Characterisation of real GPRS traffic with analytical tools

With GPRS and UMTS networks lunched, wireless multimedia services are commercially becoming the most attractive applications next to voice. Because of the nature of bursty, packet-switched schemes and multiple data rates, the traditional Erlang approach and Poisson models for characterising voice-centric services traffic are not suitable for studying wireless multimedia services traffic. Therefore, research on the characterisation of wireless multimedia services traffic is very challenging. The typical reference for the study of wireless multimedia services traffic is wired Internet services traffic. However, because of the differences in network protocol, bandwidth, and QoS requirements between wired and wireless services, their traffic characterisations may not be similar. Wired network Internet traffic shows self-similarity, long-range dependence and its file sizes exhibit heavy-tailedness. This paper reports the use of existing tools to analyse real GPRS traffic data to establish whether wireless multimedia services traffic have similar properties as wired Internet services traffic

Performance Analysis of the Ethernet under Conditions of Bursty Traffic

In this paper we present a simulation study of the Ethernet performance under conditions of bursty traffic. This study is motivated by two observations: Ethernet will continue to be a widely used Local Area Network (LAN), especially as an access LAN for future high speed internet (or Broadband ISDN); and future high speed applications can best be modeled as bursty sources. Bursty traffic in this study is specified using three parameters: peak bandwidth, average bandwidth, and burst factor. The simulation study shows that the inherent behavior of the Ethernet does not change with bursty traffic. That is, as long as the utilization is less than a threshold value, packet delay, is almost equal to transmission time, queue lengths are minimal, and packet delay, queue lengths, and packet loss rate increase very quickly. Although the basic trend of the Ethernet performance is the same, performance metrics deteriorate faster with bursty traffic. For example, packet loss due to collision, packet delay, and buffer sizes increase with burstiness of traffic sources. The ratio of peak to average bandwidth of traffic sources has an unexpected effect on the packet loss rate and queue lengths. At high utilization, packet loss and queue lengths are less for higher peak-to-average ratio of bursty sources

Scalable BGP Prefix Selection for Effective Inter-domain Traffic Engineering

Inter-domain Traffic Engineering for multi-homed networks faces a scalability challenge, as the size of BGP routing table continue to grow. In this context, the choice of the best path must be made potentially for each destination prefix, requiring all available paths to be characterised (e.g., through measurements) and compared with each other. Fortunately, it is well-known that a few number of prefixes carry the larger part of the traffic. As a natural consequence, to engineer large volume of traffic only few prefixes need to be managed. Yet, traffic characteristics of a given prefix can greatly vary over time, and little is known on the dynamism of traffic at this aggregation level, including predicting the set of the most significant prefixes in the near future. %based on past observations. Sophisticated prediction methods won't scale in such context. In this paper, we study the relationship between prefix volume, stability, and predictability, based on recent traffic traces from nine different networks. Three simple and resource-efficient methods to select the prefixes associated with the most important foreseeable traffic volume are then proposed. Such proposed methods allow to select sets of prefixes with both excellent representativeness (volume coverage) and stability in time, for which the best routes are identified. The analysis carried out confirm the potential benefits of a route decision engine

Performance modelling of a multiple threshold RED mechanism for bursty and correlated Internet traffic with MMPP arrival process

Access to the large web content hosted all over the world by users of the Internet engage many hosts, routers/switches and faster links. They challenge the internet backbone to operate at its capacity to assure e±cient content access. This may result in congestion and raises concerns over various Quality of Service (QoS) issues like high delays, high packet loss and low throughput of the system for various Internet applications. Thus, there is a need to develop effective congestion control mechanisms in order to meet various Quality of Service (QoS) related performance parameters. In this paper, our emphasis is on the Active Queue Management (AQM) mechanisms, particularly Random Early Detection (RED). We propose a threshold based novel analytical model based on standard RED mechanism. Various numerical examples are presented for Internet traffic scenarios containing both the burstiness and correlation properties of the network traffic

Deep Reinforcement Learning for Real-Time Optimization in NB-IoT Networks

NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices with heterogeneous requirements. A configuration specifies the amount of radio resource allocated to each group of devices for random access and for data transmission. Assuming no knowledge of the traffic statistics, there exists an important challenge in "how to determine the configuration that maximizes the long-term average number of served IoT devices at each Transmission Time Interval (TTI) in an online fashion". Given the complexity of searching for optimal configuration, we first develop real-time configuration selection based on the tabular Q-learning (tabular-Q), the Linear Approximation based Q-learning (LA-Q), and the Deep Neural Network based Q-learning (DQN) in the single-parameter single-group scenario. Our results show that the proposed reinforcement learning based approaches considerably outperform the conventional heuristic approaches based on load estimation (LE-URC) in terms of the number of served IoT devices. This result also indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve almost the same performance with much less training time. We further advance LA-Q and DQN via Actions Aggregation (AA-LA-Q and AA-DQN) and via Cooperative Multi-Agent learning (CMA-DQN) for the multi-parameter multi-group scenario, thereby solve the problem that Q-learning agents do not converge in high-dimensional configurations. In this scenario, the superiority of the proposed Q-learning approaches over the conventional LE-URC approach significantly improves with the increase of configuration dimensions, and the CMA-DQN approach outperforms the other approaches in both throughput and training efficiency