Traditional Internet routing is simple, scalable and robust, but cannot provide perfect QoS support due to the current completely distributed hop-by-hop routing architecture. Software defined networking (SDN) opens up the door to traffic engineering innovation and makes possible QoS routing with a broader picture of overall network resources. We further argue that SDN can provide more opportunity for the network users to make their own routing selections with network programmability. In this paper, we propose OpenMCR, a general framework for network users to make their own choice of routing given various requirements. OpenMCR provides routing subject to several additive QoS constraints, which is NP-hard when the number of constraints is two or more. By composing various necessary conditions with different path extension schemes, our platform can customize routing solutions for each network user based on their own requirements. Through experiments in an SDN emulated environment, we evaluate multiple aspects of OpenMCR, demonstrate its effectiveness compared with several baselines and validate our theoretical analysis.

Robustness and reliability are critical issues in network management. To provide resiliency, a popular protection scheme against network failures is simultaneous routing along multiple paths. The use of Software-Defined Networking (SDN), with fine-grained forwarding rules and the limited forwarding table size of commodity switches, brings a new set of constraints and challenges. In this work, we consider the problem of routing demands with dependability in a software-defined ISP backbone. We propose splitting large demands over multiple paths to reduce fragmentation and achieve service reliability under failures. We also minimize the delay differential among these paths to reduce the number of out-of-order packets. Finally, our routing algorithm works with forwarding tables of bounded size, commonly required by low-cost SDN switches. We report emulation and simulation results confirming the practicality of our solutions.

Saving power on base stations (BSs) becomes a critical issue in wireless cellular networks. Many existing work has proposed to schedule BS into sleep to save energy. However, in reality, it is very difficult to shut down and reboot BSs frequently due to numerous technical issues and performance requirements. In this paper, we propose a much more practical solution and offer a new perspective on implementing green wireless networking by embracing the hot-trended small cell network idea. Instead of putting BSs into sleep, we tactically reduce the coverage (and the power usage) of each BS, and strategically place microcells (relay stations) to offload the traffic transmitted to/from BSs in order to save total power consumption. We propose approximation algorithms for various network design scenarios, with different wireless network setups and different power saving optimization objectives. Extensive numerical results are presented to confirm our theoretical analysis.

Inspired by the success of use of Virtual Machines (VMs) in cloud computing, virtualization has been introduced to wireless networking recently, enabling support for multiple Mobile Virtual Network Operators (MVNOs) via isolated slices over a shared wireless substrate. In this paper, we present design, implementation and evaluation of a novel cloud framework, R-Cloud, to enable radio resources at Base Stations (BSs) to be effectively allocated to multiple MVNOs as a service, which is referred to as Radio-as-a-Service (RaaS). R-Cloud employs a hybrid two-level control framework to enable coarse-grained and fine-grained resource allocation at the cloud and BS levels respectively. Specifically, R-Cloud not only coordinates resource allocation among BSs, MVNOs and mobile users across a Radio Access Network (RAN) and enables performance isolation by optimizing resource sharing and user association using an LP-rounding based algorithm at the cloud level; but also effectively schedule transmissions among multiple users at a BS using an optimal scheduling policy. We implemented R-Cloud over a wireless network testbed with software defined radios. It has been shown by extensive experimental and simulation results that R-Cloud can achieve effective RaaS over wireless networks and the proposed resource allocation algorithms outperform widely-used baseline solutions.

Device-to-device (D2D) communication has emerged as a promising technique for improving capacity and reducing power consumption in wireless networks. Most existing works on D2D communications either targeted CDMA-based single-channel networks or aimed at maximizing network throughput. In this paper, we, however, aim to enable green D2D communications in OFDMA-based wireless networks. We formally define an optimization problem based on a practical link data rate model, whose objective is to minimize total power consumption while meeting user data rate requirements. We propose solving it using a joint optimization approach by presenting two effective and efficient algorithms, which both jointly determines mode selection, channel allocation and power assignment. It has been shown by extensive simulation results that the proposed algorithms can achieve over 68% power savings, compared to several baseline methods.

With the exploding popularity of wireless communication, the radio spectrum has become a scarce commodity. To further improve the network capacity, various solutions have been proposed to increase spectrum efficiency and network throughput. Small cell network is one of these new trends for next generation mobile network design. One model is using relay stations (RS) as small cell providers to achieve extended coverage, lower cost, and higher network capacity. Considering multiple related physical constraints such as channel capacity, signal to noise ratio (SNR) requirement of subscribers, relay power and network topology, this paper studies a joint signal-aware RS placement and power allocation problem with multiple base stations in wireless relay networks. We presented approximation schemes which first find a minimum number of RS, using maximum transmission power, to cover all the subscribers meeting each SNR requirement, and then ensure communications between any subscriber to a base station by adjusting the transmission power of each RS. Numerical results are presented to confirm the theoretical analysis of our schemes, and to show strong performances of our solutions.

Device-to-Device (D2D) communication has emerged as a promising technique for improving capacity and reducing power consumption in wireless networks. Most existing works on D2D communications either targeted CDMA-based single-channel networks or aimed to maximize network throughput. In this paper, we, however, aim at enabling green D2D communications in OFDMA-based wireless networks. We formally define an optimization problem based on a practical link data rate model, whose objective is to minimize power consumption while meeting user data rate requirements. We then present an effective algorithm to solve it in polynomial time, which jointly determines mode selection, channel allocation and power assignment. It has been shown by extensive simulation results that the proposed algorithm can achieve over 57% power savings, compared to several baseline methods.

Currently, a few online review and recommendation systems (such as Yelp and Trip Advisor) have attracted millions of users and are gaining increasing popularity. They usually rate and rank places and attractions based on subjective ratings provided by users. In this paper, we present design, implementation and evaluation of a mobile phone Sensing based Objective Ranking (SOR) system, which ranks a target place based on data collected via mobile phone sensing. Our system has the following desirable features: 1) it is easy to use, 2) its architecture is so scalable that various embedded and external sensors can be easily integrated into it, 3) an online scheduling algorithm is proposed and used to schedule sensing activities for coverage maximization, which has a constant approximation ratio of 1/2, 4) a personalizable ranking algorithm is developed and used to rank target places based on various sensor readings and user preferences. We validate and evaluate SOR via both field tests (using real hiking trails and coffee shops in Syracuse, NY as target places) and simulation. The field-testing results show that data collected and processed by SOR can well capture characteristics of target places, and personalizable rankings produced by SOR can well match user preferences. In addition, simulation results well justify effectiveness of the proposed scheduling algorithm.

Saving power on base stations (BS) becomes a critical issue in wireless cellular networks. Many existing work has proposed to schedule BS into sleep to save energy. However, in reality, it is very difficult to shut down and reboot BSs frequently due to numerous technical issues and performance requirements. In this work, we propose a much more practical solution and offer a new perspective on implementing Green Wireless Networking by embracing the hot-trended small cell network idea. Instead of putting BSs into sleep, we tactically reduce the coverage (and the power usage) of each BS, and strategically place microcells (relay stations) to offload the traffic transmitted to/from BSs in order to save total power consumption. We propose approximation algorithms for various network design scenarios, with different wireless network setups and different power saving optimization objectives. Extensive numerical results are presented to confirm our theoretical analysis.

Small cell network is the new trend for next generation mobile network design. One feasible model is using Relay stations (RS) as small cell providers to achieve extended coverage, lower cost, and higher network capacity. This paper studies Signal-aware relay station placement and power allocation problem in wireless relay networks with multiple base stations in the field. This problem consists of both subscriber coverage problem and relay power optimization problem, which have not been extensively studied together in previous works. This work takes into account physical constraints such as channel capacity, signal to noise ratio (SNR) requirement of subscribers, relay power cost and network topology. We set up a two-step goal that is firstly to find minimum number of RS in order to cover all the subscribers meeting each SNR requirement, and then to ensure communications built between any subscriber to a base station. In order to ensure each subscriber's SNR, transmission power of each RS should be adjustable. Thus, minimizing power cost of RSs is our goal in the second step. We divide the problem into two sub-problems, Lower-tier Coverage Relay Allocation (LCRA) problem and Upper-tier Connectivity Relay Allocation (UCRA) problem. For the LCRA problem, we present two approximation solutions based on minimum hitting set and maximum independent set. For the UCRA problem, an approximation algorithm and an optimal algorithm are proposed. At the end, an approximation solution for our original problem, which combines the approaches of the two sub-problems, is provided. Numerical results are presented to confirm the theoretical analysis of our schemes, and to show strong performances of our solutions.

With wireless resource virtualization, multiple Mobile Virtual Network Operators (MVNOs) can be supported over a shared physical wireless network and traffic loads in a Base Station (BS) can be easily migrated to more power-efficient BSs in its neighborhood such that idle BSs can be turned off or put into sleep to save power. In this paper, we propose to leverage load migration and BS consolidation for green communications and consider a power-efficient network planning problem in virtualized Cognitive Radio Networks (CRNs) with the objective of minimizing total power consumption while meeting traffic load demand of each MVNO. First, we present a Mixed Integer Linear Programming (MILP) to provide optimal solutions. Then we present a general optimization framework to guide algorithm design, which solves two subproblems, channel assignment and load allocation, in sequence. For channel assignment, we present a (Δ1)-approximation algorithm (where Δ is the maximum number of BSs a BS can potentially interfere with). For load allocation, we present a polynomial-time optimal algorithm for a special case where BSs are power-proportional as well as two effective heuristic algorithms for the general case. In addition, we present an effective heuristic algorithm that jointly solves the two subproblems. It has been shown by extensive simulation results that the proposed algorithms produce close-to-optimal solutions, and moreover, achieve over 45% power savings compared to a baseline algorithm that does not migrate loads or consolidate BSs.