Design and Implementation of Measurement-Based Resource Allocation Schemes Using the Realtime Traffic Flow Measurement Architecture

Transcription

1 Design and Implementation of Measurement-Based Resource Allocation Schemes Using the Realtime Traffic Flow Measurement Architecture Robert D. Callaway, Michael Devetsikiotis, and Chao Kan Department of Electrical and Computer Engineering Alcatel Research and Innovation Center North Carolina State University Alcatel USA, Inc. Raleigh, NC Plano, TX June 22, 2004 IEEE International Conference on Communications 2004: Paris, France

2 Presentation Outline Motivation and Background Effective Bandwidth Estimators Overview of Realtime Traffic Flow Measurement Architecture Modifications to Realtime Traffic Flow Measurement Architecture Emulation Setup and Tests Results and Conclusions 1

3 Motivation and Background Goals of Self-Sizing Networks Optimize network utilization while ensuring QoS Ensure QoS of network traffic Adaptively change to network conditions while meeting the above criterion Benefits of Measurement-Based Resource Allocation Not dependent on a priori assumptions Able to track some transient behavior in traffic (non-abrupt changes) 2

4 Our Contribution Review effective bandwidth proposals in literature Implement effective bandwidth algorithms in IETF-standardized environment Verify and validate the implementation Monitor allocations and QoS of traffic to measure algorithm accuracy Demonstrate by emulation that algorithms are implementable in real networks 3

5 Effective Bandwidth Estimators A generic formula for effective bandwidth was proposed by Kelly as: eb(s, t) = 1 st log E [ e sx[0,t]] The s parameter in the general definition cannot be directly estimated from measurements; therefore, the direct application of this formula in an online measurement resource allocation scheme is not practical. Three algorithms were chosen for further analysis because of their computational complexity, performance, and memory requirements. Gaussian Approximation Courcoubetis Approximation Norros Approximation 4

6 Gaussian Approximation Guerin, et. al defined the Gaussian Approximation as: C EB = µ + σ 2 ln ɛ ln 2π where µ is the mean arrival rate of the traffic, σ is the standard deviation of the arriving traffic, and ɛ is the QoS parameter (packet loss probability). Assumes a bufferless link Serves as an upper bound 5

7 Courcoubetis Approximation Courcoubetis, et. al defined the following approximation for effective bandwidth: C EB = µ + IDs 2B where µ is the mean arrival rate of the traffic, ID is the index of dispersion, s is the space parameter, and B is the buffer size of the queue. The index of dispersion is defined as: 1 ID = lim n n E ( n i=1 ) 2 X i The s parameter is calculated from an asymptotically exponential decrease assumption. This approximation does not address long range dependent traffic. 6

8 Norros Approximation Norros defined the following approximation for effective bandwidth: C EB = µ + [ B H 1 κ (H) ] 1 H 2aµ ln ɛ where κ (H) = H H (1 H) 1 H, µ is the mean arrival rate of the traffic, B is the buffer size of the queue, H is the Hurst parameter of the traffic, a is the coefficient of variation of the traffic, and ɛ is the QoS parameter (packet loss probability) of the traffic flow. The coefficient of variation is approximated by the index of dispersion; this approximation is only valid when the arriving traffic is short range dependent. The Norros Approximation is the only formula we considered that uses the Hurst parameter in its calculations; therefore, it is the only formula that takes self-similarity into consideration. It is also the only formula that addresses long range dependent traffic. 7

9 Overview of Realtime Traffic Flow Measurement Architecture The 3 main components within the RTFM architecture are the meter, reader, and manager. The meter serves to collect statistics on network flows that pass through links that are connected to it. The reader retrieves the statistics from the meter at a regular interval via SNMP. 8

10 Implementation within RTFM Architecture We are interested in the number of arriving bytes in a given time period (t slot ) for a particular traffic flow; RFC 2722 provides a byte counting statistic called tooctets. We utilize a sliding window system with a size of N slots in our online implementation. Initialization of Effective Bandwidth Thread Delay for t slot seconds Input tooctets from the last t slot into sliding window system Recompute Mean Recompute Variance Recompute Index of Dispersion Yes Using Courcoubetis or Norros? time_to_realloc=n time_to_realloc-- No time_to_realloc=0 No Change service rate on queue Recompute Effective Bandwidth Yes The mean, variance, and index of dispersion are recalculated after every t slot. Each network flow (or class) is filtered into its own queue, so after N slots, the service rate of the queue is dynamically changed to the measured effective bandwidth. 9

11 Emulation Setup We added the effective bandwidth algorithms into the meter component of the RTFM architecture. We installed NeTraMeT onto several Linux PC s in order to validate and verify the integrity of our environment. Traffic used in the emulation tests was generated using the Sup-FRP method proposed by Byu & Rowen. RTFM meter EB Algorithms Effective Bandwidth carolina SNMP RTFM meter reader / manager ncstate /100 Mbps Switch ingress core C Incoming Traffic C C Outgoing Traffic wolfpack Logical Diagram Network Diagram 10

12 Emulation Cases We present the results from three cases of our emulation tests: Case I: The performance of each algorithm is tested against the same traffic trace. Case II: The scalability of the implementation is tested when multiple flows are sent simultaneously through the measurement architecture. Case III: The ability of the implementation to track abrupt transient behavior in the traffic characteristics (mean arrival rate) 11

13 Emulation Results: Case I x 105 Plot of Traffic Trace vs. Estimated Effective Bandwidths Meter Implementation: 1 Stream Actual Traffic Gaussian Method Courcoubetis Method Norros Method 10 2 Packet Loss Probability vs. Target PLP: 10 3 Meter Implementation: 1 Stream Target PLP Gaussian PLP Courcoubetis PLP Norros PLP Throughput (bytes/sec) Packet Loss Probability Time (sec) Packet Number x 10 4 From these graphs, we can see that each of the EB algorithms can provide the requested QoS while providing significant bandwidth savings over peak-rate allocation. 12

14 Emulation Results: Case II Plot of 3 x Traffic Trace vs. Estimated Effective Bandwidths Gaussian Method Meter Implementation: 3 Streams Actual Traffic Stream 1 Stream 2 Stream Packet Loss Probability vs. Target PLP: 10 3 Gaussian Method Meter Implementation: 3 Stream Target PLP Stream 1 PLP Stream 2 PLP Stream 3 PLP Throughput (bytes/sec) Packet Loss Probability Time (sec) Packet Number x 10 4 These graphs illustrate the robustness of the implementation to track multiple flows simultaneously and still provide the QoS for each flow. 13

15 Emulation Results: Case III x 105 Plot of Traffic Trace vs. Estimated Effective Bandwidths Meter Implementation: 1 Stream Actual Traffic Gaussian Method Courcoubetis Method Norros Method 10 1 Packet Loss Probability vs. Target PLP: 10 3 Meter Implementation: 1 Stream Target PLP Gaussian PLP Courcoubetis PLP Norros PLP 4 Throughput (bytes/sec) Packet Loss Probability Time (sec) Packet Number x 10 5 These graphs show that two of the algorithms are unable to provide the requested QoS when there is an abrupt increase in the mean arrival rate of the traffic. 14

16 Conclusions & Summary of Our Contribution Implemented EB algorithms in open-source implementation of RTFM environment Verified and validated the implementation Showed the robustness and scalability of the system The measurement time scale is relative to the traffic characteristics; therefore, a static t slot value fails to accurately capture the characteristics of non-stationary traffic. Additional work has shown that dynamically changing the length of t slot at the completion of N window slots allows the system to accurately track abrupt changes in the characteristics of the traffic. With a dynamic t slot, QoS constraints can be met even when dramatic changes in the traffic characteristics are observed. Demonstrated by emulation that algorithms are feasible to be implemented in real networks 15

17 Acknowledgements This research was partly supported by the Center for Advanced Computing and Communication - North Carolina State University, as a Core Project. The authors thank Fatih Hacıömeroğlu for his assistance and suggestions. 16