path Chirp SpatioTemporal Available Bandwidth Estimation Vinay Ribeiro

path. Chirp Spatio-Temporal Available Bandwidth Estimation Vinay Ribeiro Rolf Riedi, Richard Baraniuk Rice University

A Bird’s Eye View of the Internet • Data transmitted as packets • Multiple routers on end-to-end path • Routers queue bursts of packets

Edge-based Probing • Internet provides connectivity • Lack of optimization • Difficult to obtain information from routers Solution: inject probe packets to measure internal properties

Questions awaiting Answers • What does the Internet topology look like? • Where does congestion occur and why? • Given several mirror sites to download data which one to choose? • Is my ISP honoring the service-level agreement?

Available Bandwidth Estimation

Network Model • End-to-end paths – Multi-hop – No packet reordering • Router queues – FIFO – Constant service rate Packet delay = constant term (propagation, service time) + variable term (queuing delay)

Available Bandwidth • Unused capacity along path Available bandwidth: • Goal: use end-to-end probing to estimate available bandwidth

Applications • Server selection • Route selection (e. g. BGP) • Network monitoring • SLA verification • Congestion control

Available Bandwidth Probing Tool Requirements • Fast estimate within few RTTs • Unobtrusive introduce light probing load • Accurate • No topology information (e. g. link speeds) • Robust to multiple congested links

Principle of Self-Induced Congestion Probing rate < available bw no delay increase Probing rate > available bw delay increases • Advantages – No topology information required – Robust to multiple bottlenecks • TCP-Vegas uses self-induced congestion principle

Trains of Packet-Pairs (TOPP) [Melander et al] • Shortcoming: packet-pairs do • not capture temporal Vary sender packet-pair spacing queuing behavior for packet-pair spacing • Compute avg. useful receiver • Constrained regression based estimate available bandwidth estimation Packet-pairs Packet train

Pathload [Jain & Dovrolis] • CBR packet trains • Vary rate of successive trains • Converge to available bandwidth • Shortcoming Efficiency: only one data rate per train

Chirp Packet Trains • Exponentially decrease packet spacing within packet train • Wide range of probing rates • Efficient: few packets

Chirps vs. Packet-Pairs • Each chirp train of N packets contains N-1 packet pairs at different spacings • Reduces load by 50% – Chirps: N-1 packet spacings, N packets – Packet-pairs: N-1 packet spacings, 2 N-2 packets • Captures temporal queuing behavior

Chirps vs. CBR Trains • Multiple rates in each chirping train – Allows one estimate per-chirp – Potentially more efficient estimation

CBR Cross-Traffic Scenario • Point of onset of increase in queuing delay gives available bandwidth

Bursty Cross-Traffic Scenario • Goal: exploit information in queuing delay signature

Path. Chirp Methodology I. II. Per-packet pair available bandwidth, (k=packet number) Per-chirp available bandwidth III. Smooth per-chirp estimate over sliding time window of size

Self-Induced Congestion Heuristic • Definitions: delay of packet k inst rate at packet k

Excursions • Must take care while using self-induced congestion principle • Segment signature into excursions from x-axis • Valid excursions are those consisting of at least “L” packets • Apply only to valid excursions

Setting Per-Packet Pair Available Bandwidth • Valid excursion • increasing Last Invalid excursion decreasing queuing delay excursions queuing delay

path. Chirp Tool • UDP probe packets • No clock synchronization required, only uses relative queuing delay within a chirp duration • Computation at receiver • Context switching detection • User specified average probing rate • open source distribution at spin. rice. edu

Performance with Varying Parameters • Vary probe size, spread factor • Probing load const. • Mean squared error (MSE) of estimates Result: MSE decreases with increasing probe size, decreasing spread factor

Multi-hop Experiments • First queue is bottleneck • Compare – No cross-traffic at queue 2 – With cross-traffic at queue 2 • Result: MSE close in both scenarios

Internet Experiments • 3 common hops between SLAC Rice and Chicago Rice paths • Estimates fall in proportion to introduced Poisson traffic

Comparison with TOPP • Equal avg. probing rates for path. Chirp and TOPP • Result: path. Chirp outperforms TOPP 30% utilization 70% utilization

Comparison with Pathload • 100 Mbps links • path. Chirp uses 10 times fewer bytes for comparable accuracy Available Efficiency bandwidth pathchirp pathload Accuracy path. Chirp 10 -90% pathload Avg. min-max 30 Mbps 0. 35 MB 3. 9 MB 19 -29 Mbps 16 -31 Mbps 50 Mbps 0. 75 MB 5. 6 MB 39 -48 Mbps 39 -52 Mbps 70 Mbps 0. 6 MB 8. 6 MB 54 -63 Mbps 63 -74 Mbps

Tight Link Localization

Key Definitions Path available bandwidth Sub-path available bandwidth Tight link: link with least available bandwidth • Goal: use end-to-end probing to locate tight link in space and over time

Applications • Science: where do Internet tight links occur and why? • Network aware applications - server selection • Network monitoring - locating hot spots

Methodology • Estimate A[1, m] • For m>tight link, A[1, m] remains constant

Principle of Self-Induced Congestion • Probing rate = R, path available bandwidth = A R < A no delay increase R > A delay increases • Advantages – No topology information required – Robust to multiple bottlenecks

Packet Tailgating • Large packets of size P (TTL=m) small packets of size p • Large packets exit at hop m • Small packets reach receiver with timing information • Previously employed in capacity estimation

Estimating A[1, m] • Key: Probing rate decreases by p/(p+P) at link m • Assumption: r<A[m+1, N], no delay change after link m R < A[1, m] no delay increase R > A[1, m] delay increases

Tight Link Localization • Tight link: link after which A[1, m] remains constant • Applicable to any self-induced congestion tool: pathload, path. Chirp, IGI, netest etc.

estimate tight link ns-2 Simulation • Heterogeneous sources • Tight link location changes over time • path. Chirp tracks tight link location change accurately

Internet Experiment SLAC Rice tight link UIUC Rice tight link • Two paths: UIUC Rice and SLAC Rice • Paths share 4 common links • Same tight link estimate for both paths

Comparison with MRTG Data SLAC Rice UIUC Rice • A[1, m] decreases as expected • Tight link location differs from MRTG data by 1 hop

High Speed Probing • System I/O limits probing rate • On high speed networks: cannot estimate A using self-induced congestion

Receiver System I/O Limitation • Treat receiver I/O bus as an extra link • Use packet tailgating • If then we can estimate A[1, N-1]

Sender System I/O Limitations • Combine sources to increase net probing rate • Issue: machine synchronization

Conclusions • Towards spatio-temporal available bandwidth estimation • Combine self-induced congestion and packet tailgating • Available bandwidth and tight link localization in space and over time • ns-2 and Internet experiments encouraging • Solutions to system I/O bandwidth limitations spin. rice. edu