Burst MetricIn packet-based networks

Initial Considerations for IPPM burst metric

Tuesday, March 21, 2006

•Why

•Need to better size network resources

•Improve Performance management

•Network traffic is bursty;

•Bursty applications

•Network congestions

•Asymmetric nature of interactive traffic

•TCP applications

•Unbalanced parallel streams

•Unstable network, poorly sized network resources on sender side

•Burstness of the traffic may affect the application performance

•Packet drops/ buffer overflow

•Poor video / voice quality

•A single stream may be bursty while the combined flow from n streams may not

•Need to understand and measure traffic characteristics

•We have measures for packet loss, gap, burst loss (RFC3611), loss patterns ( RFC 3357) not packet burst

•Burstness metric ( requests) may be used to size the servers•Burstneess metric may be used to design and optimize network resources

•Bandwidth allocation•Buffer sizing

•Burstness metric maybe used to dimension QoS class parameters

Some measures

• Degree of burstness– Ratio of peak to average transmission rate over time

interval• Hurst Index

– Measure of self-similarity of traffic• Token bucket burst measures

– Amount of traffic that can be sent within a given unit of time

• Index of dispersion for counts (IDC)– Ratio between the variance and expected number of

arrivals (Nt) over interval (t)• IDCt= Var(Nt)/E(Nt)

To do list

• Develop IPPM Burst metric– Need to define the phenomena we want to

measure– Need a definition of metric– Need a metric– Need operationalization

References• RFC 3611 - RTP Control Protocol Extended Reports (RTCP XR)

• RFC 3357 - One-way Loss Pattern Sample Metrics

• draft-auerbach-mgcp-rtcpxr-03.txt

• draft-clark-avt-rtcpxr-video-01.txt

• Why is the Internet traffic bursty in short time scales? Hao Jiang, Constantine Dovrolis- College of Computing Georgia Institute of Technology

– Well understood cause-effect relationship in large scales (> 1 second)

• Leland et al. (TNet’94): asymptotic self-similarity and LRD behavior, assuming stationarity

• Major causes:

– Willinger et al. (Sigcomm’95): heavy-tailed ON-OFF behavior in traffic interarrivals

– Crovella/Bestavros (TNet’99): heavy-tailed ON-OFF behavior in Web transfers

– Also well understood is the role of TCP congestion control in sup-RTT time scales

• Figueiredo et al. (CompNets’02): pseudo-self similar behavior in a range of medium time scales

– From a single RTT to tens/hundreds of RTTs

• Cause: strong correlations due to TCP congestion control and timeouts