Efficient epidemic multicast in heterogeneous networks

The scalability and resilience of epidemic multicast, also called probabilistic or gossip-based multicast, rests on its symmetry: Each participant node contributes the same share of bandwidth thus spreading the load and allowing for redundancy. On the other hand, the symmetry of gossiping means that it does not avoid nodes or links with less capacity. Unfortunately, one cannot naively avoid such symmetry without also endangering scalability and resilience. In this paper we point out how to break out of this dilemma, by lazily deferring message transmission according to a configurable policy. An experimental proof-of-concept illustrates the approach.Fundação para a Ciência e a Tecnologia (FCT) - Project “P-SON: Probabilistically Structured Overlay Networks” (POS C/EIA/60941/2004)

Performance evaluation of an open distributed platform for realistic traffic generation

Network researchers have dedicated a notable part of their efforts to the area of modeling traffic and to the implementation of efficient traffic generators. We feel that there is a strong demand for traffic generators capable to reproduce realistic traffic patterns according to theoretical models and at the same time with high performance. This work presents an open distributed platform for traffic generation that we called distributed internet traffic generator (D-ITG), capable of producing traffic (network, transport and application layer) at packet level and of accurately replicating appropriate stochastic processes for both inter departure time (IDT) and packet size (PS) random variables. We implemented two different versions of our distributed generator. In the first one, a log server is in charge of recording the information transmitted by senders and receivers and these communications are based either on TCP or UDP. In the other one, senders and receivers make use of the MPI library. In this work a complete performance comparison among the centralized version and the two distributed versions of D-ITG is presented

A Review on Features’ Robustness in High Diversity Mobile Traffic Classifications

Mobile traffics are becoming more dominant due to growing usage of mobile devices and proliferation of IoT. The influx of mobile traffics introduce some new challenges in traffic classifications; namely the diversity complexity and behavioral dynamism complexity. Existing traffic classifications methods are designed for classifying standard protocols and user applications with more deterministic behaviors in small diversity. Currently, flow statistics, payload signature and heuristic traffic attributes are some of the most effective features used to discriminate traffic classes. In this paper, we investigate the correlations of these features to the less-deterministic user application traffic classes based on corresponding classification accuracy. Then, we evaluate the impact of large-scale classification on feature's robustness based on sign of diminishing accuracy. Our experimental results consolidate the needs for unsupervised feature learning to address the dynamism of mobile application behavioral traits for accurate classification on rapidly growing mobile traffics

The Dynamics of Internet Traffic: Self-Similarity, Self-Organization, and Complex Phenomena

The Internet is the most complex system ever created in human history. Therefore, its dynamics and traffic unsurprisingly take on a rich variety of complex dynamics, self-organization, and other phenomena that have been researched for years. This paper is a review of the complex dynamics of Internet traffic. Departing from normal treatises, we will take a view from both the network engineering and physics perspectives showing the strengths and weaknesses as well as insights of both. In addition, many less covered phenomena such as traffic oscillations, large-scale effects of worm traffic, and comparisons of the Internet and biological models will be covered.Comment: 63 pages, 7 figures, 7 tables, submitted to Advances in Complex System

Techniques for Processing TCP/IP Flow Content in Network Switches at Gigabit Line Rates

The growth of the Internet has enabled it to become a critical component used by businesses, governments and individuals. While most of the traffic on the Internet is legitimate, a proportion of the traffic includes worms, computer viruses, network intrusions, computer espionage, security breaches and illegal behavior. This rogue traffic causes computer and network outages, reduces network throughput, and costs governments and companies billions of dollars each year. This dissertation investigates the problems associated with TCP stream processing in high-speed networks. It describes an architecture that simplifies the processing of TCP data streams in these environments and presents a hardware circuit capable of TCP stream processing on multi-gigabit networks for millions of simultaneous network connections. Live Internet traffic is analyzed using this new TCP processing circuit

IEEE 802.11 wireless LAN traffic analysis: a cross-layer approach

textThe deployment of broadband wireless data networks, e.g., wireless local area networks (WLANs) [29], experienced tremendous growth in the last several years, and this trend is continuously gaining momentum. In fact, WLAN is becoming an indispensable component of the modern telecommunication infrastructure. Despite this optimistic outlook, however, little is known about the impact of the wireless channel on the characteristics of WLAN traffic. This dissertation characterizes the correlation structures of WLAN channel with traffic statistics from a cross-layer point of view, and provides new measurement methodologies and statistical models for WLAN networks. Currently WLAN standards are designed within the paradigm of the layered network architecture. For example, the architecture of IEEE 802.11 vii is almost identical to the Ethernet. However, wireless networks are fundamentally different from their wired peers due to the shift of transmission media from cables to over-the-air radio waves. This transition exposes wireless systems to the influence of radio propagation, and more importantly, to the temporal and spacial fluctuations of the radio channel that can actually be propagated up to upper layers. However, the current WLAN architecture isolates network layers, and largely ignores this impact. Therefore, we believe that a cross-layer based approach is necessary to understand and reflect this underlying impact of the channel to the upper layers of the network, especially in relation to WLAN traffic behavior. Measurement is one of the fundamental tools used to quantify radio propagation. As part of this dissertation, a complete framework for a measurement methodology, including hardware, software, and measurement procedures, is established. Characteristics of the propagation channel are estimated from measurement data, and the channel knowledge is applied to the upper layers for more realistic and accurate modeling. In WLAN environments, knowledge of the traffic characteristics is essential for proper network provisioning, and for improving the performance of the IEEE 802.11 standard and network devices, e.g., to design improved MAC schemes, or to build better buffer scheduling algorithms with channel knowledge, etc. Built upon extensive WLAN traffic traces, this dissertation work presents cross-layer models for WLAN throughput predictions, traffic statistics, and link layer characteristics. viii The main goal of this dissertation work is to experiment with and develop new methods for identifying channel characteristics. Thereby utilizing this knowledge, we show how to predict and improve WLAN performance. Within the framework of the developed cross-layer measurement methodology, we conducted extensive measurements in different physical environments and different settings such as office buildings and stores, and (1) show that the impact of the propagation channel can be quantified by using simple large scale channel metric (throughput over longer period of time), and (2) also present the existence of a Doppler effect within today’s WLAN packet traffic at sub-second time scales. We also show the real-world WLAN usage pattern from our measurement results. From this data, we conclude that the key issues to study WLAN networks include accurate site-specific propagation channel modeling and real-time autonomous traffic control.Electrical and Computer Engineerin

SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning

Software-defined networks (SDNs) have the capabilities of controlling the efficient movement of data flows through a network to fulfill sufficient flow management and effective usage of network resources. Currently, most data center networks (DCNs) suffer from the exploitation of network resources by large packets (elephant flow) that enter the network at any time, which affects a particular flow (mice flow). Therefore, it is crucial to find a solution for identifying and finding an appropriate routing path in order to improve the network management system. This work proposes a SDN application to find the best path based on the type of flow using network performance metrics. These metrics are used to characterize and identify flows as elephant and mice by utilizing unsupervised machine learning (ML) and the thresholding method. A developed routing algorithm was proposed to select the path based on the type of flow. A validation test was performed by testing the proposed framework using different topologies of the DCN and comparing the performance of a SDN-Ryu controller with that of the proposed framework based on three factors: throughput, bandwidth, and data transfer rate. The results show that 70% of the time, the proposed framework has higher performance for different types of flows.</jats:p