Endpoint Admission Control WebTP Presentation 9/26/00 Presented by Ye Xia Reference: L. Breslau, E. W. Knightly, S. Shenkar, I. Stoica, H. Zhang, “Endpoint Admission Control: Architectural Issues And Performance”. Sigcomm 2001.

Why Endpoint? Aim of admission control (AC): provide QOS to real-time flows IntServ has per-flow and router-based AC; requires hop-by-hop signalling (RSVP); each router keeps per-flow state; scalability problem. DiffServ lacks AC; providing QOS to each flow is not a primary concern; but more scalable. Hope: endpoint AC can combine the strength of both.

Algorithm Admission decision based on loss only Probing phase: each flow (at the end host) probes the network for loss or marking ratio (say, for 5 seconds) If the ratio is below a threshold, , flow is admitted. Loss model:

Router scheduling mechanisms Fair Queueing has “stolen bandwidth” problem. Example: suppose two types of flows; r2 > r1; and  = 0. Type 1 flow is admitted if r 1 (n 1 +n 2 ) < C; type 2 flow is admitted if r 1 n 1 + r 2 n 2 < C. When r 1 (n 1 +n 2 ) = C, type 1 flows experience no loss; type 2 flows’ loss ratio is (r 2 – r 1 )/ r 2

Best-Effort (TCP) Traffic Need to isolate TCP traffic and AC traffic. Consider what happens when –TCP traffic source is idle –TCP induces loss

Architecture Choice Priority queues –High priority for AC traffic –Low priority for TCP traffic –Probe traffic may take intermediate priority –FIFO queueing for AC traffic AC traffic is rate-limited and served at that rate. –non-work conserving scheduler

Probing Algorithms Difficulty in sampling loss/mark ratio Out-of-band probing –probing traffic takes lower priority than regular data traffic –Probing traffic has higher loss ECN marking: –marking rate higher than dropping rate –Router simulates a virtual queue drained at 90% capacity Problem: cannot relate specified threshold, , with actual loss ratio

Slow-Start Probing Thrashing: when many flows waiting for admission, probing traffic overloads the link. Cause: flow of rate r probes at rate r. Solution: slow-start probing. Gradually ramp up rate of probing traffic.

Thrashing Utilization collapses for both in-band and out-band probing For in-band probing, data loss ratio increases as well

Simulation Models Leaky-bucket constrained traffic sources –On-off sources and movie traces Poisson arrival of flows; exponential holding time with mean 300s. Interfering TCP traffic needs not to be simulated.  = 0,.01,.02,.03,.04,.05,.1,.15,.2. Comparison with router-based AC.

Traffic Sources

Basic Scenario Offered load: 20% blocking prob. Loss rate competitive with MBAC  is meaningful only for in-band drop. Other probing algo. reduce utilization. For in-band drop, 0.4% loss rate when  = 0. For out-band marking, low loss ratio can be achieve after probing for 5 seconds.

Longer Probing Time In-band dropping Lower loss ratio and lower utilization

High Load – In-band Dropping 400% offered load; 75% blocking prob. High loss Slow-start probing does better

High Load – Out-band Probing All algorithms are similar Probing traffic does not cause extra loss to data traffic Slow-start probing has higher utilization and loss ratio

High Load - Marking

Heterogeneous Traffic Large flow has 4 times the peak rate and higher blocking probability MBAC has similar behavoir

Multi-hop Loss Probability

Multi-hop – Blocking Probability

Sharing FIFO Queue with TCP Two lower curves are for  = 0.04 and 0.05 TCP prevents AC traffic to be admitted

Comments Quick conclusion on queueing/scheduling –Reconcile scheduling with end-to-end measurement Probing time is long. –can aggregate probing traffic –What to probe? AC criteria needs to be expanded (not just loss)  has no relationship with actual loss ratio WebTP has similar setup and similar issues.