A case for evaluating backpressure routing using Ns-3 emulation in a WiFi mesh testbed

Nunez-Martinez, Jose; Mangues‐Bafalluy, Josep

doi:10.1145/2348688.2348696

Cited by 4 publications

(8 citation statements)

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“…Their test-bed consisted of a set of scripts running Linux Containers, providing a way to start the service discovery protocol. NS-3 emulation is also used in [18] for the evaluation of a distributed back pressure routing protocol for WMNs. Emulation is also used in one of our previous work for prototypic telematic services, such as safety applications or location based services, and for evaluating how these services influence the underlying network infrastructure on top of which they operate [19].…”

Section: A Emulation Conceptmentioning

confidence: 99%

Increasing User Perceived Quality by Selective Load Balancing of Video Traffic in Wireless Networks

Hava

Ghamri‐Doudane

Muntean

et al. 2015

IEEE Trans. on Broadcast.

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Wireless mesh networks (WMNs) are becoming increasingly popular mostly due to their deployment flexibility. The main drawback of these networks is their lack of guaranteeing high Quality of Service (QoS) levels to their clients. The latest ubiquitous mobile and wireless support and significant growth in smartphone features have fueled user demand for rich media services on their devices. Unfortunately, this traffic increase puts additional pressure on WMN resources, eventually affecting user QoS levels and providing solutions to address this is highly challenging. This paper shows how by using ViLBaS, an innovative selective load balancing solution for video deliveries in WMN, increased QoS levels of remotely transmitted video are obtained. ViLBaS employs distributed monitoring of network traffic, identifies the node most affected by congestion and prevents imminent packet drops by rerouting the video flows around the congested node. A hybrid simulation-emulation-based test-bed is built and used for assessing ViLBaS performance in comparison with clas-sic solutions employing the best-known routing metrics. Real video traffic was transmitted from a sever to a client over a WMN topology and the received video quality was assessed in different scenarios. The results demonstrate that ViLBaS outperforms all other solutions when delivering various video content with diverse characteristics and at different quality levels.

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Section: A Emulation Conceptmentioning

confidence: 99%

Increasing User Perceived Quality by Selective Load Balancing of Video Traffic in Wireless Networks

Hava

Ghamri‐Doudane

Muntean

et al. 2015

IEEE Trans. on Broadcast.

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“…Specifically [6] focuses on multi-gateway SC deployments, whereas in [7] the focus is on sparse wireless mesh backhaul deployments. Additionally, in [9] we presented the integration details and preliminary experimental results of our scheme using ns-3 emulation features (see section 2). However, the evaluation in [6] was merely 30 based on simulations in a single-radio single-channel backhaul deployments, and the experimental evaluation in [9] was scarce.…”

Section: Introductionmentioning

confidence: 99%

Experimental evaluation of self-organized backpressure routing in a wireless mesh backhaul of small cells

2015

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Small cells (SC) are low-power base stations designed to cope with the anticipated huge traffic growth of mobile communications. These increasing capacity requirements require the corresponding backhaul capacity to transport traffic from/to the core network. Since it is unlikely that fiber reaches every SC, a wireless mesh backhaul amongst SCs is expected to become popular. These low-cost deployments require to balance resource consumption amongst SCs, however, current routing protocols were not designed to fulfill this requirement.To tackle this challenge, we presented and developed with ns-3 a self-organized backpressure routing protocol (BP), designed to make the most out of the backhaul resources. This paper provides the evaluation of BP exploiting built in ns-3 emulation features to allow rapid prototyping under real-world conditions and through controlled ns-3 simulations. Through a novel evaluation methodology based on ns-3 emulation, we evaluate BP in a 12 SC indoor wireless mesh backhaul testbed under different wireless link rates and topologies, showing Packet Delivery Ratio (PDR) gains of up to 50% with respect to shortest path (SP). Through simulations, we show BP scalability properties with both the size of the backhaul and the number of backhaul radios per SC. Results in single-and multi-radio deployments show TCP traffic gains with BP of up to 79% and 95% ✩ Fully documented templates are available in the elsarticle package on CTAN.compared to SP in terms of throughput and latency, respectively. 65 the ns-3 simulator [8] provides emulation capabilities for evaluating network protocols in real testbeds [13]. Figure 2(a) illustrates the main architecture of a node (in this case a SC) running ns-3 emulation. The ns-3 system allows executing ns-3 IP stacks over real physical devices. In particular, the Emu-NetDevice class provides the interface to send/receive packets to/from the real 70 physical device underneath through a raw socket. The EmuNetDevice lying on

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“…6. We demonstrated by conducting real experiments in a testbed [14][15][16] the performance of the resulting routing policy. Experimental results and consequent discussion detailed in in chapter 10 confirm the properties showed by our proposed mechanism through simulation in chapter 8.…”

Section: Contributionsmentioning

confidence: 99%

“…This chapter describes the implementation of the Lyapunov drift-plus-penalty routing scheme described in previous chapters to obtain real measurements in a real wireless mesh backhaul testbed. The contributions of this chapter have been published in [13] and [15]. The goal is to minimize the time devoted to deploy the protocol in the testbed.…”

Section: Proof-of-concept Implementationmentioning

confidence: 99%

“…The goal in this chapter is to use the ns-3 emulation framework, which has been completely re-designed with respect to previous versions of the ns simulator for the evaluation of wireless routing protocols. In contrast, in this work, initiated in [15], we want to allow for rapid prototyping of protocols for wireless networks so that the same (or almost the same) code could be run in a real wireless mesh backhaul testbed, and in the simulator. As for the latter, Network Simulation Cradle (NSC) [117] is a pioneer of introducing real world stacks (e.g., Linux stacks) into the network simulators.…”

Section: Background On Emulationmentioning

confidence: 99%

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Self-organized backpressure routing for the wireless mesh backhaul of small cells

Núñez Martínez

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The ever increasing demand for wireless data services has given a starring role to dense small cell (SC) deployments for mobile networks, as increasing frequency re-use by reducing cell size has historically been the most effective and simple way to increase capacity. Such densification entails challenges at the Transport Network Layer (TNL), which carries packets throughout the network, since hard-wired deployments of small cells prove to be cost-unfeasible and inflexible in some scenarios. The goal of this thesis is, precisely, to provide cost-effective and dynamic solutions for the TNL that drastically improve the performance of dense and semi-planned SC deployments. One approach to decrease costs and augment the dynamicity at the TNL is the creation of a wireless mesh backhaul amongst SCs to carry control and data plane traffic towards/from the core network. Unfortunately, these lowcost SC deployments preclude the use of current TNL routing approaches such as Multiprotocol Label Switching Traffic Profile (MPLS-TP), which was originally designed for hard-wired SC deployments. In particular, one of the main problems is that these schemes are unable to provide an even network resource consumption, which in wireless environments can lead to a substantial degradation of key network performance metrics for Mobile Network Operators. The equivalent of distributing load across resources in SC deployments is making better use of available paths, and so exploiting the capacity offered by the wireless mesh backhaul formed amongst SCs. To tackle such uneven consumption of network resources, this thesis presents the design, implementation, and extensive evaluation of a self-organized backpressure routing protocol explicitly designed for the wireless mesh backhaul formed amongst the wireless links of SCs. Whilst backpressure routing in theory promises throughput optimality, its implementation complexity introduces several concerns, such as scalability, large end-to-end latencies, and centralization of all the network state. To address these issues, we present a throughput suboptimal yet scalable, decentralized, low-overhead, and low-complexity backpressure routing scheme. More specifically, the contributions in this thesis can be summarized as follows: We formulate the routing problem for the wireless mesh backhaul from a stochastic network optimization perspective, and solve the network optimization problem using the Lyapunov-driftplus-penalty method. The Lyapunov drift refers to the difference of queue backlogs in the network between different time instants, whereas the penalty refers to the routing cost incurred by some network utility parameter to optimize. In our case, this parameter is based on minimizing the length of the path taken by packets to reach their intended destination. Rather than building routing tables, we leverage geolocation information as a key component to complement the minimization of the Lyapunov drift in a decentralized way. In fact, we observed that the combination of both components helps to mitigate backpressure limitations (e.g., scalability,centralization, and large end-to-end latencies). The drift-plus-penalty method uses a tunable optimization parameter that weight the relative importance of queue drift and routing cost. We find evidence that, in fact, this optimization parameter impacts the overall network performance. In light of this observation, we propose a self-organized controller based on locally available information and in the current packet being routed to tune such an optimization parameter under dynamic traffic demands. Thus, the goal of this heuristically built controller is to maintain the best trade-off between the Lyapunov drift and the penalty function to take into account the dynamic nature of semi-planned SC deployments. We propose low complexity heuristics to address problems that appear under different wireless mesh backhaul scenarios and conditions...

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A case for evaluating backpressure routing using Ns-3 emulation in a WiFi mesh testbed

Cited by 4 publications

References 5 publications

Increasing User Perceived Quality by Selective Load Balancing of Video Traffic in Wireless Networks

Increasing User Perceived Quality by Selective Load Balancing of Video Traffic in Wireless Networks

Experimental evaluation of self-organized backpressure routing in a wireless mesh backhaul of small cells

Self-organized backpressure routing for the wireless mesh backhaul of small cells

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