Flow and Congestion Control for Reliable Multicast Communication In Wide-Area Networks

Flow and Congestion Control for Reliable Multicast CommunicationIn Wide-Area Networks A Doctoral Dissertation By Supratik Bhattacharyya

Talk Overview • General Problem • Thesis Contributions • Congestion Control for Single Multicast Group • Efficient Flow Control Using Multiple Multicast Groups • Summary and Future Research Directions

One-to-many reliable multicasting Transport-level techniques for congestion control flow control Focus Of Thesis Source Router R4 R1 R3 R2

Challenges - many rcvrs, many network paths : Heterogeneity links, receiver capabilities Scale feedback implosion Fairness how to share bandwidth with unicast Multicast Flow/Congestion Control : a hard problem Source R1 R3 R2 R4 : end-to-end feedback

Thesis Contributions • Source-based Congestion Control : • identified and analyzed the Loss Path Multiplicity problem • identified a fair and scalable approach • formulated an axiomatic approach towards multicast congestion control • developed novel technique for responding to packet loss indications • designed a TCP-friendly protocol (NCA) for an active services architecture

Thesis Contributions • Flow-control: • developed bulk data transfer approach using multiple multicast groups. • proposed and evaluated algorithms for determining transmission rate of each multicast group.

Challenge : How to aggregate feedback into single rate control decision Congestion signals (CS): filtered versions of loss indications (LI)  : congestion signal probability filters can be distributed Feedback Aggregation congestion signal (CS) loss indications (LI) rate change Rate control algorithm filter 

Copies of same packet lost on many network paths Set of receivers treated as single aggregate receiver Example : n : no. of receivers p : loss prob. on link to each rcvr. : congestion signal probability LI LI R3 R1 Problem : Loss Path Multiplicity (LPM)  ?   1 as n  R2

. . . How Severe is the LPM Problem? Example : end-to-end loss prob. = p=0.05 • Severe degradationin throughput with - • no. of receivers • independent losses f : fraction of end-to-end loss on independent link

Feedback Aggregation/Filtering :Related Work • Restrict response to one LI per time interval T • Montgomery 1997 • Restrict response to subset of receivers : • choose K rcvrs out of N asrepresentatives • Delucia et al. 1997 • Reduce response to each LI : • Golestani, Bhattacharyya 1998, Delucia et al. 1997 Q :How much bandwidth should a multicast session get?

Challenge : How to achieve “fair” sharing among multicast and unicast sessions Multicast allocation according to “worst” end-to-end path Multicast session shares equally with a unicast session on its “worst” end-to-end path. L2 L1 “Fair” Bandwidth Sharing Ucast1 Ucast2 Mcast L2 L1 - 1 Mbps, L2 - 2 Mbps

Background : End-to-end Rate Control Algorithms : rate after i-th update • Additive increase, multiplicative decrease : on congestion signal : else, per T : • We derive average session throughput B

Solution to LPM Problem : Our Approach • Worst Estimate-based Tracking (WET) : • Identify (estimate) most congested/ ”worst” receiver • Respond to LIs from only “worst” receiver • Simulations show that WET • prevents throttling of multicast transmission rate • allows fair bandwidth sharing

WET is one way of designing a Loss Indication Filter (LIF) Qn : Given our fairness goal, can we formulate general rules for LIF design? Architecture for Loss Indication-based Multicast Congestion Control loss indications (LI) rate change congestion signal (CS) Rate control algorithm filter 

N receivers, loss probabilities = unicast bandwidth on path to rcvr i Axiom 1 :IfN=1, then = Axiom 2 : If then Axiom 3 : As Goal : Multicast bandwidth allocation must be worst-path fair Axiomatic Approach for Loss Indication Filter Design . . . 2 1 N

Linear Proportional Response (LPR) • Receiver i periodically reports loss count over W packets ( estimates ) • On LI from receiver i, source reduces rate with probability • Showed that LPR satisfies all three axioms

Related : Random Listening Algorithm (RLA) [Wang98] Analytic Result : LPR provides tighter upper bound on r LPR : RLA : Comparison of LPR and RLA

Summary of Results • LPR “more fair” than RLA for realistic W (~100 packets) • Steady State : • WET is closest to fairness goal • LPR is close to WET • RLA can be extremely unfair • Transient Behavior : • LPR, RLA respond faster to changes in network conditions than WET

Transient Behavior 5 mcast over all links • At t=300 sec, two multicast sessions stop receiving feedback from receivers at the end of L1 L10 L1 L2 10 ucast 5 ucast 5 ucast . . . Loss probability on Link L2

Challenge : reliable delivery of finite volume of data diverse receive-rates Goal : minimize average completiontime Approach : multiple IP multicast groups (channels) Flow-controlled Bulk Data Transfer :Overview R3=3 R1=1 R2=2 R4=4

Q : How to : assign channel rates? assign receivers to channels? partition data among channels? Assumptions : error-free channels known, static receive-rate constraints Solution with unlimited channels : minimizes average completion time minimizes bandwidth Flow-controlled Bulk Data Transfer 2 pkts/sec 4 pkts/sec 1 pkt/sec R2 a R4 R1 b a a b c d R1,R2,R4 r1 = 1 a b d c r2 = 1 d b R2,R4 c d r3 = 2 R4

Q : How to : assign channel rates? assign receivers to channels? partition data among channels? Assumptions : error-free channels known, static receive-rate constraints Solution with unlimited channels : minimizes average completion time minimizes bandwidth c d Flow-controlled Bulk Data Transfer 2 pkts/sec 4 pkts/sec 1 pkt/sec R2 a R4 R1 b a a c b c d R1,R2,R4 r1 = 1 a b d c r2 = 1 d b R2,R4 c d r3 = 2 R4

Q : How to : assign channel rates? assign receivers to channels? partition data among channels? Assumptions : error-free channels known, static receive-rate constraints Solution withunlimited channels : minimizes average completion time minimizes bandwidth c d Flow-controlled Bulk Data Transfer 2 pkts/sec 4 pkts/sec 1 pkt/sec R2 a R4 R1 b a b a c b d c d R1,R2,R4 r1 = 1 a b d c r2 = 1 d b R2,R4 c d r3 = 2 R4

Summary of Results • Developed solution for minimizing average completion time with N receivers and K channels • Developed simple rate assignment algorithms that • scale well to large number of receivers • have close to optimal average completion time • make efficient use of network bandwidth • Showed that small number of multicast groups sufficient for above algorithms

Summary of Contributions • Source-based Congestion Control : • identified and analyzed the Loss Path Multiplicity problem • identified a fair and scalable approach • formulated an axiomatic approach towards multicast congestion control • developed novel technique for responding to packet loss indications • designed a TCP-friendly protocol (NCA) for an active services architecture

Summary of Contributions • Flow-control: • developed bulk data transfer approach using multiple multicast groups. • proposed and evaluated algorithms for determining transmission rate of each multicast group.

Future Research Directions : Congestion Control • WET : • How can the source detect changes in network congestion levels in a timely fashion? • LPR : • Can steady state performance be improved? • Can the NCA protocol be based on LPR instead of WET? • NCA : • implementation details - start-up, nominee changeover, etc.

Future Research Directions :Flow Control • Flow-controlled bulk data transfer : • evaluate performance when sender has imperfect knowledge of receive-rates • explore feasibility of our approach in a practical setting • Synergy with per-group congestion control techniques

Flow and Congestion Control for Reliable Multicast Communication In Wide-Area Networks