Admission control in Flow-Aware Networking (FAN) architectures under GridFTP traffic

This is the author’s version of a work that was accepted for publication in Optical Switching and Networking. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Optical Switching and Networking, 6, 9 (2009) DOI: 10.1016/j.osn.2008.05.003Selected papers from First International Symposium on Advanced Networks and Telecommunication Systems, ANTS 2007Computing and networking resources virtualization is the main objective of Grid services. Such a concept is already used in the context of Web-services on the Internet. In the next few years, a large number of applications belonging to various domains (biotechnology, banking, finance, car and aircraft manufacturing, nuclear energy etc.) will also benefit from Grid services. Admission control is a key functionality for Quality of Service (QoS) provision in IP networks, and more specifically for Grid services provision. Service differentiation (DS) is a widely deployed technique on the Internet. It operates at the packet level on a best-effort mode. Flow-Aware Networking (FAN) that operates at the scale of the IP flows relies on implicit flow differentiation through priority fair queuing (PFQ). It may be seen as an alternative to DS. A Grid session may be seen as a succession of parallel TCP/IP flows characterized by data transfers with much larger volume than usual TCP/IP flows. In this paper, we propose an extension of FAN for the Grid environment called Grid over FAN (GoFAN). We compare, by means of computer simulations, the efficiency of Grid over DS (GoDS) and GoFAN. Two variants of GoFAN architectures based on different fair queuing algorithms are considered. As a first step, we provide two short surveys on QoS for Grid environment and on QoS in IP networks respectively

Network delay control through adaptive queue management

Timeliness in delivering packets for delay-sensitive applications is an important QoS (Quality of Service) measure in many systems, notably those that need to provide real-time performance. In such systems, if delay-sensitive traffic is delivered to the destination beyond the deadline, then the packets will be rendered useless and dropped after received at the destination. Bandwidth that is already scarce and shared between network nodes is wasted in relaying these expired packets. This thesis proposes that a deterministic per-hop delay can be achieved by using a dynamic queue threshold concept to bound delay of each node. A deterministic per-hop delay is a key component in guaranteeing a deterministic end-to-end delay. The research aims to develop a generic approach that can constrain network delay of delay-sensitive traffic in a dynamic network. Two adaptive queue management schemes, namely, DTH (Dynamic THreshold) and ADTH (Adaptive DTH) are proposed to realize the claim. Both DTH and ADTH use the dynamic threshold concept to constrain queuing delay so that bounded average queuing delay can be achieved for the former and bounded maximum nodal delay can be achieved for the latter. DTH is an analytical approach, which uses queuing theory with superposition of N MMBP-2 (Markov Modulated Bernoulli Process) arrival processes to obtain a mapping relationship between average queuing delay and an appropriate queuing threshold, for queue management. While ADTH is an measurement-based algorithmic approach that can respond to the time-varying link quality and network dynamics in wireless ad hoc networks to constrain network delay. It manages a queue based on system performance measurements and feedback of error measured against a target delay requirement. Numerical analysis and Matlab simulation have been carried out for DTH for the purposes of validation and performance analysis. While ADTH has been evaluated in NS-2 simulation and implemented in a multi-hop wireless ad hoc network testbed for performance analysis. Results show that DTH and ADTH can constrain network delay based on the specified delay requirements, with higher packet loss as a trade-off

E3MS: A traffic engineering prototype for autoprovisioning services in IP/DiffServ/MPLS networks

This paper presents the testbed definition, implementation and trials of a new strategy for traffic autoprovisioning for MPLS and IP/DiffServ. This is the proof of concept of a new scenario for traffic engineering, for selfconfiguring control and end-to-end quality of service management by means of a tool based on Web Services. The system is structured in 3 layers: A Graphical User Interface, a Network Elements layer (an interface to physical devices) and, in the middle, a Network Management System layer, where decisions about admission, load balancing, path selection, rerouting and bandwidth allocation per class are taken. The system includes Dynamic Resource Allocation (DRA) and Background Monitoring System (BMS) modules to globally manage network resources. The so-called Squatter and Legalization mechanisms are introduced as novelties added to traffic engineering. Those strategies permit the use of part of the available resources from other classes only while unused by the class owning them. The trials hav validated the management system, using Cisco routers.Postprint (published version

Resource dimensioning through buffer sampling

Link dimensioning, i.e., selecting a (minimal) link capacity such that the users’ performance requirements are met, is a crucial component of network design. It requires insight into the interrelationship between the traffic offered (in terms of the mean offered load M, but also its fluctuation around the mean, i.e., ‘burstiness’), the envisioned performance level, and the capacity needed. We first derive, for different performance criteria, theoretical dimensioning formulae that estimate the required capacity C as a function of the input traffic and the performance target. For the special case of Gaussian input traffic these formulae reduce to C = M+V , where directly relates to the performance requirement (as agreed upon in a service level agreement) and V reflects the burstiness (at the timescale of interest). We also observe that Gaussianity applies for virtually all realistic scenarios; notably, already for a relatively low aggregation level the Gaussianity assumption is justified.\ud As estimating M is relatively straightforward, the remaining open issue concerns the estimation of V . We argue that, particularly if V corresponds to small time-scales, it may be inaccurate to estimate it directly from the traffic traces. Therefore, we propose an indirect method that samples the buffer content, estimates the buffer content distribution, and ‘inverts’ this to the variance. We validate the inversion through extensive numerical experiments (using a sizeable collection of traffic traces from various representative locations); the resulting estimate of V is then inserted in the dimensioning formula. These experiments show that both the inversion and the dimensioning formula are remarkably accurate

Quality of service aspect for BRAIN architecture

Session 4.1International audienceIn this paper, we present different aspects of Quality of Service that should be adapted to the BRAIN architecture. Several parameters and policies of QoS are depicted. Also, the paper shows the dynamic adaptation of these parameters in the context of BRAIN

Quality-of-service management in IP networks

Quality of Service (QoS) in Internet Protocol (IF) Networks has been the subject of active research over the past two decades. Integrated Services (IntServ) and Differentiated Services (DiffServ) QoS architectures have emerged as proposed standards for resource allocation in IF Networks. These two QoS architectures support the need for multiple traffic queuing systems to allow for resource partitioning for heterogeneous applications making use of the networks. There have been a number of specifications or proposals for the number of traffic queuing classes (Class of Service (CoS)) that will support integrated services in IF Networks, but none has provided verification in the form of analytical or empirical investigation to prove that its specification or proposal will be optimum. Despite the existence of the two standard QoS architectures and the large volume of research work that has been carried out on IF QoS, its deployment still remains elusive in the Internet. This is not unconnected with the complexities associated with some aspects of the standard QoS architectures. [Continues.

Resource dimensioning through buffer sampling

Link dimensioning, i.e., selecting a (minimal) link capacity such that the users’ performance requirements are met, is a crucial component of network design. It requires insight into the interrelationship among the traffic offered (in terms of the mean offered load , but also its fluctuation around the mean, i.e., ‘burstiness’), the envisioned performance level, and the capacity needed. We first derive, for different performance criteria, theoretical dimensioning formulas that estimate the required capacity $c$ as a function of the input traffic and the performance target. For the special case of Gaussian input traffic, these formulas reduce to $c = M + \alpha V$, where directly relates to the performance requirement (as agreed upon in a service level agreement) and $V$ reflects the burstiness (at the timescale of interest). We also observe that Gaussianity applies for virtually all realistic scenarios; notably, already for a relatively low aggregation level, the Gaussianity assumption is justified.\ud As estimating $M$ is relatively straightforward, the remaining open issue concerns the estimation of $V$. We argue that particularly if corresponds to small time-scales, it may be inaccurate to estimate it directly from the traffic traces. Therefore, we propose an indirect method that samples the buffer content, estimates the buffer content distribution, and ‘inverts’ this to the variance. We validate the inversion through extensive numerical experiments (using a sizeable collection of traffic traces from various representative locations); the resulting estimate of $V$ is then inserted in the dimensioning formula. These experiments show that both the inversion and the dimensioning formula are remarkably accurate