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

Abstract

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

Applying backpressure to balance resource usage in software-defined wireless backhauls

“…The backpressure algorithm was afterwards extended by Neely and Modiano [13], proposing to combine the previous scheme with a drift-plus-penalty technique to optimize the performance of wireless multi-hop networks. The theoretical strengths derived from this work have recently increased the interest on practical implementations in the context of wireless multi-hop backhauls [10], [11]. Experimental and simulation results showed that the resulting distributed backpressure routing strategy attains an even network resource consumption in a non-SDN wireless backhaul, where each SC participates in the distributed control plane.…”

“…For this reason, backpressure routing is able to exploit several paths between source and destination SCs. Practical extensions of backpressure has been developed in legacy distributed wireless multi-hop backhauls [10], [11].…”

“…The goal here is to distribute traffic flows to improve the usage of network resources. Different from our previous work [10], [11], where routing decisions were taken on a per-packet granurality, the centralized backpressure policy performs routing decisions with coarser granularity, that is, per-flow statically and on a periodical basis. In the second use case, we propose a distributed backpressure algorithm to balance the processing load among available SDN controllers due to uneven generation of requests by OF switches forming the software-defined wireless backhaul.…”

Proposal and Implementation of MPLS Fuzzy Traffic Monitor

“…For validating incremental progress related to solving the problem of traffic splitting, a comparison between classical approach (OSPF) and proposed work (FLCS) was conducted by multiple experiments using NS-3 [18][19][20][21][22][23][24][25][26]. We have performed a group of simulations in the scenario composed of MPLS-TE together with constraint based routing.…”

Evaluation of hybrid terrestrial-satellite suburban wireless mesh backhauls for LTE networks

“…The relative importance of each component is adjusted dynamically by means of a parameter that finds the best trade-off between congestion avoidance and path length. The design of BP-MR started with the necessity of extending the operation and mitigate the inefficiencies of BP [20], a decentralized flavor of the original backpressure algorithm [9], towards routing LTE traffic (TCP and UDP) through heterogeneus wireless backhauls technologies forming sparse backhauls. To tackle sparse backhauls, Backpressure for Sparse Deployments (BS) [21] added a penalty function able to overcome dead ends in a scalable and decentralized way.…”