An analytical model for jitter in IP networks

ABSTRACT: Traditionally, IP network planning and design is mostly based on the average delay or loss constraints which can often be easily calculated. Jitter, on the other hand, is much more difficult to evaluate, but it is particularly important to manage the QoS of real-time and interactive services such as VoIP and streaming video. In this paper, we present simple formulas for the jitter of Poisson traffic in a single queue that can be quickly calculated . It takes into account the packets delay correlation and also the correlation of tandem queues that have a significant impact on the end-to-end jitter. We then extend them to the end-to-end jitter of a tagged stream based on a tandem queueing network. The results given by the model are then compared with event-driven simulations. We find that they are very accurate for Poisson traffic over a wide range of traffic loads and more importantly that they yield conservative values for the jitter so that they can be used in network design procedures. We also find some very counter-intuitive results. We show that jitter actually decreases with increasing load and the total jitter on a path depends on the position of congested links on that path. We finally point out some consequences of these results for network design procedures

Dynamic Service Rate Control for a Single Server Queue with Markov Modulated Arrivals

We consider the problem of service rate control of a single server queueing system with a finite-state Markov-modulated Poisson arrival process. We show that the optimal service rate is non-decreasing in the number of customers in the system; higher congestion rates warrant higher service rates. On the contrary, however, we show that the optimal service rate is not necessarily monotone in the current arrival rate. If the modulating process satisfies a stochastic monotonicity property the monotonicity is recovered. We examine several heuristics and show where heuristics are reasonable substitutes for the optimal control. None of the heuristics perform well in all the regimes. Secondly, we discuss when the Markov-modulated Poisson process with service rate control can act as a heuristic itself to approximate the control of a system with a periodic non-homogeneous Poisson arrival process. Not only is the current model of interest in the control of Internet or mobile networks with bursty traffic, but it is also useful in providing a tractable alternative for the control of service centers with non-stationary arrival rates.Comment: 32 Pages, 7 Figure

Wavelength converter sharing in asynchronous optical packet/burst switching: An exact blocking analysis for markovian arrivals

Cataloged from PDF version of article.In this paper, we study the blocking probabilities in a wavelength division multiplexing-based asynchronous bufferless optical packet/burst switch equipped with a bank of tuneable wavelength converters dedicated to each output fiber line. Wavelength converter sharing, also referred to as partial wavelength conversion, corresponds to the case of a number of converters shared amongst a larger number of wavelength channels. In this study, we present a probabilistic framework for exactly calculating the packet blocking probabilities for optical packet/burst switching systems utilizing wavelength converter sharing. In our model, packet arrivals at the optical switch are first assumed to be Poisson and later generalized to the more general Markovian arrival process to cope with very general traffic patterns whereas packet lengths are assumed to be exponentially distributed. As opposed to the existing literature based on approximations and/or simulations, we formulate the problem as one of finding the steady-state solution of a continuous-time Markov chain with a block tridiagonal infinitesimal generator. To find such solutions, we propose a numerically efficient and stable algorithm based on block tridiagonal LU factorizations. We show that exact blocking probabilities can be efficiently calculated even for very large systems and rare blocking probabilities, e.g., systems with 256 wavelengths per fiber and blocking probabilities in the order of 10−40. Relying on the stability and speed of the proposed algorithm, we also provide a means of provisioning wavelength channels and converters in optical packet/burst switching systems

Dynamic threshold-based assembly algorithms for optical burst switching networks subject to burst rate constraints

Control plane load stems from burst control packets which need to be transmitted end-to-end over the control channel and furtherprocessed at core nodes of an optical burst switching (OBS) network for reserving resources in advance for an upcoming burst. Burst assembly algorithms are generally designed without taking into consideration the control plane load they lead to. In this study, we propose traffic-adaptive burst assembly algorithms that attempt to minimize the average burst assembly delay subject to burst rate constraints and hence limit the control plane load. The algorithms we propose are simple to implement and we show using synthetic and real traffic traces that they perform substantially better than the usual timer-based schemes. © Springer Science+Business Media, LLC 2010

Exact analysis of single-wavelength optical buffers with feedback markov fluid queues

Optical buffering via fiber delay lines is used for contention resolution in optical packet and optical burst switching nodes. This article addresses the problem of exactly finding the blocking probabilities in an asynchronous single-wavelength optical buffer. Packet lengths are assumed to be variable and modeled by phase-type distributions, whereas the packet arrival process is modeled by a Markovian arrival process that can capture autocorrelations in interarrival times. The exact solution is based on the theory of feedback fluid queues for which we propose numerically efficient and stable algorithms. We not only find the packet blocking probabilities but also the entire distribution of the unfinished work in this system from which all performance measures of interest can be derived. © 2009 Optical Society of America

QoS Routing in Wireless Mesh Networks

Wireless Mesh Networking is envisioned as an economically viable paradigm and a promising technology in providing wireless broadband services. The wireless mesh backbone consists of fixed mesh routers that interconnect different mesh clients to themselves and to the wireline backbone network. In order to approach the wireline servicing level and provide same or near QoS guarantees to different traffic flows, the wireless mesh backbone should be quality-of-service (QoS) aware. A key factor in designing protocols for a wireless mesh network (WMN) is to exploit its distinct characteristics, mainly immobility of mesh routers and less-constrained power consumption. In this work, we study the effect of varying the transmission power to achieve the required signal-to-interference noise ratio for each link and, at the same time, to maximize the number of simultaneously active links. We propose a QoS-aware routing framework by using transmission power control. The framework addresses both the link scheduling and QoS routing problems with a cross-layer design taking into consideration the spatial reuse of the network bandwidth. We formulate an optimization problem to find the optimal link schedule and use it as a fitness function in a genetic algorithm to find candidate routes. Using computer simulations, we show that by optimal power allocation the QoS constraints for the different traffic flows are met with more efficient bandwidth utilization than the minimum power allocations

Dynamic threshold-based algorithms for communication networks

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 68-72.A need to use dynamic thresholds arises in various communication networking scenarios under varying traffic conditions. In this thesis, we propose novel dynamic threshold-based algorithms for two different networking problems, namely the problem of burst assembly in Optical Burst Switching (OBS) networks and of bandwidth reservation in connection-oriented networks. Regarding the first problem, we present dynamic threshold-based burst assembly algorithms that attempt to minimize the average burst assembly delay due to burstification process while taking the burst rate constraints into consideration. Using synthetic and real traffic traces, we show that the proposed algorithms perform significantly better than the conventional timer-based schemes. In the second problem, we propose a model-free adaptive hysteresis algorithm for dynamic bandwidth reservation in a connection-oriented network subject to update frequency constraints. The simulation results in various traffic scenarios show that the proposed technique considerably outperforms the existing schemes without requiring any prior traffic information.Toksöz, Mehmet AltanM.S

Workload Prediction for Efficient Performance Isolation and System Reliability

In large-scaled and distributed systems, like multi-tier storage systems and cloud data centers, resource sharing among workloads brings multiple benefits while introducing many performance challenges. The key to effective workload multiplexing is accurate workload prediction. This thesis focuses on how to capture the salient characteristics of the real-world workloads to develop workload prediction methods and to drive scheduling and resource allocation policies, in order to achieve efficient and in-time resource isolation among applications. For a multi-tier storage system, high-priority user work is often multiplexed with low-priority background work. This brings the challenge of how to strike a balance between maintaining the user performance and maximizing the amount of finished background work. In this thesis, we propose two resource isolation policies based on different workload prediction methods: one is a Markovian model-based and the other is a neural networks-based. These policies aim at, via workload prediction, discovering the opportune time to schedule background work with minimum impact on user performance. Trace-driven simulations verify the efficiency of the two pro- posed resource isolation policies. The Markovian model-based policy successfully schedules the background work at the appropriate periods with small impact on the user performance. The neural networks-based policy adaptively schedules user and background work, resulting in meeting both performance requirements consistently. This thesis also proposes an accurate while efficient neural networks-based pre- diction method for data center usage series, called PRACTISE. Different from the traditional neural networks for time series prediction, PRACTISE selects the most informative features from the past observations of the time series itself. Testing on a large set of usage series in production data centers illustrates the accuracy (e.g., prediction error) and efficiency (e.g., time cost) of PRACTISE. The superiority of the usage prediction also allows a proactive resource management in the highly virtualized cloud data centers. In this thesis, we analyze on the performance tickets in the cloud data centers, and propose an active sizing algorithm, named ATM, that predicts the usage workloads and re-allocates capacity to work- loads to avoid VM performance tickets. Moreover, driven by cheap prediction of usage tails, we also present TailGuard in this thesis, which dynamically clones VMs among co-located boxes, in order to efficiently reduce the performance violations of physical boxes in cloud data centers

Performance analysis of a proposed hybrid optical network

This dissertation discusses a novel Hybrid Optical Network (HON) that can provide service differentiation based on traffic characteristics (i.e., packet, burst, and long-lived flow) with QoS guarantee not only in network layer, but also in physical layer. The DHON consists of sophisticated edge-nodes, which can classify, monitor, and dynamically adjust optical channels in the core layer as traffic variation. The edge nodes aggregate traffic, identifying end-to-end delay by ingress queuing delay or burst timeout. The network can estimate number of channels by arriving traffic intensity and distribution with estimated upper-bound delay. The core layer employs two parallel optical switches (OCS, OBS) in the same platform. Thanks to the overflow system, the proposed network enhances utilization with fewer long distance premium channels. The premium channel can quickly handle burst traffic without new channel assignment. With less overprovisioning capacity design, the premium channel enhances utilization and decrease number of costly premium channels. This research also proposes mathematic models to represent particular DHON channels (i.e., circuit, packet, and burst). We employ method of moments based on overflow theory to forecast irregular traffic pattern from circuit-based channel (i.e., M/M/c/c) to overflow channel, in which G/G/1 model based on Ph/Ph/1 matrix can represent the overflow channel. Moreover, secondary channel supports packet-based traffic over wavelength channel with two service classes: Class I based on delay sensitive traffic (i.e., long flow) and Class II for non-delay sensitive traffic (e.g., best effort). In addition, mixture of traffic in the wavelength channels is investigated based on M/G/1 and M/G/2 with specific service time distribution for particular class. Finally, we show our DHON based on (O-O-O) switching paradigm has improved the performance over typical (O-E-O) switching network architecture based on NSF topology