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End-2-End QoS Internet Presented by: Zvi Rosberg 3 Dec, 2007 Caltech Seminar www.ict.csiro.au What is this talk about www.ict.csiro.au The shortcoming of QoS support in current Internet A novel holistic Rate Management Protocol A new scalable QoS guarantee architecture The theoretical foundation of our architecture How TCP window flow control may adapt in the presence of our network layer RMP Another E-2-E prioritized Delay/Loss RMP Motivation www.ict.csiro.au Shortcoming of current QoS architecture Beside being immature and requiring horrendous configuration, current QoS also has… Fundamental inhibitors: 1. Scalability for real QoS guarantee (IntServ and Cisco’s “IntServ over DiffServ”) 2. No bandwidth nor E2E delay guarantee when using a scalable configuration of DiffServ So what are we doing about it ? www.ict.csiro.au We are implementing a prototype on Network Processors (NPU) addressing the current QoS issues - The architecture is 1. Scalable and has bandwidth, loss and E2E delay guarantee 2. Adaptive - so configuration is minimized 3. Allocates the residual bandwidth fairly The NPUs execute a new IP layer protocol that router’s should run in the future www.ict.csiro.au The Architecture The Key Elements of our solution www.ict.csiro.au 53 Provides Services to Management functions in the Edge Routers Ethernet RMP Core Router 11 51 2 User Devices Ethernet 1 Novel Rate Management Protocol (RMP) for Multi-Service Flows RMP RMP 4 3 Core Router 13 RMP Core Router 14 3 Services Edge Router 20 RMP Core Router 15 RMP Ethernet User 52 Devices Edge Router 30 RMP Core Router 12 Edge Router 10 Services Runs in Edge & Core Routers at IP layer User Devices RMP Services Architectural Components Control Data Plane Scalable Bandwidth Reservation Protocol QoS Fair Rate Calculation www.ict.csiro.au Classification/Marking at Edge Routers Rate Policing in the Edge Admission Control Priority Packet Scheduling in Routers RMP Link Penalties Gathering Performance Probing www.ict.csiro.au Theoretical Foundation Our Theoretical Contribution www.ict.csiro.au Extending Fairness beyond “best-effort” service Extending the primal-dual iterative distributed algorithm (used by Kelly) for rate allocation with 1. Rate and delay constraints 2. Priority packet scheduling Revisit TCP flow control when rate is controlled by the network layer An aside question is: Why priority scheduling? It improves link utilization – delay-sensitive packets will not have to wait for delay-insensitive packets, so we can have more from the delay-insensitive packets Fairness with Best-effort www.ict.csiro.au - proportional fairness is equivalent to the solution of: as long as X is convex Fairness with QoS www.ict.csiro.au A natural way to extend the best effort fairness is to add the QoS requirements to the constraints and … … optimize on the residual link capacities Fairness with QoS (Cont.) www.ict.csiro.au Flow rates of prio 1,2…,m traversing each link minimum bandwidth constraints maximum loss and delay constraints Since X is convex – proportional fairness follows Fairness with QoS (Cont.) www.ict.csiro.au The delay/loss constraints are NOT EXPLICIT – they are attained by an outer-loop control of Primal-dual iterative distributed algorithm extension www.ict.csiro.au The fair residual rates, , are computed iteratively after a reduction to residual link capacities, , given by … which is made possible by our scalable reservation protocol The policed rate of flow is then The Rate Management Protocol (RMP) www.ict.csiro.au • Route penalty of flow i • Link capacity reduced by utilization upper • Total rate of flows from priorities 1,..,m on link n bound per priority class m on unreserved link capacity • Adaptively set from sources based on RTT and Loss probing • In each router output link n and priority m : Stability Proof To www.ict.csiro.au prove stability with fixed We redefine the routing matrix, , to include one virtual link for each priority class Flows with priority m use all virtual links having priorities m along their original path The redefined problem is a single class problem equivalent to the priority problem After this reduction, stability follows by Kelly’s results Stability Proof (cont.) To www.ict.csiro.au prove stability with adaptive “Unhappy” flow sources (having excessive delay/losses) signal it in their RMP packets Congested To links decrease the respective prove convergence, we allow to decrease In only practice, convergence is observed also when are also increased when flow sources are “too-happy” www.ict.csiro.au TCP Flow Control - Revisited TCP Flow Control Revaluation www.ict.csiro.au Once RMP is in place, TCP flow control needs a revaluation The RMP of the core network will take care of fair rate calculation and congestion avoidance RMP will also signal end applications about their current target rates, and then… TCP could be extended beyond “best-effort” Given rate, , TCP can achieve it with a window update of the form: Performance Evaluation www.ict.csiro.au We showed that assuming linear scalability, the window flow control converges to a unique stable state under totally asynchronous updates linear scalability: Total number of bytes queued in each link scales up linearly with the window size It is an average flow property of the flows crossing a given link, rather a per-flow property Plausible for large networks Stability was also verified by simulation In the fluid model of [Mo & Walrand] used to relate rate and windows, linear scalability is implied TCP Flow Control Comparison Epoch ISP Network, USA # core links: 74 (37 full-duplex) # flows: 512 # access links: 512 core link capa: 1 Gb/s access link capa: 0.1 Gb/s www.ict.csiro.au Simulation Method www.ict.csiro.au 2-way TCP flows using fixed shortest paths ACKs are either piggybacked or pure (statistically) RTO is estimated according to RFC 2988 (Jacobson Alg) Duplicate ACKs are triggered if All TCP flow controls half their window size upon 3duplicate ACKs and reduce it to 2 MSS upon RTO Otherwise - Fast TCP adapts its window sizes according Simulation Method (cont.) www.ict.csiro.au Simulation time is about 3.5 real operational minutes In every step - window packets are processed in one batch First, they are arbitrarily distributed between forward and backward paths Then, the packets that can “fill” the links are in transit The rest, are distributed between the bottleneck links in proportion to the bottleneck queueing time Async operation is modelled by i.i.d Bernoulli r.v's determining which of the flows receive an ACK TCP Flow Control Comparison Our TCP Flow Control (9 typical flows windows) www.ict.csiro.au TCP Flow Control Comparison Fast TCP Flow Control www.ict.csiro.au TCP Flow Control Comparison TCP Vegas Flow Control www.ict.csiro.au TCP Flow Control Comparison TCP Reno Flow Control (“Sawtooth”) www.ict.csiro.au Comparison Summary Avg www.ict.csiro.au Avg RTT Avg Win Fairness Dev Max Fair Dev Rate Ours 492 P 191 ms 28 P 3% 20% Fast 479 P 231 ms 28 P 5% 25% Vegas 449 P 248 ms 29 P 4% 44% Reno 451 P 548 ms 59 P 12% 91% Flow Control with QoS Support www.ict.csiro.au 3 x 256 2-way TCP connections with 3 priorities Utilization upper bounds: (0.1, 0.75, 1.0) Avg total fair rate: 164.30 packets (compared with 492) Avg Fairness deviation: 5.5% Avg Rate Avg RTT Avg Win Priority 1 43.8 P 50 ms 1P Priority 2 224 P 56 ms 5.12 P Priority 3 225 P 81 ms 7P Simulation with Link Utilization Adaptation www.ict.csiro.au When are adapted based on flow source experienced RTT and Losses (i.e., RTT > RTO), then all QoS requirements are met www.ict.csiro.au Another E2E Delay-Loss Control Rate Time Derivative in the Fluid Model www.ict.csiro.au We study the following prioritized combined Rate-Delay control problem clearance time of bits from flows with prio higher/equal p in link l at time t delay prices for flow i at time t Delay Time Derivative in the Fluid Model www.ict.csiro.au total rate of flows with priorities less/equal p in link l at time t The rate control is the gradient search of Delay Prices Adapting www.ict.csiro.au is learned by the flow source from the RMP packets … and is adapted if Adaptation signals must also be disseminated to other relevant sources …. which is done again with RMP signalling packets Result Summary If www.ict.csiro.au the routing matrix is full-rank, then Synchronous Fluid Model For any e2e delay requirement, there is a unique equilibrium point The adaptive rate control converges to the stable point from any initial condition Time Lag Fluid Model (Rate and Delay effects) For a single bottleneck case – global stability holds true only if time lag is limited (e.g., ~650 ms) Emulation – holds true for multiple bottlenecks www.ict.csiro.au Thank You