TY - GEN
T1 - Routing Oblivious Measurement Analytics
AU - Basat, Ran Ben
AU - Chen, Xiaoqi
AU - Einziger, Gil
AU - Feibish, Shir Landau
AU - Raz, Danny
AU - Yu, Minlan
N1 - Publisher Copyright:
© 2020 IFIP.
PY - 2020/6/1
Y1 - 2020/6/1
N2 - Network-wide traffic analytics are often needed for various network monitoring tasks. These measurements are often performed by collecting samples at network switches, which are then sent to the controller for aggregation. However, performing such analytics without 'overcounting'' flows or packets that traverse multiple measurement switches is challenging. Therefore, existing solutions often simplify the problem by making assumptions on the routing or measurement switch placement. We introduce AROMA, a measurement infrastructure that generates a uniform sample of packets and flows regardless of the topology, workload and routing. Therefore, AROMA can be deployed in many settings, and can also work in the data plane using programmable PISA switches. The AROMA infrastructure includes controller algorithms that approximate a variety of essential measurement tasks while providing formal accuracy guarantees. Using extensive simulations on real-world network traces, we show that our algorithms are competitively accurate compared to the best existing solutions despite the fact that they make no assumptions on the underlying network or the placement of measurement switches.
AB - Network-wide traffic analytics are often needed for various network monitoring tasks. These measurements are often performed by collecting samples at network switches, which are then sent to the controller for aggregation. However, performing such analytics without 'overcounting'' flows or packets that traverse multiple measurement switches is challenging. Therefore, existing solutions often simplify the problem by making assumptions on the routing or measurement switch placement. We introduce AROMA, a measurement infrastructure that generates a uniform sample of packets and flows regardless of the topology, workload and routing. Therefore, AROMA can be deployed in many settings, and can also work in the data plane using programmable PISA switches. The AROMA infrastructure includes controller algorithms that approximate a variety of essential measurement tasks while providing formal accuracy guarantees. Using extensive simulations on real-world network traces, we show that our algorithms are competitively accurate compared to the best existing solutions despite the fact that they make no assumptions on the underlying network or the placement of measurement switches.
UR - http://www.scopus.com/inward/record.url?scp=85090048462&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85090048462
T3 - IFIP Networking 2020 Conference and Workshops, Networking 2020
SP - 449
EP - 457
BT - IFIP Networking 2020 Conference and Workshops, Networking 2020
PB - Institute of Electrical and Electronics Engineers
T2 - 2020 IFIP Networking Conference and Workshops, Networking 2020
Y2 - 22 June 2020 through 25 June 2020
ER -