Description
Date depot: 1 janvier 1900
Titre: Scalable Online Algorithms for Software Defined Wireless Networks
Directeur de thèse:
Navid NIKAEIN (Eurecom)
Directeur de thèse:
Thrasyvoulos SPYROPOULOS (Eurecom)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Non defini
Resumé:
Software-Defined Networking (SDN) technologies are quickly gaining momentum in the networking industry. By separating the data plane from the control plane, they allow for quick & easy on-the-fly implementation of flow rules in the network routers and switches (see for example: Forces [1], PCEP [2] and OpenFlow [3]). Using such rules, network administrators can go beyond the standard MPLS approaches, engineer the network in a central manner by collecting data and dispatching router commands, and ultimately optimize resource utilization, cost, reliability, and other objectives. Due to this flexibility, SDN is a major technological driving force today. Huawei, is a leading contributor to the open-source ONOS platform for SDN [4], a leading constructor of networking equipment controlled by SDN, and also a technological leader in proposing new algorithms for network optimization.
In the wireless domain, operators are struggling to keep up with the rapidly increasing mobile data traffic, while at the same time facing increasing costs to upgrade to the newer technologies (e.g., LTE/LTE-A) needed to alleviate this data crunch in the short term. As a consequence, the upcoming 5th Generation (5G) networks are considering a number of technological breakthroughs in order to provide the urgently required 1000x performance improvements in the long term. This vision includes aggressive densification (via femto-cells, pico-cells and relays), extensive carrier aggregation and offloading (between carriers and/or RATs), cooperation between BSs (such as Coordinated Multi-Point Transmission/Reception, and eICIC), and massive centralization of baseband processing, especially to facilitate cooperation and optimal resource allocation, in the context of CloudRAN [5].
While of high potential benefit, the above technologies also introduce significant additional complexity for the implementation, reconfiguration, and update of related algorithms. Softwarization and virtualization of the often ossified, highly complex, and expensive Radio Access Network (RAN) architecture via SDN is seen as a necessary step towards 5G networks, often referred to as Software Defined Wireless Networks (SDWN) [6,7]. Radio access, backhaul, and core network programmability will allow easy reconfiguration and tuning, flexible online adaptation of algorithms to react to traffic load shifts, context- and service-specific traffic handling, and the ability to optimally allocate resources when and where needed.
Nevertheless, while centralization of network control increases the flexibility to handle different traffic flows at will, it also raises some performance and scalability concerns. Given the number of users, BSs, access technologies and (virtual) operators sharing a given network, taking all flow decisions at a remote, centralized SDN controller is prohibitive. Furthermore, classical SDN schemes control the network by deciding rules that apply to flows (sequences of packets). This flow-level approach has the advantage of being simple and easy to apply to network routers. Yet, wireless systems are extremely sensitive to time-variations of transmitted signals. Two subsequent packets in the same flow might encounter an entirely different electromagnetic environment. Hence, the benefits of specific optimization algorithms (as, for example opportunistic scheduling [8]) hinge on performing fast operations at packet-level. This might require a reaction from the local network at a time-scale that is much smaller than the time to send relevant information to and receive a decision from a central SDN controller.
Summarizing, SDN promises to facilitate novel, optimal resource allocation algorithms for wireless networks, but wireless optimization is currently performed at a closed-loop involving the communication devices, and at the moment is seems impossible to include in this loop the SDN controller. The motivation for this PhD topic comes from exactly these tradeoffs. Specifically, the thesis has two key goals:
1) To model and understand the performance tradeoffs stemming from the combination of SDN with wireless networks, and the interplay between optimal flow-level rules at the SDN controller with dynamic optimization at packet-level in the wireless communication devices.
2) To use this understanding to propose an optimization framework for scalable online (semi-)distributed SDWN algorithms for efficient resource allocation and traffic steering in dense, heterogeneous 5G SDWNs.
Objectives and research directions
The main objective is to propose flow management algorithms that optimize performance along both short (packet-level) and longer (flow-level) time-scales, taking into account the dynamics of wireless networks and finding an optimal partition or placement of the algorithm between the SDN controller(s) and the local communication devices. Specifically, we will be using techniques inspired from the literature on online and distribu
Doctorant.e: Liakopoulos Nikolaos