In-Network Congestion Control for Multirate Multicast

We present a novel control scheme that dynamically optimizes multirate multicast. By computing the differential backlog at every node, our scheme adaptively allocates transmission rates per session/user pair in order to maximize throughput. An important feature of the proposed scheme is that it does not require source cooperation or centralized calculations. This methodology leads to efficient and distributed algorithms that scale gracefully and can be embraced by low-cost wireless devices. Additionally, it is shown that maximization of sum utility is possible by the addition of a virtual queue at each destination node of the multicast groups. The virtual queue captures the desire of the individual user and helps in making the correct resource allocation to optimize total utility. Under the operation of the proposed schemes backlog sizes are deterministically bounded, which provides delay guarantees on delivered packets. To illustrate its practicality, we present a prototype implementation in the NITOS wireless testbed. The experimental results verify that the proposed schemes achieve maximum performance while maintaining low complexity.National Science Foundation (U.S.) (grant CNS-0915988)National Science Foundation (U.S.) (grant CNS-1116209)United States. Office of Naval Research (grant N00014-12-1-0064

Optimization driven multi-hop network design and experimentation: the approach of the FP7 project OPNEX

International audienceThe OPNEX project exemplifies system and optimization theory as the foundations for algorithms that provably maximize capacity of wireless networks. The algorithms termed in abstract network models have been converted to protocols and architectures practically applicable to wireless systems. A validation methodology through experimental protocol evaluation in real network testbeds has been proposed and used. OPNEX uses recent advances in system theoretic network control, including the Back-Pressure principle, max-weight scheduling, utility optimization, congestion control, and the primal-dual method for extracting network algorithms. These approaches exhibited vast potential for achieving high capacity and full exploitation of resources in abstract network models and found their way to reality in high performance architectures developed as a result of the research conducted within OPNEX

SDN Controller Placement and Switch Assignment for Low Power IoT

Software defined networking (SDN) complements low power Internet of Things (IoT), since the former offers dynamicity and the latter is susceptible to environmental changes. The SDN controller placement refers to the selection of the IoT sensors running the controllers, while the switch assignment is the process of mapping each sensor to a controller. Both choices affect the volume of the control traffic, a significant metric in low power wireless IoT networks where bandwidth is scarce or energy consumption is important. In this paper, we model an optimization problem for minimum control traffic, assess its complexity and devise a set of heuristic algorithms for expediting its solution. We initially present a fast and simple heuristic algorithm, which is then extended to two iterative algorithms with even better performance at the cost of time complexity. Our simulations and testbed experimentation reveal close to optimal performance of all heuristic solutions with significantly less computation time than explicitly solving the optimization problem. At the end, we provide insights for further enhancements of these heuristics with a bias for minimum control delay