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Fairness Issues in RED Gateways

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.

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Fairness Issues in RED Gateways

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  1. Fairness Issues in RED Gateways Srinivas R. Avasarala sra@cs.purdue.edu CS Dept., Purdue University

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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)

  7. 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

  8. 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

  9. 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

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