High Availability Cluster System for Local Disaster Recovery with Markov Modeling Approach

The need for high availability (HA) and disaster recovery (DR) in IT environment is more stringent than most of the other sectors of enterprises. Many businesses require the availability of business-critical applications 24 hours a day, seven days a week, and can afford no data loss in the event of a disaster. It is vital that the IT infrastructure is resilient with regard to disruption, even site failures, and that business operations can continue without significant impact. As a result, DR has gained great importance in IT. Clustering of multiple industries standard servers together to allow workload sharing and fail-over capabilities is a low cost approach. In this paper, we present the availability model through Semi-Markov Process (SMP) and also analyze the difference in downtime of the SMP model and the approximate Continuous Time Markov Chain (CTMC) model. To acquire system availability, we perform numerical analysis and SHARPE tool evaluation.Comment: International Journal of Computer Science Issues, IJCSI Volume 6, Issue 2, pp25-32, November 200

Forecasting High-Dimensional Realized Volatility Matrices Using A Factor Model

Modeling and forecasting covariance matrices of asset returns play a crucial role in finance. The availability of high frequency intraday data enables the modeling of the realized covariance matrix directly. However, most models in the literature suffer from the curse of dimensionality. To solve the problem, we propose a factor model with a diagonal CAW model for the factor realized covariance matrices. Asymptotic theory is derived for the estimated parameters. In an extensive empirical analysis, we find that the number of parameters can be reduced significantly. Furthermore, the proposed model maintains a comparable performance with a benchmark vector autoregressive model

SINR Model with Best Server Association for High Availability Studies of Wireless Networks

The signal-to-interference-and-noise ratio (SINR) is of key importance for the analysis and design of wireless networks. For addressing new requirements imposed on wireless communication, in particular high availability, a highly accurate modeling of the SINR is needed. We propose a stochastic model of the SINR distribution where shadow fading is characterized by random variables. Therein, the impact of shadow fading on the user association is incorporated by modification of the distributions involved. The SINR model is capable to describe all parts of the SINR distribution in detail, especially the left tail which is of interest for studies of high availability.Comment: 11 pages, 4 figures, accepted for publication in IEEE Wireless Communications Letter

Availability Analysis of Redundant and Replicated Cloud Services with Bayesian Networks

Due to the growing complexity of modern data centers, failures are not uncommon any more. Therefore, fault tolerance mechanisms play a vital role in fulfilling the availability requirements. Multiple availability models have been proposed to assess compute systems, among which Bayesian network models have gained popularity in industry and research due to its powerful modeling formalism. In particular, this work focuses on assessing the availability of redundant and replicated cloud computing services with Bayesian networks. So far, research on availability has only focused on modeling either infrastructure or communication failures in Bayesian networks, but have not considered both simultaneously. This work addresses practical modeling challenges of assessing the availability of large-scale redundant and replicated services with Bayesian networks, including cascading and common-cause failures from the surrounding infrastructure and communication network. In order to ease the modeling task, this paper introduces a high-level modeling formalism to build such a Bayesian network automatically. Performance evaluations demonstrate the feasibility of the presented Bayesian network approach to assess the availability of large-scale redundant and replicated services. This model is not only applicable in the domain of cloud computing it can also be applied for general cases of local and geo-distributed systems.Comment: 16 pages, 12 figures, journa

The volatility of realized volatility

Using unobservable conditional variance as measure, latent-variable approaches, such as GARCH and stochastic-volatility models, have traditionally been dominating the empirical finance literature. In recent years, with the availability of high-frequency financial market data modeling realized volatility has become a new and innovative research direction. By constructing "observable" or realized volatility series from intraday transaction data, the use of standard time series models, such as ARFIMA models, have become a promising strategy for modeling and predicting (daily) volatility. In this paper, we show that the residuals of the commonly used time-series models for realized volatility exhibit non-Gaussianity and volatility clustering. We propose extensions to explicitly account for these properties and assess their relevance when modeling and forecasting realized volatility. In an empirical application for S&P500 index futures we show that allowing for time-varying volatility of realized volatility leads to a substantial improvement of the model's fit as well as predictive performance. Furthermore, the distributional assumption for residuals plays a crucial role in density forecasting. Klassifikation: C22, C51, C52, C5

Interoperator fixed-mobile network sharing

We propose the novel idea of interoperator fixed-mobile network sharing, which can be software-defined and readily-deployed. We study the benefits which the sharing brings in terms of resiliency, and show that, with the appropriate placement of a few active nodes, the mean service downtime can be reduced more than threefold by providing interoperator communication to as little as one optical network unit in one hundred. The implementation of the proposed idea can be carried out in stages when needed (the pay-as-you-grow deployment), and in those parts of the network where high service availability is needed most, e.g., in a business district. While the performance should expectedly increase, we show the resiliency is brought almost out of thin air by using redundant resources of different operators. We evaluated the service availability for 87400 networks with the relative standard error of the sample mean below 1%.Comment: 19th International Conference on Optical Network Design and Modeling (ONDM), pp. 192-197, May 201

ROMEO: A Plug-and-play Software Platform of Robotics-inspired Algorithms for Modeling Biomolecular Structures and Motions

Motivation: Due to the central role of protein structure in molecular recognition, great computational efforts are devoted to modeling protein structures and motions that mediate structural rearrangements. The size, dimensionality, and non-linearity of the protein structure space present outstanding challenges. Such challenges also arise in robot motion planning, and robotics-inspired treatments of protein structure and motion are increasingly showing high exploration capability. Encouraged by such findings, we debut here ROMEO, which stands for Robotics prOtein Motion ExplOration framework. ROMEO is an open-source, object-oriented platform that allows researchers access to and reproducibility of published robotics-inspired algorithms for modeling protein structures and motions, as well as facilitates novel algorithmic design via its plug-and-play architecture. Availability and implementation: ROMEO is written in C++ and is available in GitLab (https://github.com/). This software is freely available under the Creative Commons license (Attribution and Non-Commercial). Contact: [email protected]: 6 pages, 5 figure

Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders

Generative models in vision have seen rapid progress due to algorithmic improvements and the availability of high-quality image datasets. In this paper, we offer contributions in both these areas to enable similar progress in audio modeling. First, we detail a powerful new WaveNet-style autoencoder model that conditions an autoregressive decoder on temporal codes learned from the raw audio waveform. Second, we introduce NSynth, a large-scale and high-quality dataset of musical notes that is an order of magnitude larger than comparable public datasets. Using NSynth, we demonstrate improved qualitative and quantitative performance of the WaveNet autoencoder over a well-tuned spectral autoencoder baseline. Finally, we show that the model learns a manifold of embeddings that allows for morphing between instruments, meaningfully interpolating in timbre to create new types of sounds that are realistic and expressive

D2D-Aware Device Caching in MmWave-Cellular Networks

In this paper, we propose a novel policy for device caching that facilitates popular content exchange through high-rate device-to-device (D2D) millimeter-wave (mmWave) communication. The D2D-aware caching (DAC) policy splits the cacheable content into two content groups and distributes it randomly to the user equipment devices (UEs), with the goal to enable D2D connections. By exploiting the high bandwidth availability and the directionality of mmWaves, we ensure high rates for the D2D transmissions, while mitigating the co-channel interference that limits the throughput gains of D2D communication in the sub-6 GHz bands. Furthermore, based on a stochastic-geometry modeling of the network topology, we analytically derive the offloading gain that is achieved by the proposed policy and the distribution of the content retrieval delay considering both half- and full-duplex mode for the D2D communication. The accuracy of the proposed analytical framework is validated through Monte-Carlo simulations. In addition, for a wide range of a content popularity indicator the results show that the proposed policy achieves higher offloading and lower content-retrieval delays than existing state-of-the-art approaches.Comment: added main body of the pape

Localized Realized Volatility Modelling

With the recent availability of high-frequency Financial data the long range dependence of volatility regained researchers' interest and has lead to the consideration of long memory models for realized volatility. The long range diagnosis of volatility, however, is usually stated for long sample periods, while for small sample sizes, such as e.g. one year, the volatility dynamics appears to be better described by short-memory processes. The ensemble of these seemingly contradictory phenomena point towards short memory models of volatility with nonstationarities, such as structural breaks or regime switches, that spuriously generate a long memory pattern (see e.g. Diebold and Inoue, 2001; Mikosch and Starica, 2004b). In this paper we adopt this view on the dependence structure of volatility and propose a localized procedure for modeling realized volatility. That is at each point in time we determine a past interval over which volatility is approximated by a local linear process. Using S&P500 data we find that our local approach outperforms long memory type models in terms of predictability.Localized Autoregressive Modeling, Realized Volatility, Adaptive Procedure