Video quality prediction under time-varying loads

We are on the cusp of an era where we can responsively and adaptively predict future network performance from network device statistics in the Cloud. To make this happen, regression-based models have been applied to learn mappings between the kernel metrics of a machine in a service cluster and service quality metrics on a client machine. The path ahead requires the ability to adaptively parametrize learning algorithms for arbitrary problems and to increase computation speed. We consider methods to adaptively parametrize regularization penalties, coupled with methods for compensating for the effects of the time-varying loads present in the system, namely load-adjusted learning. The time-varying nature of networked systems gives rise to the need for faster learning models to manage them; paradoxically, models that have been applied have not explicitly accounted for their time-varying nature. Consequently previous studies have reported that the learning problems were ill-conditioned -the practical, undesirable consequence of this is variability in prediction quality. Subset selection has been proposed as a solution. We highlight the short-comings of subset selection. We demonstrate that load-adjusted learning, using a suitable adaptive regularization function, outperforms current subset selection approaches by 10% and reduces computation

Improvising Safety and Energy Efficiency of IoT based Networks Data Routing

The Internet of Things is also referred to as IoT, outlines the physical object networking which is comprised of sensors, software and other associated technologies and technical tools in order to connect and exchange data over the internet with other devices and systems. The IoT devices range from household to industrial tools. Over the years, one of the most emerging technologies of the 21st century is IoT as it plays a huge role in sophisticated industries to smart application such as cars, household appliances and many more. With the implementation of IoT, people can take an advantage of seamless communication between other people, processes as well as things. Without human intervention, data having key information can be gathered by different means such as computing, cloud, big data, and associated mobile technologies. This paper focuses on making an IOT based network’s data routine safer and more energy efficient

Design and Evaluation of a Session-Aware Admission Control Framework for Improving Service Providers' Profitability

International audienceService-aware network management is a key issue for the current and next generation service-oriented networks. In this work, we focus on improving the QoS user experience (QoE) of Internet subscribers connecting to heavily loaded servers. Firstly, we advocate an innovative architecture for fine-grained admission-control of Internet servers subjected to multiple flow-based sessions. The proposed architecture is based on original concepts which meet the constraints of multiple-flow based sessions by explicitly identifying the flows pertaining to a single subscriber's session. Secondly, based on an economically-justified service model, we investigate novel session-aware admission-control for improving providers' profitability. Our fundamental observation is that it is sometimes desirable to reject some customers' new sessions so that others may be completed and thereby generate some revenue for the service provider. We evaluate the performance of the advocated model by conducting advanced simulations where we consider realistic CBR voice traffic. Results demonstrate that responsive session-aware admission-control improves service provider's benefits. For a satisfactory operator's revenue, session-aware admission-control improves the success rate by more than 69 %, compared to standard request-aware admission-control

A Survey on Subsurface Signal Propagation

Wireless Underground Communication (WUC) is an emerging field that is being developed continuously. It provides secure mechanism of deploying nodes underground which shields them from any outside temperament or harsh weather conditions. This paper works towards introducing WUC and give a detail overview of WUC. It discusses system architecture of WUC along with the anatomy of the underground sensor motes deployed in WUC systems. It also compares Over-the-Air and Underground and highlights the major differences between the both type of channels. Since, UG communication is an evolving field, this paper also presents the evolution of the field along with the components and example UG wireless communication systems. Finally, the current research challenges of the system are presented for further improvement of the WUCs

Uncertainty of the implementation time of geodynamic monitoring system in multi-criteria ranking of alternatives

The paper deals with the problem of ranking alternatives to geodynamic monitoring systems in the case of uncertainty of their implementation time. The problem is characterized by the fact that the choice of alternatives and the effect of it depends on the quality properties of the applied organizational and technical solutions, taking into account the time of implementation. The ordering of alternatives is proposed taking into account the uncertainty of the implementation time factors. Ranking is realized by comparing the trees of functional characteristics of alternatives taking into account the compliance of their characteristics with time-varying requirement

Distributed real-time hybrid simulation: Modeling, development and experimental validation

Real-time hybrid simulation (RTHS) has become a recognized methodology for isolating and evaluating performance of critical structural components under potentially catastrophic events such as earthquakes. Although RTHS is efficient in its utilization of equipment and space compared to traditional testing methods such as shake table testing, laboratory resources may not always be available in one location to conduct appropriate large-scale experiments. Consequently, distributed systems, capable of connecting multiple RTHS setups located at numerous geographically distributed facilities through information exchange, become essential. This dissertation focuses on the development, evaluation and validation of a new distributed RTHS (dRTHS) platform enabling integration of physical and numerical components of RTHS in geographically distributed locations over the Internet.^ One significant challenge for conducting successful dRTHS over the Internet is sustaining real-time communication between test sites. The network is not consistent and variations in the Quality of Service (QoS) are expected. Since dRTHS is delay-sensitive by nature, a fixed transmission rate with minimum jitter and latency in the network traffic should be maintained during an experiment. A Smith predictor can compensate network delays, but requires use of a known dead time for optimal operation. The platform proposed herein is developed to mitigate the aforementioned challenge. An easily programmable environment is provided based on MATLAB/xPC. In this method, (i) a buffer is added to the simulation loop to minimize network jitter and stabilize the transmission rate, and (ii) a routine is implemented to estimate the network time delay on-the-fly for the optimal operation of the Smith predictor.^ The performance of the proposed platform is investigated through a series of numerical and experimental studies. An illustrative demonstration is conducted using a three story structure equipped with an MR damper. The structure is tested on the shake table and its global responses are compared to RTHS and dRTHS configurations where the physical MR damper and numerical structural model are tested in local and geographically distributed laboratories.^ The main contributions of this research are twofold: (1) dRTHS is validated as a feasible testing methodology, alternative to traditional and modern testing techniques such as shake table testing and RTHS, and (ii) the proposed platform serves as a viable environment for researchers to develop, evaluate and validate their own tools, investigate new methods to conduct dRTHS and advance the research in this area to the limits