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1 Capacity Dimensioning Based on Traffic Measurement in the Internet Kazumine Osaka University Shingo Ata (Osaka City Univ.)

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Presentation on theme: "1 Capacity Dimensioning Based on Traffic Measurement in the Internet Kazumine Osaka University Shingo Ata (Osaka City Univ.)"— Presentation transcript:

1 1 Capacity Dimensioning Based on Traffic Measurement in the Internet Kazumine Matoba(k-matoba@jp.fujitsu.com) Osaka University Shingo Ata (Osaka City Univ.) Masayuki Murata (Osaka Univ.) Now in Fujitsu Laboratories Kazumine Matoba(k-matoba@jp.fujitsu.com) Osaka University Shingo Ata (Osaka City Univ.) Masayuki Murata (Osaka Univ.) Now in Fujitsu Laboratories * *

2 2 Measuring Networks ISPs need to offer stable QoS (Quality of Service) for redesigning current ISP’s network, end-to-end network characteristics is needed propose network planning framework based on current end-to-end network characteristics propose network planning framework based on current end-to-end network characteristics Important QoS metric is measured only by end users e.g. Web document download time measurement from end user

3 3 Capacity Dimensioning Active Traffic Measurement Analysis of the measurement result Internet User Server Bottleneck Link 1. find the bottleneck area between end hosts 2. measure the bottleneck link utilization 3. propose the design framework for determining link capacity Network Capacity Dimensioning

4 4 Contents Capacity Dimensioning 1. Measurement Based Identification Method of Performance Bottleneck 2. Measurement Method of Bottleneck Link Utilization 3. Capacity Dimensioning Based on Traffic Measurement Conclusion

5 5 Bottleneck Classification Three kinds of bottlenecks in the end-to-end communication Sender Side Configuration collect information about TCP communication User Target Path Server Receiver Side Configuration (Buffer size) Network Condition (Congestion)

6 6 How to Detect Each Bottleneck? TCP throughput (estimated value)* take no thought of sender side configuration * J.Padhye, V.Firoiu, D.Toesley.,and J.Kurose, “Modeling TCP throughput: A simple model and its empirical validation”, Proceedings of ACM SIGCOMM’98, pp.303-314, September 1998. expected window size of TCP connection actual throughput << estimated throughput ① sender side configuration ② check buffer size (receiver side) b : duplicate ACK number p : packet loss rate ③ check network congestion (network condition) congested router queue and/or drop packets large RTT and high packet loss rate * bottleneck is sender! buffer size is insufficient! network is congested!

7 7 - buffer size is insufficient - when buffer size is increased, TCP throughput becomes high Bottleneck Classification Process measure RTT, Packet Loss Rate, and TCP throughput check socket buffer size of receiver host repeat measurement, changing socket buffer size - sufficient size of buffer - high packet loss rate - large RTT - no relationship between buffer size and throughput - actual throughput << estimated throughput ① sender side configuration ② buffer size (receiver side) ③ network congestion (network condition)

8 8 Server 2Server 1 Classification Experimens in the Internet Estimated throughput Actual throughput No special feature buffer increase ⇒ Throughput increase Larger than Actual throughput Buffer size is insufficient Network ConditionReceiver SideSender Side Packet Loss Rate Round Trip Time Large RTT High Packet Loss Rate For each bottleneck, their characteristics can be found For each bottleneck, their characteristics can be found Server 3

9 9 Contents Capacity Dimensioning 1. Measurement Based Identification Method of Performance Bottleneck 2. Measurement Method of Bottleneck Link Utilization 3. Capacity Dimensioning Based on Traffic Measurement Conclusion

10 10 Estimate Link Utilization measure and estimate capacity of bottleneck link Pathchar, Pchar, Clink, etc. c c r r b b r r b b u u c c   estimate bottleneck link utilization measure the amount of cross traffic passing through the bottleneck link

11 11 need these items! Estimate Throughput of Cross Traffic assume probe packets are queued at router n cross traffic c r probe packet Bottleneck Router n Router n+1 Bottleneck Link n+1 l n l sb lb llr nnc   1 the amount of cross traffic s :size of probe packet b :capacity of link n+1 throughput of cross traffic n n c l sb l r    1 client host send ICMP ECHO packet continuously router n and n+1 return reply packets observe returned packet and calculate interval of arrival time client host send ICMP ECHO packet continuously router n and n+1 return reply packets observe returned packet and calculate interval of arrival time n n l l n+1 l l

12 12 Estimation Results of Link Utilization measure bottleneck link between two universities Estimation error is under 10% in link utilization link utilization (cross traffic) estimation 15.9% (0.95 Mbps)22.8% 27.9% (1.7 Mbps)27.8% 38.5% (2.3 Mbps)32.1% 69.5% (4.1 Mbps)60.3% 77.6% (4.6Mbps)81.6% Bottleneck link 6Mbps Osaka City Univ. Osaka Univ. Measurement Host Router n Router n+1 Target Host

13 13 Contents Capacity Dimensioning 1. Measurement Based Identification Method of Performance Bottleneck 2. Measurement Method of Bottleneck Link Utilization 3. Capacity Dimensioning Based on Traffic Measurement Conclusion

14 14 Capacity Dimensioning from user’s viewpoint fulfill a demand of user Designing network for each bottleneck 1. sender or receiver side configuration 2. network configuration (link bandwidth) based on end-to-end network characteristics propose a design framework

15 15 Capacity Dimensioning 1: Bottleneck Resides in the End Host Sender side configuration remove the cause of bottleneck Ex. configure rate shaping, upgrade hardwares, etc. Receiver side configuration (buffer size) increase buffer size to f f RTT t t f f ' ' ' '   t t ' ' : throughput which user needs : Round Trip Time RTT ' ' Bottleneck may shift to another area need to upgrade link capacity?

16 16 Designing Link Capacity: Bottleneck Resides in the End Host most thin available bandwidth link time cross traffic user’s traffic C C t t C C A A       t t C C ) ) 1 1 ( (       link utilization t t   t t t t A A C C         ' ' lower bound of new link capacity lower bound of new link capacity link capacity determine new link capacity C : link capacity A : throughput of cross traffic t : throughput of user ’ s traffic

17 17 user traffic both of the user and cross traffic Capacity Dimensioning 2: Bottleneck Resides in Network time send data time cross traffic user’s traffic t time cross traffic time cross traffic user’s traffic send data cross traffic A link capacity same as the former capacity decision t t AC     ' predict amount of future cross traffic upgrade cross traffic may increase

18 18 Designing Link Capacity: Bottleneck Resides in Network assume and simplify the situation bottleneck router is M/M/1 queueing system current link capacity C will increase to C’ =a C choose following  user traffic will increase to t’ a a T T bp t t T T     2 2 3 3 ' ' 1 1   T : packet service time at bottleneck router b : the number of arrival packets notified one ACK p : packet loss rate T a : constant (=RTT-T) T : packet service time at bottleneck router b : the number of arrival packets notified one ACK p : packet loss rate T a : constant (=RTT-T)

19 19 Conclusion Capacity dimensioning based on traffic measurement in the Internet Identification method of performance bottleneck classify into 3 kinds of bottlenecks Measurement method of link utilization focused on the bottleneck link Capacity dimensioning Modeling the router as an M/M/1 queueing system

20 20 The End

21 21 Available Bandwidth Existing Method to Estimate Link Utilization Bottleneck link packet pair packet stream probe packet affects measurement result but not take into consideration only focus on the end-to-end performance User Server - capacity of bottleneck link - available bandwidth in end- to-end communication


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