Sherif Akoush

Predicting the Performance of Virtual Machine Migration

With the ability to move virtual machines between physical hosts, live migration is a core feature of virtualisation. However for migration to be useful, deployable feature on a large (datacentre) scale, we need to predict migration times with accuracy. In this paper, we characterise the parameters affecting live migration with particular emphasis on the Xen virtualisation platform. We discuss the relationships between the important parameters that affect migration and highlight how migration performance can vary considerably depending on the workload. We further provide 2 simulation models that are able to predict migration times to within 90% accuracy for both synthetic and real-world benchmarks.

Mobile user movement prediction using bayesian learning for neural networks

A technique for reducing the wireless cost of tracking mobile users with uncertain parameters is developed in this paper. Such uncertainty arises naturally in wireless networks, since an efficient user tracking is based on a prediction of its future call and mobility parameters. The conventional approach based on dynamic tracking is not reliable in the sense that inaccurate prediction of the user mobility parameters may significantly reduce the tracking efficiency. Unfortunately, such uncertainty is unavoidable for mobile users, especially for a burst mobility patterns. In this paper, we present a novel hybrid Bayesian neural network model for predicting locations on Cellular Networks (can also be extended to other wireless networks such as WI-FI and WiMAX). We investigate different parallel implementation techniques on mobile devices of the proposed approach and compare it to many standard neural network techniques such as: Back-propagation, Elman, Resilient, Levenberg-Marqudat, and One-Step Secant models. Bayesian learning for Neural Networks predicts location better than standard neural network techniques since it uses well founded probability model to represent uncertainty about the relationships being learned. The result of Bayesian training is a posterior distribution over network weights. We use Markov chain Monte Carlo methods (MCMC) to sample N values from the posterior weights distribution.

A primer on provenance

Assessing the quality or validity of a piece of data is not usually done in isolation. You typically examine the context in which the data appears and try to determine its original sources or review the process through which it was created. This is not so straightforward when dealing with digital data, however: the result of a computation might have been derived from numerous sources and by applying complex successive transformations, possibly over long periods of time.

Movement Prediction Using Bayesian Learning for Neural Networks

A technique for reducing the wireless cost of tracking mobile users with uncertain parameters is developed in this paper. Such uncertainty arises naturally in wireless networks, since an efficient user tracking is based on a prediction of its future call and mobility parameters. The conventional approach based on dynamic tracking is not reliable in the sense that inaccurate prediction of the user mobility parameters may significantly reduce the tracking efficiency. Unfortunately, such uncertainty is unavoidable for mobile users, especially for a burst mobility patterns. In this paper, we present a novel hybrid Bayesian neural network model for predicting locations on Cellular Networks (can also be extended to other wireless networks such as WI-FI and WiMAX). We investigate different parallel implementation techniques on mobile devices of the proposed approach and compare it to many standard neural network techniques such as: Back-propagation, Elman, Resilient, Levenberg-Marqudat, and One-Step Secant models. Bayesian learning for Neural Networks predicts location better than standard neural network techniques since it uses well founded probability model to represent uncertainty about the relationships being learned. The result of Bayesian training is a posterior distribution over network weights. We use Markov chain Monte Carlo methods (MCMC) to sample N values from the posterior weights distribution.

Bayesian Learning of Neural Networks for Mobile User Position Prediction

In this paper, a novel technique for location prediction of mobile users has been proposed, and a paging technique based on it is developed. Mobile users are creatures of habits. They tend to repeat their behaviors. Hence, neural networks with its learning and generalization ability may act as a suitable tool to predict the location of a mobile user provided it is trained appropriately by the personal mobility profile. For prediction, a novel hybrid Bayesian neural network model for predicting locations on Cellular Networks (can also be extended to other wireless networks such as Wi-Fi and WiMAX) is suggested. We investigate its different parallel implementation techniques on mobile devices, and compare its performance to many standard neural network techniques such as: Back-propagation, Elman, Resilient, Levenberg-Marqudat, and One-Step Secant models. This approach is free from all unrealistic assumptions about the movement of the users. It is applicable to any arbitrary cell architecture. It attempts to reduce the total location management cost and paging delay. In general, it enhances mobility management in wireless networks (in location management and hand-off management). In our experiments, we compare results of the proposed Bayesian Neural Network with 5 standard neural network techniques in predicting next location. Bayesian learning for Neural Networks predicts location better than standard neural network techniques since it uses well founded probability model to represent uncertainty about the relationship being learned. The result of Bayesian training is a posterior distribution over network weights.

Intelligent Energy-Aware Networks

Today the energy consumption of Information and Communication Technology (ICT) industry is a significant contributor to the total energy demand in many developed countries. Recent studies show that the ICT industry is responsible for about 2% of the global emission of CO2 and this percentage is predicted to increase as the Internet expands in bandwidth and reach. In this chapter we highlight different approaches for energy efficiency in communication networks. Firstly we review the techniques proposed to reduce the energy consumption of communication networks at the equipment and network levels. Secondly we investigate the use of renewable energy to reduce the CO2 emission of IP over WDM networks. Issues including how to use renewable energy (solar in this work) more effectively, how to reduce the nonrenewable energy consumption of transponders (the second most energy consuming device in a node), how to select the location of nodes using renewable energy, and load dependent energy consumption are considered. Thirdly we discuss workload migration using virtualization technologies in data centers as an approach of energy consumption minimization. Finally we consider some of the photonic systems advances which have the potential to reduce significantly the energy consumption within Ethernet switches and IP routers in the datacenter, showing how integrated photonic switch fabrics are starting to have the performance required for energy efficient high switching applications.This two-volume work levels both criticism and challenge to traditional developmental psychology. For too long, developmental psychologists have been studying individuals as if they developed in a sociocultural vacuum. As psychologists began to study the individuals development more broadly, they considered the impact of a number of other factors in the physical and social environment: early education, sociocultural differences, mass communication, alternative living arrangements, and medical care-to name but a few. Volume I, Historical and Cultural Issues, examines the problems of behavioral development from historical, political, theoretical, and cultural points of view. A number of content areas already familiar to developmental psychologists are discussed: Piagets theory, perceptual development, socialization, and language acquisition. In addition, topics relatively unfamiliar to American psychologists are included: the contribution of early European developmentalists such as William and Clara Stern, Alfred Binet, and Eduard Spranger; and an introduction to recent Soviet developmental theory. Volume II, Social and Environmental Issues, considers the effects of changes in social and environmental conditions upon individual development. The expanding impact of technology such as the communications media, the importance of nutrition, and the design of playgrounds and other spaces for growing children are among the changes examined, as are the impact of social organizations and interactions within small groups, focusing upon preschool education, interaction within the family, and personality development throughout the individuals life.