On Diagnosis of Forwarding Plane via Static Forwarding Rules in Software Defined Networks

Software Defined Networks (SDN) decouple the forwarding and control planes from each other. The control plane is assumed to have a global knowledge of the underlying physical and/or logical network topology so that it can monitor, abstract and control the forwarding plane. In our paper, we present solutions that install an optimal or near-optimal (i.e., within 14% of the optimal) number of static forwarding rules on switches/routers so that any controller can verify the topology connectivity and detect/locate link failures at data plane speeds without relying on state updates from other controllers. Our upper bounds on performance indicate that sub-second link failure localization is possible even at data-center scale networks. For networks with hundreds or few thousand links, tens of milliseconds of latency is achievable.Comment: Submitted to Infocom'14, 9 page

A FORMALIZED URBAN PROSUMER MODEL: SUPPORT OF AUTOMATED SIMULATION AND DESIGN OPTIMIZATION

Many global cities have announced ambitious net-zero energy consumption targets or net-zero CO2 emissions plans. It is well recognized that this can only be realized through a mix of measures such as efficiency improvements at the sites of consumption and decentralized energy generation, storage and delivery mechanisms. This transition will not happen without major changes to energy supply networks, especially in the way they enable frictionless inclusion of renewable energy sources and local supply, for instance through microgrids. At the urban scale, buildings constitute the major consumers of electricity and their integration through building-to-building and building-to-grid controls is crucial to realize efficient energy sharing in urban energy networks. Over the last decade, the building energy simulation domain has moved its focus from traditional local studies to urban energy studies. The main objective of this thesis is to make a contribution to this growing research domain, especially in enabling the simulation of energy supply networks in a robust manner and at a large scale. It is possible to simulate such networks with customized software but considering that there is no systematic way to specify urban energy models (especially with multiple concurrent control topologies), the simulation software has to be hand-customized which leads to opaque simulations that moreover are hard to use for rapid variant explorations. The thesis argues that this can be overcome by the development of an urban prosumer (UP) schema that facilitates the specification and automated mapping of an urban energy network into simulations, focusing on the effective specification of controls outside the software. At a high level, the UP schema is comprised of a physical and a logical layer. The physical layer conceptualizes existing urban energy networks using directed graphs for energy transport between nodes. The logical layer conceptualizes how the dynamic processing (reasoning) of sensor data leads to instructions to a set of actuators that execute the control. In doing so, two levels of control are distinguished: (a) “private” (mostly rule-based) control such as the internal HVAC system following temperature setpoints, (b) “public” control that is exposed to the rest of the network and thus within the scope of the UP schema. Public control can be either rule-based or optimal control, the latter driven by an appropriate optimality criterion, defined at a network scale. In design situations, the optimality criterion is not limited to control variables but can also include design parameters, such as building design parameters, solar installation sizes, community battery size, and the number of EV charging stations. Mixed-integer non-linear programming (MINLP) is used to solve optimal control problems. The genetic algorithm is employed to solve design optimization problems. The case studies using the UP schema for ten Georgia Tech campus buildings are presented. The purpose of the case studies is to prove that the UP schema can facilitate simulations involving different levels of controls. The simulations target optimal energy decisions for the selected campus buildings in the presence of PV and electricity battery. Additionally, three residential buildings in California are chosen to investigate how the design and control parameters act together to avoid the power outage situation with the embedded UP schema in the simulation platform.Ph.D

Allocation of control and data channels for Large-Scale Wireless Sensor Networks

Both IEEE 802.15.4 and 802.15.4a standards allow for dynamic channel allocation and use of multiple channels available at their physical layers but its MAC protocols are designed only for single channel. Also, sensor's transceivers such as CC2420 provide multiple channels and as shown in [1], [2] and [3] channel switch latency of CC2420 transceiver is just about 200$\mu$s. In order to enhance both energy efficiency and to shorten end to end delay, we propose, in this report, a spectrum-efficient frequency allocation schemes that are able to statically assign control channels and dynamically reuse data channels for Personal Area Networks (PANs) inside a Large-Scale WSN based on UWB technology

Recursive SDN for Carrier Networks

Control planes for global carrier networks should be programmable (so that new functionality can be easily introduced) and scalable (so they can handle the numerical scale and geographic scope of these networks). Neither traditional control planes nor new SDN-based control planes meet both of these goals. In this paper, we propose a framework for recursive routing computations that combines the best of SDN (programmability) and traditional networks (scalability through hierarchy) to achieve these two desired properties. Through simulation on graphs of up to 10,000 nodes, we evaluate our design's ability to support a variety of routing and traffic engineering solutions, while incorporating a fast failure recovery mechanism

SIMULATION OF A TRAINED TRAINED NEURAL NETWORK OF A FUZZY LOGIC REGULATION SYSTEM BASED ON THE COTTON DRYING PROCESS

The article discusses the modeling of a fuzzy-logical system of regulation of the process of drying of raw cotton. The tasks of overcoming uncertainties arising in the process of operation of technological units at the enterprises of the cotton-cleaning industry are presented. An example of solving such a problem by using an artificial neural network is given. Mathematical models based on the neural network have been developed that are used to formalize the process of drying raw cotton and determine the optimal tuned parameters of the fuzzy-logical PID controller, allowing the fate of changing the operating modes of the technological units of the drying drum. A method for determining the number of synoptic weights of artificial neural networks is proposed, which minimizes the number of trainings and increases the speed of management decisions. To train the neural network weights use the reverse spreading error method. The range of variation of the regulator parameter is justified, taking into account the features of the cotton drying process. As a result, the proposed model was used in the control system of the drying process in terms of quality indicators, which led to an increase in the accuracy of the technological process