26 research outputs found
A new approach for asynchronous distributed rate control of elastic sessions in integrated packet networks
We develop a new class of asynchronous distributed algorithms for the explicit rate control of elastic sessions in an integrated packet network. Sessions can request for minimum guaranteed rate allocations (e.g., minimum cell rates in the ATM context), and, under this constraint, we seek to allocate the max-min fair rates to the sessions. We capture the integrated network context by permitting the link bandwidths available to elastic sessions to be stochastically time varying. The available capacity of each link is viewed as some statistic of this stochastic process [e.g., a fraction of the mean, or a large deviations-based equivalent service capacity (ESC)]. The ESC is obtained so as to satisfy an overflow probability constraint on the buffer length. For fixed available capacity at each link, we show that the vector of max-min fair rates can be computed from the root of a certain vector equation. A distributed asynchronous stochastic approximation technique is then used to develop a provably convergent distributed algorithm for obtaining the root of the equation, even when the link flows and the available capacities are obtained from on-line measurements. The switch algorithm does not require per connection monitoring, nor does it require per connection marking of control packets. A virtual buffer based approach for on-line estimation of the ESC is utilized. We also propose techniques for handling large variations in the available capacity owing to the arrivals or departures of CBR/VBR sessions. Finally, simulation results are provided to demonstrate the performance of this class of algorithms in the local and wide area network context
Design and analysis of flow control algorithms for data networks
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (leaves 110-112).by Paolo L. NavĂĄez Guarnieri.M.S
Reactive traffic control mechanisms for communication networks with self-similar bandwidth demands
Communication network architectures are in the process of being redesigned so that many different services are integrated within the same network. Due to this integration, traffic management algorithms need to balance the requirements of the traffic which the algorithms are directly controlling with Quality of Service (QoS) requirements of other classes of traffic which will be encountered in the network. Of particular interest is one class of traffic, termed elastic traffic, that responds to dynamic feedback from the network regarding the amount of available resources within the network. Examples of this type of traffic include the Available Bit Rate (ABR) service in Asynchronous Transfer Mode (ATM) networks and connections using Transmission Control Protocol (TCP) in the Internet. Both examples aim to utilise available bandwidth within a network.
Reactive traffic management, like that which occurs in the ABR service and TCP, depends explicitly on the dynamic bandwidth requirements of other traffic which is currently using the network. In particular, there is significant evidence that a wide range of network traffic, including Ethernet, World Wide Web, Varible Bit Rate video and signalling traffic, is self-similar. The term self-similar refers to the particular characteristic of network traffic to remain bursty over a wide range of time scales. A closely associated characteristic of self-similar traffic is its long-range dependence (LRD), which refers to the significant correlations that occur with the traffic. By utilising these correlations, greater predictability of network traffic can be achieved, and hence the performance of reactive traffic management algorithms can be enhanced.
A predictive rate control algorithm, called PERC (Predictive Explicit Rate Control), is proposed in this thesis which is targeted to the ABR service in ATM networks. By incorporating the LRD stochastic structure of background traffic, measurements of the bandwidth requirements of background traffic, and the delay associated with a particular ABR connection, a predictive algorithm is defined which provides explicit rate information that is conveyed to ABR sources. An enhancement to PERC is also described. This algorithm, called PERC+, uses previous control information to correct prediction errors that occur for connections with larger round-trip delay. These algorithms have been extensively analysed with regards to their network performance, and simulation results show that queue lengths and cell loss rates are significantly reduced when these algorithms are deployed. An adaptive version of PERC has also been developed using real-time parameter estimates of self-similar traffic. This has excellent performance compared with standard ABR rate control algorithms such as ERICA.
Since PERC and its enhancement PERC+ have explicitly utilised the index of self-similarity, known as the Hurst parameter, the sensitivity of these algorithms to this parameter can be determined analytically. Research work described in this thesis shows that the algorithms have an asymmetric sensitivity to the Hurst parameter, with significant sensitivity in the region where the parameter is underestimated as being close to 0.5. Simulation results reveal the same bias in the performance of the algorithm with regards to the Hurst parameter. In contrast, PERC is insensitive to estimates of the mean, using the sample mean estimator, and estimates of the traffic variance, which is due to the algorithm primarily utilising the correlation structure of the traffic to predict future bandwidth requirements.
Sensitivity analysis falls into the area of investigative research, but it naturally leads to the area of robust control, where algorithms are designed so that uncertainty in traffic parameter estimation or modelling can be accommodated. An alternative robust design approach, to the standard maximum entropy approach, is proposed in this thesis that uses the maximum likelihood function to develop the predictive rate controller. The likelihood function defines the proximity of a specific traffic model to the traffic data, and hence gives a measure of the performance of a chosen model. Maximising the likelihood function leads to optimising robust performance, and it is shown, through simulations, that the system performance is close to the optimal performance as compared with maximising the spectral entropy.
There is still debate regarding the influence of LRD on network performance. This thesis also considers the question of the influence of LRD on traffic predictability, and demonstrates that predictive rate control algorithms that only use short-term correlations have close performance to algorithms that utilise long-term correlations. It is noted that predictors based on LRD still out-perform ones which use short-term correlations, but that there is Potential simplification in the design of predictors, since traffic predictability can be achieved using short-term correlations.
This thesis forms a substantial contribution to the understanding of control in the case where self-similar processes form part of the overall system. Rather than doggedly pursuing self-similar control, a broader view has been taken where the performance of algorithms have been considered from a number of perspectives. A number of different research avenues lead on from this work, and these are outlined
Flow control and service differentiation in optical burst switching networks
Cataloged from PDF version of article.Optical Burst Switching (OBS) is being considered as a candidate architecture
for the next generation optical Internet. The central idea behind OBS is the assembly
of client packets into longer bursts at the edge of an OBS domain and the
promise of optical technologies to enable switch reconfiguration at the burst level
therefore providing a near-term optical networking solution with finer switching
granularity in the optical domain. In conventional OBS, bursts are injected to
the network immediately after their assembly irrespective of the loading on the
links, which in turn leads to uncontrolled burst losses and deteriorating performance
for end users. Another key concern related to OBS is the difficulty of
supporting QoS (Quality of Service) in the optical domain whereas support of
differentiated services via per-class queueing is very common in current electronically
switched networks. In this thesis, we propose a new control plane protocol,
called Differentiated ABR (D-ABR), for flow control (i.e., burst shaping) and
service differentiation in optical burst switching networks. Using D-ABR, we
show with the aid of simulations that the optical network can be designed to
work at any desired burst blocking probability by the flow control service of the proposed architecture. The proposed architecture requires certain modifications
to the existing control plane mechanisms as well as incorporation of advanced
scheduling mechanisms at the ingress nodes; however we do not make any specific
assumptions on the data plane of the optical nodes. With this protocol, it is
possible to almost perfectly isolate high priority and low priority traffic throughout
the optical network as in the strict priority-based service differentiation in
electronically switched networks. Moreover, the proposed architecture moves the
congestion away from the OBS domain to the edges of the network where it is
possible to employ advanced queueing and buffer management mechanisms. We
also conjecture that such a controlled OBS architecture may reduce the number
of costly Wavelength Converters (WC) and Fiber Delay Lines (FDL) that are
used for contention resolution inside an OBS domain.Boyraz, HakanM.S
Recommended from our members
Performance analysis and improvement of InfiniBand networks. Modelling and effective Quality-of-Service mechanisms for interconnection networks in cluster computing systems.
The InfiniBand Architecture (IBA) network has been proposed as a new
industrial standard with high-bandwidth and low-latency suitable for constructing
high-performance interconnected cluster computing systems. This architecture
replaces the traditional bus-based interconnection with a switch-based network for
the server Input-Output (I/O) and inter-processor communications. The efficient
Quality-of-Service (QoS) mechanism is fundamental to ensure the import at QoS
metrics, such as maximum throughput and minimum latency, leaving aside other
aspects like guarantee to reduce the delay, blocking probability, and mean queue
length, etc.
Performance modelling and analysis has been and continues to be of great
theoretical and practical importance in the design and development of
communication networks. This thesis aims to investigate efficient and cost-effective
QoS mechanisms for performance analysis and improvement of InfiniBand
networks in cluster-based computing systems.
Firstly, a rate-based source-response link-by-link admission and congestion
control function with improved Explicit Congestion Notification (ECN) packet
marking scheme is developed. This function adopts the rate control to reduce
congestion of multiple-class traffic. Secondly, a credit-based flow control scheme is
presented to reduce the mean queue length, throughput and response time of the system. In order to evaluate the performance of this scheme, a new queueing
network model is developed. Theoretical analysis and simulation experiments show
that these two schemes are quite effective and suitable for InfiniBand networks.
Finally, to obtain a thorough and deep understanding of the performance attributes
of InfiniBand Architecture network, two efficient threshold function flow control
mechanisms are proposed to enhance the QoS of InfiniBand networks; one is Entry
Threshold that sets the threshold for each entry in the arbitration table, and other is
Arrival Job Threshold that sets the threshold based on the number of jobs in each
Virtual Lane. Furthermore, the principle of Maximum Entropy is adopted to analyse
these two new mechanisms with the Generalized Exponential (GE)-Type
distribution for modelling the inter-arrival times and service times of the input traffic.
Extensive simulation experiments are conducted to validate the accuracy of the
analytical models
A hybrid queueing model for fast broadband networking simulation
PhDThis research focuses on the investigation of a fast simulation method for broadband
telecommunication networks, such as ATM networks and IP networks. As a result of
this research, a hybrid simulation model is proposed, which combines the analytical
modelling and event-driven simulation modelling to speeding up the overall
simulation.
The division between foreground and background traffic and the way of dealing with
these different types of traffic to achieve improvement in simulation time is the major
contribution reported in this thesis. Background traffic is present to ensure that proper
buffering behaviour is included during the course of the simulation experiments, but
only the foreground traffic of interest is simulated, unlike traditional simulation
techniques. Foreground and background traffic are dealt with in a different way.
To avoid the need for extra events on the event list, and the processing overhead,
associated with the background traffic, the novel technique investigated in this
research is to remove the background traffic completely, adjusting the service time of
the queues for the background traffic to compensate (in most cases, the service time
for the foreground traffic will increase). By removing the background traffic from the
event-driven simulator the number of cell processing events dealt with is reduced
drastically.
Validation of this approach shows that, overall, the method works well, but the
simulation using this method does have some differences compared with experimental
results on a testbed. The reason for this is mainly because of the assumptions behind
the analytical model that make the modelling tractable.
Hence, the analytical model needs to be adjusted. This is done by having a neural
network trained to learn the relationship between the input traffic parameters and the
output difference between the proposed model and the testbed. Following this
training, simulations can be run using the output of the neural network to adjust the
analytical model for those particular traffic conditions.
The approach is applied to cell scale and burst scale queueing to simulate an ATM
switch, and it is also used to simulate an IP router. In all the applications, the method
ensures a fast simulation as well as an accurate result