WNOS: An Optimization-based Wireless Network Operating System

This article investigates the basic design principles for a new Wireless Network Operating System (WNOS), a radically different approach to software-defined networking (SDN) for infrastructure-less wireless networks. Departing from well-understood approaches inspired by OpenFlow, WNOS provides the network designer with an abstraction hiding (i) the lower-level details of the wireless protocol stack and (ii) the distributed nature of the network operations. Based on this abstract representation, the WNOS takes network control programs written on a centralized, high-level view of the network and automatically generates distributed cross-layer control programs based on distributed optimization theory that are executed by each individual node on an abstract representation of the radio hardware. We first discuss the main architectural principles of WNOS. Then, we discuss a new approach to automatically generate solution algorithms for each of the resulting subproblems in an automated fashion. Finally, we illustrate a prototype implementation of WNOS on software-defined radio devices and test its effectiveness by considering specific cross-layer control problems. Experimental results indicate that, based on the automatically generated distributed control programs, WNOS achieves 18%, 56% and 80.4% utility gain in networks with low, medium and high levels of interference; maybe more importantly, we illustrate how the global network behavior can be controlled by modifying a few lines of code on a centralized abstraction.

[1]  Daniel Pérez Palomar,et al.  A tutorial on decomposition methods for network utility maximization , 2006, IEEE Journal on Selected Areas in Communications.

[2]  Kok-Kiong Yap,et al.  The Stanford OpenRoads deployment , 2009, WINTECH '09.

[3]  Z. Morley Mao,et al.  CellSDN : Software-Defined Cellular Networks , 2012 .

[4]  Ian F. Akyildiz,et al.  A roadmap for traffic engineering in SDN-OpenFlow networks , 2014, Comput. Networks.

[5]  Athanasios V. Vasilakos,et al.  Software-Defined and Virtualized Future Mobile and Wireless Networks: A Survey , 2014, Mobile Networks and Applications.

[6]  Jiannong Cao,et al.  SDN-Based Routing for Efficient Message Propagation in VANET , 2015, WASA.

[7]  A. Robert Calderbank,et al.  Layering As Optimization Decomposition , 2006 .

[8]  Mung Chiang,et al.  Cross-Layer Congestion Control, Routing and Scheduling Design in Ad Hoc Wireless Networks , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[9]  Rob Sherwood,et al.  OpenRoads: empowering research in mobile networks , 2010, CCRV.

[10]  Lei Ding,et al.  Cross-Layer Routing and Dynamic Spectrum Allocation in Cognitive Radio Ad Hoc Networks , 2010, IEEE Transactions on Vehicular Technology.

[11]  Sally A. McKee,et al.  Methods of inference and learning for performance modeling of parallel applications , 2007, PPoPP.

[12]  Tommaso Melodia,et al.  Self-organizing flying drones with massive MIMO networking , 2018, 2018 17th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net).

[13]  Lothar Thiele,et al.  pTunes: runtime parameter adaptation for low-power MAC protocols , 2012, IPSN.

[14]  Pablo Soldati,et al.  Mathematical decomposition techniques for distributed cross-layer optimization of data networks , 2006, IEEE Journal on Selected Areas in Communications.

[15]  Jorge Nocedal,et al.  A trust region method based on interior point techniques for nonlinear programming , 2000, Math. Program..

[16]  Mahdi Aiash,et al.  Software Defined Networking for Wireless Sensor Networks: A Survey , 2017 .

[17]  Laurent Vanbever,et al.  Central Control Over Distributed Routing , 2015, Comput. Commun. Rev..

[18]  Tommaso Melodia,et al.  To Transmit or Not to Transmit? Distributed Queueing Games in Infrastructureless Wireless Networks , 2016, IEEE/ACM Transactions on Networking.

[19]  Jorge Nocedal,et al.  An Interior Point Algorithm for Large-Scale Nonlinear Programming , 1999, SIAM J. Optim..

[20]  Laura Galluccio,et al.  SDN-WISE: Design, prototyping and experimentation of a stateful SDN solution for WIreless SEnsor networks , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[21]  Emrecan Demirors,et al.  RcUBe: Real-time reconfigurable radio framework with self-optimization capabilities , 2015, 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[22]  Larry Carter,et al.  Universal Classes of Hash Functions , 1979, J. Comput. Syst. Sci..

[23]  Philip Levis,et al.  OpenRadio: a programmable wireless dataplane , 2012, HotSDN '12.

[24]  Sampath Rangarajan,et al.  Radio access network virtualization for future mobile carrier networks , 2013, IEEE Communications Magazine.

[25]  Song Guo,et al.  Energy Minimization in Multi-Task Software-Defined Sensor Networks , 2015, IEEE Transactions on Computers.

[26]  Bin Wang,et al.  Software defined collaborative secure ad hoc wireless networks , 2015, 2015 International Conference on Collaboration Technologies and Systems (CTS).

[27]  John N. Tsitsiklis,et al.  Parallel and distributed computation , 1989 .

[28]  Ahmed Karmouch,et al.  Resource Discovery and Allocation in Network Virtualization , 2012, IEEE Communications Surveys & Tutorials.

[29]  Meixia Tao,et al.  An Auction Approach to Distributed Power Allocation for Multiuser Cooperative Networks , 2012, IEEE Transactions on Wireless Communications.

[30]  Dongfeng Yuan,et al.  Distributed Resource Management for Cognitive Ad Hoc Networks With Cooperative Relays , 2016, IEEE/ACM Transactions on Networking.

[31]  Hongqiang Zhai,et al.  Distributed Flow Control and Medium Access in Multihop Ad Hoc Networks , 2006, IEEE Transactions on Mobile Computing.

[32]  Liliana Ironi,et al.  Automated mathematical modeling from experimental data: an application to material science , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[33]  Mung Chiang.  Balancing transport and physical Layers in wireless multihop networks: jointly optimal congestion control and power control , 2005 .

[34]  Francisco Facchinei,et al.  Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems , 2013, IEEE Transactions on Signal Processing.

[35]  Nael B. Abu-Ghazaleh,et al.  Wireless Software Defined Networking: A Survey and Taxonomy , 2016, IEEE Communications Surveys & Tutorials.

[36]  F. Richard Yu,et al.  Wireless Network Virtualization: A Survey, Some Research Issues and Challenges , 2015, IEEE Communications Surveys & Tutorials.

[37]  Hwee Pink Tan,et al.  Sensor OpenFlow: Enabling Software-Defined Wireless Sensor Networks , 2012, IEEE Communications Letters.

[38]  Sachin Katti,et al.  SoftRAN: software defined radio access network , 2013, HotSDN '13.

[39]  Ilenia Tinnirello,et al.  Wireless MAC processors: Programming MAC protocols on commodity Hardware , 2012, 2012 Proceedings IEEE INFOCOM.

[40]  V. Balakrishnan,et al.  Graph Theory , 1997 .

[41]  Nick McKeown,et al.  OpenFlow: enabling innovation in campus networks , 2008, CCRV.

[42]  Song Guo,et al.  A software defined wireless sensor network , 2014, 2014 International Conference on Computing, Networking and Communications (ICNC).

[43]  Wei Ni,et al.  Software-defined wireless networking: centralized, distributed, or hybrid? , 2015, IEEE Network.

[44]  Yong Xiang,et al.  Software-Defined Wireless Networking Opportunities and Challenges for Internet-of-Things: A Review , 2016, IEEE Internet of Things Journal.

[45]  Laura Galluccio,et al.  Exploiting state information to support QoS in Software-Defined WSNs , 2016, 2016 Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net).

[46]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.