Characterization of the Burst Aggregation Process in Optical Burst Switching

Mountrouidou, Xenia; Perros, Harry G.

doi:10.1007/11753810_63

Cited by 10 publications

(12 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The burst arrival process is a slotted bulk arrival process as derived in [9]. To the best of our knowledge, this is a unique feature of our analysis: using an analytical burst arrival process as it results from a burst aggregation algorithm.…”

Section: The Queueing Networkmentioning

confidence: 98%

“…The OBS network is modeled as a multiple server system and the Erlang B formula is applied to calculate the burst loss probability. Assuming that burst arrivals follow a Poisson distribution is unrealistic as shown in [9] since the burst arrival process depends on the aggregation algorithm that is used. More specifically if the Time based aggregation is used then the burst arrival process is slotted with a burst size that has an Erlang distribution, since the burst is a convolution of exponential packet sizes.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A discrete-time queueing network model of a hub-based OBS architecture

Mountrouidou

Perros

2009

Telecommun Syst

Self Cite

View full text Add to dashboard Cite

We propose and analyze a novel discrete-time queueing network model of a zero loss hub-based Optical Burst Switched (OBS) architecture, consisting of multiple input edge nodes and one destination edge node. The arrival process of bursts is slotted with bulk arrivals as generated by a Time and Burst-Length based burst aggregation algorithm. The queueing network is analyzed by decomposition. We obtain the average end-to-end delay of a burst in the queueing network as well as queueing delays at individual nodes. Our model provides a tight upper bound as shown by comparing the analytical data to simulation results.

show abstract

Section: The Queueing Networkmentioning

confidence: 98%

mentioning

confidence: 99%

A discrete-time queueing network model of a hub-based OBS architecture

Mountrouidou

Perros

2009

Telecommun Syst

Self Cite

View full text Add to dashboard Cite

show abstract

“…Now we can use this z-transform to form the generating function of the number of packets, as shown in [6]. It remains to calculate the convolution of n IPP inter-arrivals of packets, F τ n (T − t).…”

Section: Delay Per Packet Distributionmentioning

confidence: 99%

“…The authors in [1] give an analytical method to calculate the burst size for various algorithms, assuming Poisson arrivals of fixed-size packets to the edge node. Modeling of the pdf of the number of bytes buffered during an aggregation period and of the number of bursts resulting from the mixed Time and Burst Length based aggregation algorithm is presented in [6]. In [7] we studied the burst departure process for all three algorithms assuming Poisson and IPP arrivals.…”

Section: Introductionmentioning

confidence: 99%

On the traffic modeling of burst aggregation algorithms using video and data traces

Mountrouidou

2008

Telecommun Syst

Self Cite

View full text Add to dashboard Cite

We obtain analytically the delay per packet in a burst for the three burst aggregation algorithms: Time based, Burst Length based and mixed Time and Burst Length based, assuming real video and aggregated Internet traces, as well as the IPP arrivals. For video traces we also obtain the probability that a playback machine will pause due to lack of frames when streaming video. Accuracy of the analytic results was established by comparing them to simulation data.

show abstract

“…In Reference [8], a variation of the time-based assembly algorithm is studied, in which at the end of each aggregation period a length threshold is applied and contents of the buffer are accordingly assembled into a few bursts of a given length. The analysis provides the distribution of number of bursts that are generated at the end of an assembly period.…”

Section: Related Workmentioning

confidence: 99%

Traffic analysis in optical burst switching networks: a trace‐based case study

Rostami

Wolisz

Feldmann

2009

Trans Emerging Tel Tech

View full text Add to dashboard Cite

SUMMARYOptical burst switching (OBS) appears as a promising technology for building dynamic optical transport networks. The main advantage of OBS is that it allows for dynamic allocation of resources at sub-wavelength granularity. Nevertheless, the burst contention problem, which occurs frequently inside the network, has to be addressed before OBS can be really deployed as the next generation optical transport network. Recently a lot of attention is devoted to different approaches for resolving contentions in OBS networks. Although performance analysis of these approaches is strongly dependent on the traffic characteristics in the network, the majority of the studies is so far based on very hypothetical traffic assumptions. In this study we use traces of real measurements in the Internet to derive realistic data about the traffic that is injected into the OBS network. Specifically, we investigate the marginal distributions of burst size, burst interdeparture time, assembly delay and number of packets per burst as well as the burstiness of the burst traces. We demonstrate that the performance of an OBS core node using the real traces is pretty similar to the results obtained when the traffic arriving to the core node is assumed to be Poisson. In fact, usage of the Poisson as the process of burst arrival to the core node leads in all the investigated cases to an upper bound on the burst drop rate at that node.

show abstract

Characterization of the Burst Aggregation Process in Optical Burst Switching

Cited by 10 publications

References 3 publications

A discrete-time queueing network model of a hub-based OBS architecture

A discrete-time queueing network model of a hub-based OBS architecture

On the traffic modeling of burst aggregation algorithms using video and data traces

Traffic analysis in optical burst switching networks: a trace‐based case study

Contact Info

Product

Resources

About