Abstract-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 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.

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.As the quantity of data that contributes to a particular result increases, keeping track of how different sources and transformations are related to each other becomes more difficult. This constrains the ability to answer questions regarding a result's history, such as: What were the underlying assumptions on which the result is based? Under what conditions does it remain valid? What other results were derived from the same data sources?The metadata that needs to be systematically captured to answer those (or similar) questions is called provenance (or lineage) and refers to a graph describing the relationships among all the elements (sources, processing steps, contextual information and dependencies) that contributed to the existence of a piece of data.This article presents current research in this field from a practical perspective, discussing not only existing systems and the fundamental concepts needed for using them in applications today, but also future challenges and opportunities. A number of use cases illustrate how provenance might be useful in practice.Where does data come from? Consider the need to understand the conditions, parameters, or assumptions behind a given result-in other words, the ability to point at a piece of data, for example, research result or anomaly in a system trace, and ask: Where did it come from? This would be useful for experiments involving digital data (such as in silico experiments in biology, other types of numerical simulations, or system evaluations in computer science).The provenance for each run of such experiments contains the links between results and corresponding starting conditions or configuration parameters. This becomes important especially when considering processing pipelines, where some early results serve as the basis of further experiments. Manually tracking all the parameters from a final result through intermediary data and to original sources is burdensome and error-prone.Of course, researchers are not the only ones requiring this type of tracking. The same techniques could be used to help people in the business or financial sectors-for example, figuring out the set of assumptions behind the statistics reported to a board of directors, or determining which mortgages were part of a traded security.

Nowadays, path prediction is being extensively examined for use in the context of mobile and wireless computing towards more efficient network resource management schemes. Path prediction allows the network and services to further enhance the quality of service levels that the user enjoys. In this paper we present a path prediction algorithm that exploits human creatures habits. 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. In our experiments, we compare results of the proposed Bayesian Neural Network with 5 standard neural network techniques in predicting both next location and next service to request. Bayesian learning for Neural Networks predicts both location and service 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. These N samples vote for the best prediction. Simulations of the algorithm, performed using a Realistic Mobility Patterns, show increased prediction accuracy.

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.

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.