Stochastic Network Calculus Research Articles

On applying stochastic network calculus for Gilbert-Elliot fading channel in underwater wireless communication networks

The main contribution in this research work is thus to use modern mathematical approaches using stochastic network calculus to model the fading effects in underwater channel. Also, the design methodology adopted was to create these mathematical models using Proto-C in Riverbed Modeler Simulation tool and test them using different scenario's representing an underwater communication environment. This work is the first of its kind in terms of applying stochastic network calculus and deriving the necessary stochastic service guarantees for underwater wireless communication. The value of the findings prove that stochastic network calculus can emerge as an elegant mathematical tool for assessing underwater network performance. The significance of the work is that; this approach of modelling underwater networks provides guidelines for modelling the transmission strategies in any underwater simulation studies. The need and purpose of this work is to help researchers adapt stochastic network calculus to model the physical layer and its parameters, that closely represents the real-time underwater scenario. The importance of accurately modelling the physical layer will improve the efficiency of the performance in the upper layers and the entire underwater end-to-end network. The findings derived from stochastic service guarantees verifies that stochastic performance bounds are very close and results in minimal delay, backlog in the designed underwater network.

A Statistical Property of Wireless Channel Capacity: Theory and Application

This paper presents a set of new results on wireless channel capacity by exploring its special characteristics. An appealing discovery is that the instantaneous and cumulative capacity distributions of typical fading channels are lighttailed. An implication of this property is that these distributions and subsequently the distributions of delay and backlog for constant arrivals can be upper-bounded by some exponential functions, which is often assumed but not justified in the literature of wireless network performance analysis. In addition, three representative dependence structures of the capacity process are studied, namely comonotonicity, independence, and Markovian, and bounds are derived for the cumulative capacity distribution and delay-constrained capacity. To help gain insights in the performance of a wireless channel whose capacity process may be too complex or detailed dependence information is lacking, stochastic orders are introduced to the capacity process, based on which, comparison results of delay and delay-constrained capacity are obtained. Moreover, the impact of self-interference in communication, which is an open problem in stochastic network calculus (SNC), is investigated and original results are derived. These results complement the SNC literature, easing its application to wireless networks and its extension towards a calculus for wireless networks.

Low-Latency Millimeter-Wave Communications: Traffic Dispersion or Network Densification?

Low latency is critical for many applications in wireless communications, e.g., vehicle-to-vehicle, multimedia, and industrial control networks. Meanwhile, for the capability of providing multi-gigabits per second rates, millimeter-wave (mm-wave) communication has attracted substantial research interest recently. This paper investigates two strategies to reduce the communication delay in future wireless networks: traffic dispersion and network densification. A hybrid scheme that combines these two strategies is also considered. The probabilistic delay and effective capacity are used to evaluate performance. For probabilistic delay, the violation probability of delay, i.e., the probability that the delay exceeds a given tolerance level, is characterized in terms of upper bounds, which are derived by applying stochastic network calculus theory. In addition, to characterize the maximum affordable arrival traffic for mm-wave systems, the effective capacity, i.e., the service capability with a given quality-of-service requirement, is studied. The derived bounds on the probabilistic delay and effective capacity are validated through simulations. These numerical results show that, for a given sum power budget, traffic dispersion, network densification, and the hybrid scheme exhibit different potentials to reduce the end-to-end communication delay. For instance, traffic dispersion outperforms network densification when high sum power budget and arrival rate are given, while it could be the worst option, otherwise. Furthermore, it is revealed that, increasing the number of independent paths and/or relay density is always beneficial, while the performance gain is related to the arrival rate and sum power, jointly. Therefore, a proper transmission scheme should be selected to optimize the delay performance, according to the given conditions on arrival traffic and system service capability.

Modelling Rayleigh Fading Channel in Underwater Wireless Communication Networks using Stochastic Network Calculus

A fundamental issue in underwater acoustic wireless channels is in analyzing the backlog and delay bounds that directly impacts the Quality of Service (QoS) performance in networks. Modern networks have been increasingly complex over the past few years in terms of control algorithms, applications and service expectations. Deterministic Networks Calculus (DNC) is not applicable to analyze QoS for the present day packet switched multimedia networks due to their inherently random behavior. To overcome these issues, Stochastic Network Calculus (SNC) has given rise to the optimism that it can emerge as an elegant mathematical modeling tool for assessing current network performance. In this work, we develop an underwater acoustic wireless channel subject to fading eects based on SNC that obtains Stochastic Arrival Curve (SAC) and Stochastic Service Curve (SSC). Using this we also derive the stochastic performance for delay and backlog bounds in underwater acoustic fading channel. The simulation performance analysis and bounds show that this method can provide guidelines for designing transmission strategies in underwater acoustic wireless channels.

無線マルチホップ通信の確率的性能予測手法

M2M (machine-to-machine) is required and spreads rapidly in recent years. Wireless multi-hop communication is one of the communication styles to construct M2M. However, the performance estimation of such wireless multi-hop communication is not so easy because the bit error ratio and symbol rate adaptation heavily depend on various environmental conditions. Stochastic network calculus (SNC) is a theory for the performance estimation of network. However, current SNC lacks ways to take wireless random errors, branching and joining of traffic flows into calculation, these are necessary to estimate the performance of sink-tree wireless-sensor network. This paper proposed a performance estimation method for sink-tree wireless multi-hop network based on SNC. The proposed method has three attractive points. Firstly, this introduces parameters of hop number and node number considering wireless random error due to packet collision. Secondly, this treats actual sink-tree network model by considering joining of traffic. Thirdly, this separates the service curve and bounding function calculation to avoid double counting. To evaluate the accuracy of the result of the proposed method, conventional estimation methods and simulation evaluation were conducted on a typical wireless multi-hop sensor data collection network. The results validate that the proposed method achieves much higher-accuracy than conventional methods.

Reliable Video Streaming With Strict Playout Deadline in Multihop Wireless Networks

Motivated by emerging vision-based intelligent services, we consider the problem of rate adaptation for high-quality and low-delay visual information delivery over wireless networks using scalable video coding. Rate adaptation in this setting is inherently challenging due to the interplay between the variability of the wireless channels, the queuing at the network nodes, and the frame-based decoding and playback of the video content at the receiver at very short time scales. To address the problem, we propose a low-complexity model-based rate adaptation algorithm for scalable video streaming systems, building on a novel performance model based on stochastic network calculus. We validate the analytic model using extensive simulations. We show that it allows fast near-optimal rate adaptation for fixed transmission paths, as well as cross-layer optimized routing and video rate adaptation in mesh networks, with less than $\text{10}$ % quality degradation compared to the best achievable performance.

Performance optimisation of control channel in ForCES‐based software defined network

The traditional network architecture cannot meet the growing needs of increasingly diverse network applications. The new generation network architectures, software defined network (SDN) which have features of high openness, flexibility, scalability and high controllability have widely been studied. However, a series of new problems are also produced in this new architecture, such as security problem resulted from the high openness, and performance problem resulted from the high flexibility. Currently, the researchers are mainly concentrated in the implementation and standardisation of SDN. There is no good approach to evaluate and optimise the system performance. This studies the performance problems unresolved in SDN research area, and proposed an idea and method to analyse the control channel of ForCES (Forwarding and Control Element Separation)-based SDN by using the stochastic network calculus theory. First, the architecture of ForCES-based SDN is introduced. Then, the performance model of the channel is given. Based on the model, an optimisation method to performance is detailed. This study also gives a simulation by using NS-2 (Network Simulator, Version 2) to verify the correctness and reliability of the performance model.

End-to-End Delay Bound Analysis for Location-Based Routing in Hybrid Vehicular Networks

There is an ongoing debate in the research and industry communities as to whether IEEE 802.11p or Third-Generation Partnership Project (3GPP) Long-Term Evolution (LTE) should be used for vehicular communications. In this paper, we argue that a hybrid vehicular network combining both technologies can increase the performance of the system. We first propose a mechanism to improve location-based routing in a hybrid vehicular network architecture by data and signaling traffic separation on independent wireless networks. We then develop analytical models to calculate the stochastic upper bound of the end-to-end delay (E2ED) for location-based routing in three different networking architecture alternatives based on a) short-range ad hoc only, b) cellular only, and c) the proposed hybrid ad hoc/cellular network. The analytical approach in this paper is based on the stochastic network calculus (SNC) theory, which provides a solid and uniform framework for analysis of the upper bound of the E2ED in communication networks. It is demonstrated that the proposed hybrid network provides a lower E2ED compared with the other two alternatives. Comparisons of realistic simulation results, carried out in NS-3, and analytical results show that the proposed delay bounds provide relatively tight approximations for the E2ED in the three alternative architectures for vehicular networks investigated in this paper.

Nonasymptotic Multicast Throughput and Delay in Multihop Wireless Networks

Previous works on multicast capacity mainly focus on deriving asymptotic order results in large-scale wireless networks. While they can explore the general scaling laws of throughput capacity, it is also of great interest for practical concern to predict the exact achievable throughput in networks with an arbitrary finite number of nodes. In this paper, we investigate the nonasymptotic throughput and delay of multihop wireless networks for multicast applications wherein for each source node, $k$ nodes are randomly selected as receivers. It is challenging for the exact performance analysis since multicast transmission has a dynamic nature due to the following factors: 1) random distribution of nodes; 2) bursty traffic arrivals; and 3) different timescales for transient analysis. To tackle the problem, we propose an explicit analytical model and develop a multicast routing scheme, which accounts for these aspects. With our proposed model, we derive lower bound (LB) and upper bound (UB) on nonasymptotic multicast throughput and delay using stochastic network calculus. We show that the performance results hold for all timescales and network sizes and are strongly correlated to data burstiness and the number of receivers. While we investigate from a nonasymptotic point of view, our results can also cover the asymptotic scaling laws. Simulations are conducted to further verify the accuracy of the analytical bounds.

Stochastic Network Calculus for Rician Fading in Underwater Wireless Networks

Stochastic Network Calculus for Rician Fading in Underwater Wireless Networks

Network Calculus Analysis of a Feedback System with Random Service

Feedback mechanisms are integral components of network protocols and traffic control algorithms. Their performance evaluation is hard due to intricate time correlations introduced by feedback. Network calculus has been successfully applied for the analysis of feedback mechanisms in deterministic systems. However, an extension to random systems has remained an open problem for more than a decade. We present a stochastic network calculus analysis of a random system with feedback, specifically, a window flow control system with random service and fixed feedback delay. We quantify the service impediment due to the feedback mechanism by deriving statistical lower bounds on the available service, and obtain complementary upper bounds. We also discover special cases where an exact description of the service is feasible.

Performance evaluation for SDN deployment: an approach based on stochastic network calculus

The OpenFlow implementations (SDNs) have been deployed increasingly on varieties of networks in research institutions as well as commercial institutions. To develop an OpenFlow implementation, it is required to understand the performance of the network. A few benchmark tools (e.g., Cbench and OFlops) can be used to measure the network performance, while these tools take considerable time to simulate traffic behaviors and generate the required results, therefore extending the development time. In this paper, we present an analytical model, which is based on stochastic network calculus theory, for evaluating the performance of switch to controller. The previous studies show that stochastic network calculus can provide realistic emulation of real network traffic behaviors. Our model is evaluated by using both simulation tool and realistic testbed. The results show the stochastic network calculus based analysis model can realistically measure the network performance of the end-to-end properties between controller and switch.

Stochastic Delay Analysis for Train Control Services in Next-Generation High-Speed Railway Communications System

The communication delay of train control services has a great impact on the track utilization and speed profile of high-speed trains. This paper undertakes stochastic delay analysis of train control services over a high-speed railway fading channel using stochastic network calculus. The mobility model of high-speed railway communications system is formulated as a semi-Markov process. Accordingly, the instantaneous data rate of the wireless channel is characterized by a semi-Markov modulated process, which takes into account the channel variations due to both large- and small-scale fading effects. The stochastic service curve of high-speed railway communications system is derived based on the semi-Markov modulated process. Based on the analytical approach of stochastic network calculus, the stochastic upper delay bounds of train control services are derived with both the moment generating function method and the complementary cumulative distribution function method. The analytical results of the two methods are compared and validated by simulation.

Deterministic and Stochastic Performance Analysis for Real-Time Avionics Networks

With the emergence of the new generation of avionics systems characterized by more complex architectures and much richer functionalities, traditional avionics networks can no longer supply comprehensive interactions between avionics sub- systems. The AFDX (Avionic Full Duplex Switched Ethernet) is recognized as one of the appropriate solutions allowing bridging this gap. As a real-time network, the AFDX is sensitive to data transmission delay. Hence, reliability assessment represents an important issue in the design of such networks. In this paper, the AFDX protocol with SP-FIFO scheduling policy is modeled based on the theory of network calculus. A worst case upper bound on deterministic delay is obtained. Moreover, the delay distribution is modeled and a probabilistic delay upper bound is computed using the stochastic network calculus. Experiments are carried out for both deterministic and probabilistic cases and the results conformed the validity of the approaches developed in the present work.

Delay-bounded resource allocation for femtocells exploiting the statistical multiplexing gain

Femtocell is an efficient solution for mobile operators to expand indoor coverage and increase network capacity. In this paper, we study the downlink resource allocation problem of two-tier macrocell---femtocell networks. We first formulate the problem as a Mixed Integer Non-Linear Program (MINLP) which aims to maximize the capacity of clustered femtocell networks subject to hard delay constraints of flows with different priorities. Next, we build $$(\rho (\theta ),\sigma (\theta ))$$(?(?),?(?)) arrival model for the traffics and apply Stochastic Network Calculus (SNC) to transforming the delay constraints into alternative minimum transmission rate requirements, then we propose a resource allocation algorithm called S-SAPCS to solve the MINLP. Simulation results show that the proposed algorithm has near-optimal performance. We also design a scheme based on deterministic network calculus to show that S-SAPCS is able to exploit the statistical multiplexing gain among multiple flows, which improves the throughput significantly.

On the Throughput of Hybrid-ARQ Under Statistical Queuing Constraints

Hybrid automatic repeat request (HARQ) is a high-performance communication protocol, leading to the effective use of the wireless channel and the resources with only limited feedback about the channel state information (CSI) to the transmitter. In this paper, the throughput of HARQ with incremental redundancy (IR) and fixed transmission rate is studied in the presence of statistical queuing constraints imposed as limitations on buffer overflow probabilities. In particular, tools from the theory of renewal processes and stochastic network calculus are employed to characterize the maximum arrival rates that can be supported by the wireless channel when HARQ-IR is adopted. Effective capacity is employed as the throughput metric, and a closed-form expression for the effective capacity of HARQ-IR is determined for small values of the quality-of-service (QoS) exponent. The impact of the fixed transmission rate, queuing constraints, and hard-deadline limitations on the throughput is investigated, and comparisons with type-I HARQ and HARQ with Chase Combining are provided.

A delay reliability estimation method for Avionics Full Duplex Switched Ethernet based on stochastic network calculus

A delay reliability estimation method for Avionics Full Duplex Switched Ethernet based on stochastic network calculus

Stochastic backlog and delay bounds of generic rate-based AIMD congestion control scheme in cognitive radio sensor networks

Performance guarantees for congestion control schemes in cognitive radio sensor networks (CRSNs) can be helpful in order to satisfy the quality of service (QoS) in different applications. Because of the high dynamicity of available bandwidth and network resources in CRSNs, it is more effective to use the stochastic guarantees. In this paper, the stochastic backlog and delay bounds of generic rate-based additive increase and multiplicative decrease (AIMD) congestion control scheme are modeled based on stochastic network calculus (SNC). Particularly, the probabilistic bounds are modeled through moment generating function (MGF)-based SNC with regard to the sending rate distribution of CR source sensors. The proposed stochastic bounds are verified through NS2-based simulations.

A Guide to the Stochastic Network Calculus

The aim of the stochastic network calculus is to comprehend statistical multiplexing and scheduling of non-trivial traffic sources in a framework for end-to-end analysis of multi-node networks. To date, several models, some of them with subtle yet important differences, have been explored to achieve these objectives. Capitalizing on previous works, this paper contributes an intuitive approach to the stochastic network calculus, where we seek to obtain its fundamental results in the possibly easiest way. In detail, the method that is assembled in this work uses moment generating functions, known from the theory of effective bandwidths, to characterize traffic arrivals and network service. Thereof, affine envelope functions with an exponentially decaying overflow profile are derived to compute statistical end-to-end backlog and delay bounds for networks.

Using demisubmartingales for the stochastic analysis of networks

Stochastic network calculus is the probabilistic version of the network calculus, which uses envelopes to perform probabilistic analysis of queueing networks. The accuracy of probabilistic end-to-end delay or backlog bounds computed using network calculus has always been a concern. In this article, we propose novel end-to-end probabilistic bounds based on demimartingale inequalities which improve the existing bounds for the tandem networks of GI/GI/1 queues. In particular, we show that reasonably accurate bounds are achieved by comparing the new bounds with the existing results for a network of M/M/1 queues.