IntOpt: In-band Network Telemetry Optimization Framework to Monitor Network Slices using P4

TY - JOUR

T1 - IntOpt

T2 - In-band Network Telemetry Optimization Framework to Monitor Network Slices using P4

AU - Bhamare, Deval

AU - Kassler, Andreas

AU - Vestin, Jonathan

AU - Khoshkholghi, Mohammad Ali

AU - Taheri, Javid

AU - Mahmoodi, Toktam

AU - Ohlen, Peter

AU - Curescu, Calin

N1 - Funding Information: The authors received partial funding from the Knowledge Foundation of Sweden through the Profile HITS. Publisher Copyright: © 2022 The Author(s)

PY - 2022/10/24

Y1 - 2022/10/24

N2 - The emergence of Network Functions Virtualization (NFV) is being heralded as an enabler of the recent technologies such as 5G/6G, IoT and heterogeneous networks. Existing NFV monitoring frameworks either do not have the capabilities to express the range of telemetry items needed to perform management or do not scale to large traffic volumes and rates. We present IntOpt, a scalable and expressive telemetry system designed for flexible NFV monitoring using active probing and P4. IntOpt allows us to specify monitoring requirements for individual service chain, which are mapped to telemetry item collection jobs that fetch the required telemetry items from P4 programmable data-plane elements. We propose mixed integer linear program (MILP) as well as a simulated annealing based random greedy (SARG) meta-heuristic approach to minimize the overhead due to active probing and collection of telemetry items. Using P4-FPGA, we benchmark the overhead for telemetry collection. Our numerical evaluation shows that the proposed approach can reduce monitoring overheads by 39% and monitoring delays by 57%. Such optimization may as well enable existing expressive monitoring frameworks to scale for larger real-time networks.

AB - The emergence of Network Functions Virtualization (NFV) is being heralded as an enabler of the recent technologies such as 5G/6G, IoT and heterogeneous networks. Existing NFV monitoring frameworks either do not have the capabilities to express the range of telemetry items needed to perform management or do not scale to large traffic volumes and rates. We present IntOpt, a scalable and expressive telemetry system designed for flexible NFV monitoring using active probing and P4. IntOpt allows us to specify monitoring requirements for individual service chain, which are mapped to telemetry item collection jobs that fetch the required telemetry items from P4 programmable data-plane elements. We propose mixed integer linear program (MILP) as well as a simulated annealing based random greedy (SARG) meta-heuristic approach to minimize the overhead due to active probing and collection of telemetry items. Using P4-FPGA, we benchmark the overhead for telemetry collection. Our numerical evaluation shows that the proposed approach can reduce monitoring overheads by 39% and monitoring delays by 57%. Such optimization may as well enable existing expressive monitoring frameworks to scale for larger real-time networks.

UR - https://www.scopus.com/pages/publications/85135954926

U2 - 10.1016/j.comnet.2022.109214

DO - 10.1016/j.comnet.2022.109214

M3 - Article

SN - 1389-1286

VL - 216

JO - COMPUTER NETWORKS

JF - COMPUTER NETWORKS

M1 - 109214

ER -