Sending Probe Packets Research Articles

Using Software-Defined Networking for Data Traffic Control in Smart Cities with WiFi Coverage

The growth of smart cities is fueled by the vast rise in wireless smart gadgets and uninterrupted connectivity. WiFi is the dominant wireless technology, enabling Internet-of-Things (IoT) connectivity in smart cities due to its ubiquitous access points and low deployment cost. However, smart city applications offer a wide range of services with different quality-of-service (QoS) demands. This paper addresses packet delivery latency as one of the QoS metrics affecting many time-sensitive smart city services. Thus, the paper proposes employing software-defined networking (SDN) to control the traffic load of WiFi access points (APs), preserving its symmetry in a city-wide coverage of WiFi-connected IoT gateways or fog nodes. These gateways receive data packets from smart city/IoT devices via wireless links and forward them over a city-deployed WiFi network to their management entities or servers. Three SDN-based algorithms are devised to reduce the gateways’ packet-forwarding delay and keep a symmetric traffic load at the WiFi network APs. The algorithms are developed and tested using a real hardware setup constituting WiFi devices without additional requirements on the IoT gateways (WiFi clients) or the APs, such as support for a specific roaming protocol or bandwidth-consuming signaling such as sending probe packets. Extensive hardware experimentation shows that the SDN controller, via the proposed algorithms, can effectively reduce the packet forwarding latency of IoT gateways by carefully selecting the IoT gateway with the highest packet latency and seamlessly handing it over to the least-loaded covering AP.

Achieving high utilization of flowlet-based load balancing in data center networks

Modern data center networks provide multiple paths between any host pairs. Load balancing traffic across these paths is critical to achieve low latency and high throughput. Despite prior solutions show that flowlet-based solutions are powerful in achieving load balancing in asymmetric topology, they suffer from the congestion mismatch problem in rerouting flowlets across different paths. That is, due to lacking the explicit congestion feedback, existing flowlet-based solutions are difficult to utilize bandwidth resource efficiently, which may result in low link utilization and packet loss. In this paper, we propose a congestion-aware load balancing scheme named CAF to eliminate the problem of congestion mismatch. The basic idea behind CAF is that whenever the sender perceives flowlet switching, it proactively sends probe packets to measure available bandwidth and uses the measurement result to properly set the congestion window, avoiding the unnecessary under-utilization and packet loss. Through a series of large-scale NS2 simulations and testbed experiments, we demonstrate that CAF reduces average flow completion time by up to 86% compared with the state-of-the-art mechanisms.

Time‐varying network link loss rate tracking from unicast end‐to‐end measurements

SummaryTime‐varying network link loss rate is a useful information for network managers to discover and locate the network link failures. This paper proposes a method to track time‐varying network link loss rates from unicast end‐to‐end measurements. The method first trains a state transition matrix to capture the spatio‐temporal characters of packet link transmission probabilities by sending end‐to‐end probe packets and then estimates the time‐varying link loss rates using the state transition matrix and the end‐to‐end measurements obtained from background traffic (the existed packets in network). We also introduce a validation step to check and retrain the state transition matrix in order to ensure the accuracy of the state transition matrix. Our method is capable of tracking the variation of link loss rates without incessantly sending probe packets, which is very feasible for many practical applications. The ns‐2 simulation results show the good performance of our method.

Transport mechanism for streaming media in wireless network

A novel transport protocol Wireless Multimedia Transport Congestion Control (WMTCC) to was presented to improve the performance of streaming media transmission over wireless networks. The proposed protocol can distinguish packet loss due to network congestion from packet loss due to wireless link error by sending probing packets. In this way the sender can get the accurate state of the networks and adjust the transmission rate appropriately to maintain the Quality of Service (QoS) of the streaming media services. Due to the accurate distinguishing of the packet loss between network congestion and wireless link error, the protocol can maintain high throughput even with high bit error rate. The simulation results show that the new protocol can get higher throughput and larger congestion window in wireless networks and performs consistently better than other existing mechanisms.

Estimation of available bandwidth for an [formula omitted] queueing system

This paper is concerned with estimating the available bandwidth, the residual processing capacity, of a single-server queueing system whose service rate and the input traffic load are not known in advance. We analyze the minimal-backlogging method which has been proposed to estimate the available bandwidth by sending probing packets. We show that an M / G / 1 queueing system probed by the minimal-backlogging method is stable. A consistent estimator of the inverse of available bandwidth is proposed based on the sojourn times of the probing packets. We also estimate the available bandwidth of a local server which is connected to the probing node with non-zero delay as an application of the theory developed for a single-server queue. We evaluate the accuracy of the proposed available bandwidth estimation scheme by simulation.

Proactive Retransmission and Buffer Management for Layered Video Transmission over Wireless Channel

Hybrid FEC/ARQ, which is a mixture of forward error correction (FEC) and automatic repeat request (ARQ), is a well-known technique aiming for packet-loss recovery to guarantee quality of service (QoS) for real-time communications. In this paper, focusing on layered video transmission over wireless network environment, we propose a proactive retransmission scheme for hybrid FEC/ARQ. In the proposed scheme, a receiver host periodically sends probe packets to a sender host in order to check wireless channel state. If the sender host does not receive any probe packet during a pre-specified interval, it regards the wireless channel as being in burst loss state, and it proactively retransmits packets expected to be lost during the burst loss period. The buffer management associated with layered video coding is also taken into consideration. The performance of the proposed scheme is investigated by simulation. Numerical examples show that the proposed scheme transmits packets of the base layer more successfully than the conventional FEC/ARQ.

Hop-by-hop versus end-to-end active measurements

In packet-switching networks, some performance characteristics can be estimated by end-to-end active measurements, i.e. sending probe packets from end hosts. There are different end-to-end measurement techniques to estimate one-way delay and different types of bandwidth. Nevertheless they do not always give accurate results, only work under limited conditions and have disadvantages which we summarize based on several research works [3,10,12]. We propose hop-by-hop active measurement methods to measure bandwidth and one-way delay. The concept of proposed hop-by-hop methods is that not only the end hosts, but nodes along a network path are also involved in measurements. In this paper, new necessary services are also proposed, with which we can apply hop-by-hop methods. Our performance evaluation shows that despite having to implement new services in every node, these services help network performance analysts and applications measure bandwidth and one-way delay faster and more accurately. Our work is an attempt to take part in creating and deploying infrastructure to support better measurement on the Internet.