Fixed-point analysis of a network of routers with persistent TCP/UDP flows and class-based weighted fair queuing

Fixed-point models have already been successfully used to analytically study networks consisting of persistent TCP flows only, or mixed TCP/UDP flows with a single queue per link and differentiated buffer management for these two types of flows. In the current study, we propose a nested fixed-point analytical method to obtain the throughput of persistent TCP and UDP flows in a network of routers supporting class-based weighted fair queuing allowing the use of separate queues for each class. In particular, we study the case of two classes where one of the classes uses drop-tail queue management and is intended for only UDP traffic. The other class targeting TCP, but also allowing UDP traffic for the purpose of generality, is assumed to employ active queue management. The effectiveness of the proposed analytical method is validated in terms of accuracy using ns-3 simulations and the required computational effort. © 2016, Springer Science+Business Media New York

On Class-based Isolation of UDP, Short-lived and Long-lived TCP Flows

The congestion control mechanisms of TCP make it vulnerable in an environment where flows with different congestionsensitivity compete for scarce resources. With the increasing amount of unresponsive UDP traffic in today's Internet, new mechanisms are needed to enforce fairness in the core of the network. We propose a scalable Diffserv-like architecture, where flows with different characteristics are classified into separate service queues at the routers. Such class-based isolation provides protection so that flows with different characteristics do not negatively impact one another. In this study, we examine different aspects of UDP and TCP interaction and possible gains from segregating UDP and TCP into different classes. We also investigate the utility of further segregating TCP flows into two classes, which are class of short and class of long flows. Results are obtained analytically for both Tail-drop and Random Early Drop (RED) routers. Class-based isolation have the following salient features: (1) better fairness, (2) improved predictability for all kinds of flows, (3) lower transmission delay for delay-sensitive flows, and (4) better control over Quality of Service (QoS) of a particular traffic type

Orchestrating Bulk Data Movement in Grid Environments

Data Grids provide a convenient environment for researchers to manage and access massively distributed bulk data by addressing several system and transfer challenges inherent to these environments. This work addresses issues involved in the efficient selection and access of replicated data in Grid environments in the context of the Globus Toolkit{trademark}, building middleware that (1) selects datasets in highly replicated environments, enabling efficient scheduling of data transfer requests; (2) predicts transfer times of bulk wide-area data transfers using extensive statistical analysis; and (3) co-allocates bulk data transfer requests, enabling parallel downloads from mirrored sites. These efforts have demonstrated a decentralized data scheduling architecture, a set of forecasting tools that predict bandwidth availability within 15% error and co-allocation architecture, and heuristics that expedites data downloads by up to 2 times

Networking Mechanisms for Delay-Sensitive Applications

The diversity of applications served by the explosively growing Internet is increasing. In particular, applications that are sensitive to end-to-end packet delays become more common and include telephony, video conferencing, and networked games. While the single best-effort service of the current Internet favors throughput-greedy traffic by equipping congested links with large buffers, long queuing at the congested links hurts the delay-sensitive applications. Furthermore, while numerous alternative architectures have been proposed to offer diverse network services, the innovative alternatives failed to gain widespread end-to-end deployment. This dissertation explores different networking mechanisms for supporting low queueing delay required by delay-sensitive applications. In particular, it considers two different approaches. The first one assumes employing congestion control protocols for the traffic generated by the considered class of applications. The second approach relies on the router operation only and does not require support from end hosts

Towards automatic traffic classification and estimation for available bandwidth in IP networks.

Growing rapidly, today's Internet is becoming more difficult to manage. A good understanding of what kind of network traffic classes are consuming network resource as well as how much network resource is available is important for many management tasks like QoS provisioning and traffic engineering. In the light of these objectives, two measurement mechanisms have been explored in this thesis. This thesis explores a new type of traffic classification scheme with automatic and accurate identification capability. First of all, the novel concept of IP flow profile, a unique identifier to the associated traffic class, has been proposed and the relevant model using five IP header based contexts has been presented. Then, this thesis shows that the key statistical features of each context, in the IP flow profile, follows a Gaussian distribution and explores how to use Kohonen Neural Network (KNN) for the purpose of automatically producing IP flow profile map. In order to improve the classification accuracy, this thesis investigates and evaluates the use of PCA for feature selection, which enables the produced patterns to be as tight as possible since tight patterns lead to less overlaps among patterns. In addition, the use of Linear Discriminant Analysis and alternative KNN maps has been investigated as to deal with the overlap issue between produced patterns. The entirety of this process represents a novel addition to the quest for automatic traffic classification in IP networks. This thesis also develops a fast available bandwidth measurement scheme. It firstly addresses the dynamic problem for the one way delay (OWD) trend detection. To deal with this issue, a novel model - asymptotic OWD Comparison (AOC) model for the OWD trend detection has been proposed. Then, three statistical metrics SOT (Sum of Trend), PTC (Positive Trend Checking) and CTC (Complete Trend Comparison) have been proposed to develop the AOC algorithms. To validate the proposed AOC model, an avail-bw estimation tool called Pathpair has been developed and evaluated in the Planetlah environment