Control reconfiguration

About: Control reconfiguration is a research topic. Over the lifetime, 22423 publications have been published within this topic receiving 334217 citations.

Virtual Network Topology Adaptability Based on Data Analytics for Traffic Prediction

Abstract: The introduction of new services requiring large and dynamic bitrate connectivity can cause changes in the direction of the traffic in metro and even core network segments throughout the day. This leads to large overprovisioning in statically managed virtual network topologies (VNTs), which are designed to cope with the traffic forecast. To reduce expenses while ensuring the required grade of service, in this paper we propose a VNT reconfiguration approach based on data analytics for traffic prediction (VENTURE). It regularly reconfigures the VNT based on the predicted traffic, thus adapting the topology to both the current and the predicted traffic volume and direction. A machine learning algorithm based on an artificial neural network is used to provide robust and adaptive traffic models. The reconfiguration problem that takes as its input the traffic prediction is modeled mathematically, and a heuristic is proposed to solve it in practical times. To support VENTURE, we propose an architecture that allows collecting and storing data from monitoring at the routers and that is used to train predictive models for every origin-destination pair. Exhaustive simulation results of the algorithm, together with the experimental assessment of the proposed architecture, are finally presented.

Dynamically inserting, operating, and eliminating thermal sensors of FPGA-based systems

Abstract: In this paper, a new thermal monitoring strategy suitable for field programmable logic array (FPGA)-based systems is developed. The main idea is that a fully digital temperature transducer can be dynamically inserted, operated, and eliminated from the circuit under test using run-time reconfiguration. A ring-oscillator together with its auxiliary blocks (basically counting and control stages) is first placed in the design. After the actual temperature of the die is captured, the value is read back via the FPGA configuration port. Then, the sensor is eliminated from the chip in order to release programmable resources and avoid self-heating. All the hardware of the sensor is written in Java, using the JBits API provided by the chip manufacturer. The main advantage of the technique is that the sensor is completely stand-alone, no I/O pads are required, and no permanent use of any FPGA element is done. Additionally, the sensor is small enough to arrange an array of them along the chip. Thus, FPGAs became a new tool for researchers interested in the thermal aspects of integrated circuits.

VNR Algorithm: A Greedy Approach for Virtual Networks Reconfigurations

Abstract: In this paper we address the problem of virtual network reconfiguration. In our previous work on virtual network embedding strategies, we found that most virtual network rejections were caused by bottlenecked substrate links while peak resource use is equal to 18%. These observations lead us to propose a new greedy Virtual Network Reconfiguration algorithm, VNR. The main aim of our proposal is to 'tidy up' substrate network in order to minimise the number of overloaded substrate links, while also reducing the cost of reconfiguration. We compare our proposal with the related reconfiguration strategy VNA-Periodic, both of them are incorporated in the best existing embedding strategies VNE-AC and VNE-Greedy in terms of rejection rate. The results obtained show that VNR outperforms VNA-Periodic. Indeed, our research shows that the performances of VNR do not depend on the virtual network embedding strategy. Moreover, VNR minimises the rejection rate of virtual network requests by at least 83% while the cost of reconfiguration is lower than with VNA-Periodic.

Control and learning of ambience by an intelligent building

Abstract: Modern approaches to the architecture of living and working environments emphasize the dynamic reconfiguration of space and function to meet the needs, comfort, and preferences of its inhabitants. Although it is possible for a human operator to specify a configuration explicitly, the size, sophistication, and dynamic requirements of modern buildings demands that they have autonomous intelligence that could satisfy the needs of its inhabitants without human intervention. We describe a multiagent framework for such intelligent building control that is deployed in a commercial building equipped with sensors and effectors. Multiple agents control subparts of the environment using fuzzy rules that link sensors and effectors. The agents communicate with one another by asynchronous, interest-based messaging. They implement a novel unsupervised online real-time learning algorithm that constructs a fuzzy rule-base, derived from very sparse data in a nonstationary environment. We have developed methods for evaluating the performance of systems of this kind. Our results demonstrate that the framework and the learning algorithm significantly improve the performance of the building.