Martin Dräxler

CROWD: An SDN Approach for DenseNets

Traffic demands in mobile networks are expected to grow substantially in the next years, both in terms of total traffic volume and of bit-rate required by individual users. It is generally agreed that the only possible solution to overcome the current limitations is to deploy very dense and heterogeneous wireless networks, which we call DenseNets. However, simply scaling down existing networks by orders of magnitude, as required to fulfill traffic forecasts, is not possible because of the following constraints: i) the bottleneck would shift from the Radio Access Network (RAN) to the backhaul, ii) control overhead, especially related to mobility management, would make the network collapse, iii) operational costs of the network would be unbearable due to energy consumption and maintenance/optimisation. In this paper, Software Defined Network (SDN) for mobile networks is claimed as the paradigm shift necessary to tackle adequately the above challenges. A novel architecture is proposed, which supports DenseNets made of overlapping LTE and WLAN cells connected to the core network via a reconfigurable backhaul.

MaxiNet: Distributed emulation of software-defined networks

Network emulations are widely used for testing novel network protocols and routing algorithms in realistic scenarios. Up to now, there is no emulation tool that is able to emulate large software-defined data center networks that consist of several thousand nodes. Mininet is the most common tool to emulate Software-Defined Networks of several hundred nodes. We extend Mininet to span an emulated network over several physical machines, making it possible to emulate networks of several thousand nodes on just a handful of physical machines. This enables us to emulate, e.g., large data center networks. To test this approach, we additionally introduce a traffic generator for data center traffic. Since there are no data center traffic traces publicly available we use the results of two recent traffic studies to create synthetic traffic. We show the design and discuss some challenges we had in building our traffic generator. As a showcase for our work we emulated a data center consisting of 3200 hosts on a cluster of only 12 physical machines. We show the resulting workloads and the trade-offs involved.

Efficiency of On-Path and Off-Path Caching Strategies in Information Centric Networks

Architectures for information-centric networks (ICNs), like CCN, propose simple on-path caching with LRU cache management for caching of data items in the network. We propose alternative strategies, based on off-path caching, which try to avoid redundant caching of data items to use cache space more efficiently. Based on a mathematical model for LRU and LFU cache management and a simulation, we analyze these strategies with respect to hit efficiency, delay, and power consumption and provide a competitive evaluation to asses the potential increase of energy efficiency with off-path caching.

Cross-layer scheduling for multi-quality video streaming in cellular wireless networks

Todays video delivery solutions for mobile terminals often use the HTTP Live Streaming (HLS) protocol, which has two interesting features: firstly, the video is divided into playable segments of a certain length, which allows to download and buffer segments before they are required for playback. And secondly, those segments can be available in different quality levels, which can be selected according to the available transmission capacity. Combining these features with scheduling and channel capacity prediction of wireless transmissions into a cross-layer scheduling approach could improve the QoE for users by reducing playback interruptions and by providing the best possible video quality depending on the users wireless channel capacity. In this paper we propose a mixed integer quadratically constrained program (MIQCP) to implement the aforementioned cross-layer scheduling approach. We evaluate its performance compared to greedy strategies to assess the potential for a future polynomial time implementation.

SmarterPhones: Anticipatory download scheduling for wireless video streaming

Video streaming is in high demand by mobile users. In cellular networks, however, the unreliable wireless channel leads to two major problems. Poor channel states degrade video quality and interrupt the playback when a user cannot sufficiently fill its local playout buffer: buffer underruns occur. In contrast, good channel conditions cause common greedy buffering schemes to buffer too much data. Such over-buffering wastes expensive wireless channel capacity. Assuming that we can anticipate future data rates, we plan the quality and download time of video segments ahead. This anticipatory download scheduling avoids buffer underruns by downloading a large number of segments before a drop in available data rate occurs, without wasting wireless capacity by excessive buffering. We developed a practical anticipatory scheduling algorithm for segmented video streaming protocols (e.g., HLS or MPEG DASH). Simulation results and testbed measurements show that our solution essentially eliminates playback interruptions without significantly decreasing video quality.

Cooperating base station set selection and network reconfiguration in limited backhaul networks

Managing interference by Coordinated Multi-Point (CoMP) transmission/reception is an effective mechanism to achieve high data rates in future cellular networks, like Long Term Evolution (LTE)-Advanced. For CoMP, sets of Base Stations (BSs) have to be selected to jointly serve User Equipments (UEs). These sets are typically selected based on wireless characteristics only. However, using CoMP also poses strict capacity and latency requirements on the backhaul network, which are difficult to fulfill even with future optical technologies. Hence, these requirements additionally need to be taken into account when deciding which BSs jointly serve a given UE. We have developed a BSs selection heuristic for CoMP that takes into account both aspects: the wireless channels and the backhaul network status. This heuristic can also identify, for a particular wireless channel situation, which bottlenecks in the backhaul network make a desired BSs selection infeasible. We exploit this to dynamically adapt the backhaul network to the wireless requirements. We call this network reconfiguration. Our simulations show that the heuristics solution quality is close to the optimum while execution time and memory consumption are reduced by multiple orders of magnitude compared to solving the problem via mathematical optimization. This allows real-world deployment of the heuristic. In addition, we simulate the network reconfiguration in a future backhaul network scenario based on Passive Optical Networks (PONs). The results illustrate how our approach helps to better exploit available backhaul resources.

Flow Synchronization for Network Coding

Network Coding (NC) is a means to improve network performance in various ways. Most evaluations so far were done with simplified assumptions about the application scenario, namely equal data rates and packet sizes for traffic to be encoded. Traffic in real networks, however, does not have this property. Hence, as deterministic and random NC require these properties, flows have to be synchronized prior to encoding to guarantee these properties and to be able to benefit from NC in real networks. In this paper, we present a set of algorithms that synchronize arbitrary flows in wired and wireless scenarios for joint encoding later on. These algorithms are based on fragmentation and Active Queue Management (AQM) techniques. To demonstrate the benefits of our approach, we developed an encoder and decoder for deterministic XOR NC that uses this synchronization technique. Simulation results show that with our synchronization techniques, NC, even in scenarios with bursty, self-similar traffic where NC could not have been deployed so far, increases throughput and lowers packet loss and variance of end-to-end delay compared to plain forwarding.

Dynamic network reconfiguration in wireless DenseNets with the CROWD SDN architecture

Traffic in wireless access networks has been growing substantially in the recent years, both in terms of total volume and data rate required by individual users. The commonly agreed solution to overcome the current limitations of wireless access networks is to deploy very dense and heterogeneous wireless networks, the so-called DenseNets. Simply scaling existing networks by orders of magnitude, as required to fulfill traffic forecasts, would bring along several problems because of the limited backhaul capacity, the increased energy consumption, and the explosion of signalling. Hence, the FP7 project CROWD proposes a novel architecture for DenseNets as a solution to tame the density of wireless networks. Within this architecture, flexible flow processing-aware controller placement supported by dynamic backhaul reconfiguration is the crucial component to both (i) maximize the amount of users that can be served simultaneously under high load, and (ii) minimize energy consumption and reduce costs for service providers. We present both approaches in detail and outline how they are integrated into the overall CROWD architecture.

Anticipatory power cycling of mobile network equipment for high demand multimedia traffic

The increasing energy demand of mobile phone networks is a problem for the environment and a cost factor for mobile network operators. Multimedia content is increasingly popular, demands high data rates, and increases energy usage of mobile phone networks. Thus it is important to reduce energy usage of mobile phone networks while serving multimedia content with a high quality of experience (QoE). We developed an approach to power-cycle base stations and control the playback of video streams while reducing the energy consumption and not decreasing the QoE for users. We present two implementations: an optimization problem and an iterative algorithm. Up to 80% of energy can be saved by combining power cycling and video stream controlling in low-load situations. With increasing load, the energy consumption also increases while the QoE can still be improved.

Tackling the Increased Density of 5G Networks: The CROWD Approach

The significant growth in mobile data traffic and the ever- increasing users demand for high-speed, always connected networks continue challenging network providers and lead research towards solutions to enable faster, scalable and more flexible networks. In this paper we present the CROWD approach, a networking framework providing mechanisms to tackle the high densification and heterogeneity of wireless networks. The goal of CROWD is to design protocols and algorithms for very dense and heterogeneous wireless networks, which we call DenseNets. The mechanisms we propose include energy efficiency, MAC enhancements, connectivity management and backhaul configuration to contribute to the next generation of networks considering density as a resource instead of as an obstacle.