Predictive Bandwidth Control for GEO Satellite Networks

Outline Predictive Bandwidth Control for GEO Satellite Networks L. Chisci 1 R. Fantacci 2 T. Pecorella 3 1 Dpt. di Sistemi e Informatica Università di Firenze, Italy. 2 Dpt. di Elettronica e Telecomunicazioni Università di Firenze, Italy. 3 CNIT - Unità di Firenze, Italy. e-mail: chisci@dsi.unifi.it, {fantacci,pecos}@lenst.det.unifi.it The IEEE Int. Conf. on Communications, Paris, 20-24 June 2004

Outline Outline 1 Satellite Systems 2 3 DBA Scheme Satellite Gateway (SG) 4 5

Satellite Networks Pros Easy deployment Cost-effective Mobility Broadcasting Geographical coverage Cons In GEO links propagation delay is very high (RTT 500 ms). High delay*bandwidth systems are critical for TCP flows. The bandwidth is scarce, so a careful management is needed.

QoS metrics Bandwidth Delay Jitter Packet Loss Application Required Sensitivity to Band Delay Jitter Loss VoIP Low High High Avg. Streaming video High Avg. Avg. Avg. Web Avg. Avg. Low High E-mail Low Low Low High Internet Engineering Task Force (IETF) proposals Integrated Services (IntServ) Differentiated Services (DiffServ)

Bandwidth request algorithm Dynamic Bandwidth Allocation Scheme Satellite Gateway (SG) Satellite Gateway w(t) Adaptive Traffic Predictor y(t) FIFO Buffer wˆ (t+ k t) - v(t) Bandwidth Controller b i Scheduler u(t) to the NCC v(t) from the NCC In order to compute the bandwidth requests, each SGs use 2 blocks: Traffic Predictor Bandwidth Controller

Buffers (FIFO queues) Dynamic Bandwidth Allocation Scheme Satellite Gateway (SG) Input packets are divided into n buffers (FIFO queues), according to the DiffServ policy. (e.g. EF, AF, BE classes = n = 3) y(t) IR n w(t) IR n v(t) IR n vector of queue sizes vector of input flows vector of output flows at sample time t y(t + 1) = y(t) + w(t) v(t)

Scheduler Satellite Systems Dynamic Bandwidth Allocation Scheme Satellite Gateway (SG) v(t): bandwidth assigned at time t to the SG from the NCC v(t) IR n : transmission flows at time t for the n service classes v(t) = b 1 b 2. b n v(t), b i 0, Several scheduling policies can be adopted, e.g. WFQ - Weighted Fair Queueing Priority Scheduling Weighted Round Robin n b i 1 i=1

Traffic Predictor Satellite Systems Dynamic Bandwidth Allocation Scheme Satellite Gateway (SG) ŵ(t + k t): predictions, at time t, of the input traffic flows at time t + k, based on the available data w t 1 = {w(k), k t 1}. Several predictors can be used: AR predictor (not self-similar) ARMA predictor (not self-similar) FARIMA predictor (self-similar) based on a fractionally integrated ARMA model Non parametric statistical predictors

Bandwidth Controller Dynamic Bandwidth Allocation Scheme Satellite Gateway (SG) The bandwidth request u(t) for the time t + δ, where δ is the Round Trip Delay, is based on the data already known at time t and assuming that the request is always satisfied, i.e. v(t) = u(t δ) state: x(t) = [ y (t), u(t 1),..., u(t δ) ] IR n+δ Predictive model for time evolution of queue sizes: { x(t + k + 1) = Ax(t + k) + Bu(t + k) + Ew(t + k) y(t + k) = Cx(t + k) Predictions ŵ(t + k t) are used in place of w(t + k), k 0.

Bandwidth Controller (cont.) Dynamic Bandwidth Allocation Scheme Satellite Gateway (SG) u(t) is chosen to trade-off small queue sizes (i.e. low traffic delays) vs. bandwidth waste = Cost minimization of J = H 1 k=0 [ y (t + δ + k) Q y(t + δ + k) + r u 2 (t + k) ] Q = Q 0, r > 0 subject to the constraints: { y min y(t + δ + k) y max u min u(t + k) u max k = 0, 1,..., H 1

Receding Horizon control strategy Dynamic Bandwidth Allocation Scheme Satellite Gateway (SG) Receding Horizon control is used to compensate the model uncertainties. Being u(t),..., u(t + H 1) the optimal solution at time t we apply the request u(t) discarding u(t + 1),... at time t + 1 we repeat the process with the new data.

Other strategies Satellite Systems Dynamic Bandwidth Allocation Scheme Satellite Gateway (SG) Fixed allocation Med: 110% of the average input bandwidth Max: 80% of the maximum input bandwidth Reactive controller [F. Delli Priscoli and A. Pietrabissa, CDC 2002] The bandwidth request is computed using a Smith Predictor: u(t) = w(t) + K [ y(t) t 1 k=t δ ( u(k) ) ) (1 ] δ w(k) δ where δ δ is a parameter representing the desirable queueing delay while K is a gain which, for stability, must belong to the interval (0, 1].

Simulation and Control parameters Round Trip Delay (RTD) 500 [ms] Number of input flows 3 (EF, AF, BE) Average bandwidth 1 (w 1 ) 14.124 [kbit/frame] Average bandwidth 2 (w 2 ) 6.693 [kbit/frame] Average bandwidth 3 (w 3 ) 7.563 [kbit/frame] q 1 q 2 q 3 0.1786 0.1428 1.0 r 1.0 sampling period 125.0 [ms] δ = sampling period/rtd 4 prediction horizon H 4 order of AR model h 4 y min, y max 0, [bit] u min, u max 0, 2 [Mbit/frame]

Bandwidth waste Cumulative Density Func. (CDF) 1 0.8 0.6 0.4 0.2 RHC Med 0 Max 0 5 10 15 20 25 30 35 40 Band Waste [kbit/frame] Comparable bandwidth waste between Med, RHC and SPC (not depicted) Max policy exhibits huge wastes

Queue length Cumulative Density Func. (CDF) 1 0.8 0.6 0.4 0.2 RHC q 3 = 1.00 RHC q 3 = 0.57 RHC q 3 = 0.28 Med Max SPC 0 0 50 100 150 200 250 300 350 400 Total Queue Length [kbit] RHC outperforms Med and SPC Q parameters allow fine-tuning of RHC performance

Queue length for different QoS classes Cumulative Density Func. (CDF) 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Class 1 (EF) Class 2 (AF) Class 3 (BE) 0 10 20 30 40 50 60 70 80 90 100 Queue Length [kbit] DiffServ QoS is supported by differentiating queue length.

Queue length after a (simulated) congestion Queue Length [kbit] 1200 1000 800 600 400 200 Class 1 (EF) Normal Class 1 (EF) Congested 0 0 50 100 150 200 250 300 350 400 Frame Queue Length [kbit] 1600 1400 1200 1000 800 600 400 200 0 Class 3 (BE) Normal Class 3 (BE) Congested 0 50 100 150 200 250 300 350 400 Frame Queue Length [kbit] 1200 1000 800 600 400 200 Class 2 (AF) Normal Class 2 (AF) Congested 0 0 50 100 150 200 250 300 350 400 Frame 1 Mbit initial values of queue lengths queues are served according to the QoS priority bandwidth is released only after the congestion recovery

I DBA for GEO satellite systems based on adaptive predictor for the input traffic flow formulation of DBA as an optimal control problem with cost trading off queue size vs. bandwidth waste receding-horizon strategy for bandwidth request generation Interesting results in terms of handling multiple services with differentiated QoS requirements low bandwidth waste high QoS (low delays and packet loss probability)

II Future developments adoption of more realistic self-similar models (e.g., FARIMA) for traffic prediction distributed DBA schemes taking into account the presence of multiple SGs competing for the bandwidth resource.