
POPL '99.
[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]
Monia Ghobadi,et al.
OpenTM: Traffic Matrix Estimator for OpenFlow Networks
,
2010,
PAM.
[5]
Taesang Choi,et al.
SuVMF: software-defined unified virtual monitoring function for SDN-based large-scale networks
,
2014,
CFI '14.
[6]
Harsha V. Madhyastha,et al.
FlowSense: Monitoring Network Utilization with Zero Measurement Cost
,
2013,
PAM.