Abstract

In this era of ubiquitous computing, coupled with the emergence of big data and internet of things, there have been constant changes in every aspect of cloud data center communications - its network connectivity, data storage, data transfer, and architectural design. As a result of this, the amount of data transferable, and the frequency of data transfer have tremendously increased; causing device failures and traffic congestions. To cope with these changes so that performance can be sustained amidst device failures and traffic congestion, the design of fault tolerant cloud data center is important. A fault tolerant cloud data center network should be able to provide alternative paths from source to destination during failures so that there will not be abrupt fall in performance. But still with the ongoing researches in this regard, there has not been a robust cloud data center design that can boast of being suitable for alleviating the poor fault tolerance of cloud data center. In this paper, we proposed the improved versions of fat-tree interconnection hybrid designs derived from the structure called Z-fat tree; to address the issues of fault tolerance. Then, we compared these designs with single fat tree architecture with the same amount of resources for client server communication pattern such as Email application in a cloud data center. The simulation results obtained based on failed switches and links, show that our proposed hybrid designs outperformed the single fat tree design as the inter arrival time of the packets reduces.

Abstract

Stability of Wardrop equilibria is analyzed for dynamical transportation networks in which the drivers' route choices are influenced by information at multiple temporal and spatial scales. The considered model involves a continuum of indistinguishable drivers commuting between a common origin/destination pair in an acyclic transportation network. The drivers' route choices are affected by their, relatively infrequent, perturbed best responses to global information about the current network congestion levels, as well as their instantaneous local observation of the immediate surroundings as they transit through the network. A novel model is proposed for the drivers' route choice behavior, exhibiting local consistency with their preference toward globally less congested paths as well as myopic decisions in favor of locally less congested paths. The simultaneous evolution of the traffic congestion on the network and of the aggregate path preference is modeled by a system of coupled ordinary differential equations. The main result shows that, if the frequency of updates of path preferences is sufficiently small as compared to the frequency of the traffic flow dynamics, then the state of the transportation network ultimately approaches a neighborhood of the Wardrop equilibrium. The presented results may be read as a further evidence in support of Wardrop's postulate of equilibrium, showing robustness of it with respect to non-persistent perturbations. The proposed analysis combines techniques from singular perturbation theory, evolutionary game theory, and cooperative dynamical systems. Comment: 21 pages, 2 figures, submitted

Abstract

Image Processing (IP) applications have become popular with the advent of efficient algorithms and low-cost CMOS cameras with high resolution. However, IP applications are compute-intensive, consume a lot of energy and have long processing times. Image approximation has been proposed by recent works for an energy-efficient design of these applications. It also reduces the impact of long processing times. The challenge here is that the IP applications often work as a part of bigger closed-loop control systems, e.g. advanced driver assistance system (ADAS). The impact of image approximations that tolerate certain error on these image-based control (IBC) systems is very important. However, there is a lack of tool support to evaluate the performance of such closed-loop IBC systems when the IP is approximated. We propose a framework - for both software-in-the-loop (SiL) and hardware-in-the-loop (HiL) simulation - for performance evaluation of image approximation on a closed-loop automotive IBC system (IMACS). Both simulation setups model the 3D environment in 3ds Max, and simulate the system dynamics, camera position and environment in V-REP. Our SiL setup simulates the system software in C++ or Matlab. Here, V-REP runs as a server and the software as a client in synchronous mode. Our HiL simulation setup runs the system software in the NVIDIA Drive PX2 platform and communicates to V-REP using application programming interfaces (APIs) for synchronous execution. We show the effectiveness of our framework using a vision-based lateral control example.

Abstract

Trabajo presentado a la 4th International Conference on Control, Decision and Information Technologies (CoDIT), celebrada en Barcelona (España) del 5 al 7 de abril de 2017. Inland navigation networks are composed of several artificial canals that are characterized by no slope. These canals are particularly subject to resonance phenomena, which can create waves such that the navigation condition might not be guaranteed. It is therefore required to ensure dealing with freefault measured data and actuators. In this work, a fault detection and isolation method based on the Integrator Delay Zero model (IDZ) is designed for flat navigation canals. The proposed method is dedicated to the detection and isolation of sensor and actuator faults. Finally, it is tested by considering the Cuinchy-Fontinettes canal located in the north of France. This work has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECOP) and FEDER through the project HARCRICS (ref. DPI2014-58104-R) and through the grant IJCI-2014-2081. Peer Reviewed Document type: Conference object

Abstract

Vehicle dynamics has been an active field of research for many decades. The well-known single-track model already introduced in 1940 by Riekert and Schunck is still used to explain fundamental effects in vehicle dynamics such as under- and oversteering. However, meanwhile also very complex multibody dynamics models exist, which allow very detailed simulations. On the other hand, real-time computations necessary for active safety and driver assistance systems are demanded for models of lower complexity. As important effects at the handling limits are not covered by the linear single-track model, but complex multibody models cannot be integrated fast enough by electronic control units of safety systems, models with an adjustable degree of complexity are desired. As an example, model-predictive control is an upcoming field of application and relies on models that are integrated over the prediction horizon. Therefore, the selection of an appropriate model is an important task. The crux of the matter is to make a compromise between computation time and accuracy of the model having only a rough guess of the accuracy of the model. In this contribution a systematic approach is proposed. Instead of selecting an existing model, which is supposed to match requirements in computation time and level of detail, a complex model is reduced to match the requirements using symbolic model reduction techniques. This approach has two major advantages: First, the accuracy can be set by the user. Second, the model is continuously adjustable in its complexity or propagation precision.