Fairness Issues in RED Gateways

Srinivas R. Avasarala

sra@cs.purdue.edu

CS Dept., Purdue University

Motivations

• Study the aims and approaches of RED and the variants proposed, SRED and FRED, to achieve fairness.

• Provide Implementations of RED variants on Network Simulator ns.

• Analyze and compare performances

RED Overview

• Detects congestion using avg. queue size

• Drop probability is a function of the avg. queue size. [min_th, max_th, p_max]

• No bias against bursty traffic

• No global synchronization

• A flow’s drop rate is proportional to its share of the bandwidth through the gateway

RED Drawbacks

• There is no TCP-awareness• Dropping packets from flows in proportion

to their bandwidth doesn’t result in fair sharing. e.g Fast link vs. a Slow link

• Does not consider the number of flows• Discrimination against:

– Large RTT flows w.r.t. Small RTT flows– Adaptive flows w.r.t. Non-adaptive flows

SRED: Stabilized RED

• Avg. Q size is not used for drop probability

• Does statistical estimation of #active flows

• Drop probability is: f (instantaneous Q size, #active flows)

• Stabilizes buffer occupation at a level independent of the number of active flows

• Identifies misbehaving flows

SRED Approach

• Hit(t) = 0 if no hit; 1 if hit• Hit frequency P at an instant t is defined as:

P(t) = (1 – α)*P(t – 1) + α * Hit(t), 0 < α < 1• α ~ p/M, p is overwrite prob, M is flow table size

• psred(q) = pmax if B/3 <= q < B,

= pmax/4 if B/6 <= q < B/3,

= 0 if 0 <= q < B/6

• pzap = psred(q) * min (1, 1/(256 * P(t))2)

FRED: Flow RED

• Uses per-active-flow accounting

• Each flow’s loss rate depends on the flow’s buffer use

• Protects adaptive flows

• Isolates non-adaptive greedy flows

• Uses the RED approach with modifications

FRED Approach

• Maintain a flow-state table, one entry per active flow with fields: qleni and strikei

• Parameters: minq, maxq, avgcq• Identify non-adaptive flows as:

– qleni >= maxq

– avg >= maxth & qleni > 2 * avgcq– qleni >= avgcq & strike > 1

• Drop only from robust flows: qleni > minq

My Conclusions

• SRED’s performance depends on the correctness of its statistical estimations

• SRED’s equations use parameters that require appropriate tuning to achieve results

• FRED is very effective in achieving all its goals

• FRED’s reliance on per-flow-state prevents it from getting a wide acceptance