Abstract
We present a model for measuring the impact of offloading soft real-time jobs over multi-tier cloud infrastructures. The jobs originate in mobile devices and offloading strategies may choose to execute them locally, in neighbouring devices, in cloudlets or in infrastructure cloud servers. Within this specification, we put forward several such offloading strategies characterised by their differential use of the cloud tiers with the goal of optimizing execution time and/or energy consumption. We implement an instance of the model using Jay, a software framework for adaptive computation offloading in hybrid edge clouds. The framework is modular and allows the model and the offloading strategies to be seamlessly implemented while providing the tools to make informed runtime offloading decisions based on system feedback, namely through a built-in system profiler that gathers runtime information such as workload, energy consumption and available bandwidth for every participating device or server. The results show that offloading strategies sensitive to runtime conditions can effectively and dynamically adjust their offloading decisions to produce significant gains in terms of their target optimization functions, namely, execution time, energy consumption and fulfilment of job deadlines.

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Abstract

Abstract
Being able to capture the characteristics of a time series with a feature vector is a very important task with a multitude of applications, such as classification, clustering or forecasting. Usually, the features are obtained from linear and nonlinear time series measures, that may present several data related drawbacks. In this work we introduce NetF as an alternative set of features, incorporating several representative topological measures of different complex networks mappings of the time series. Our approach does not require data preprocessing and is applicable regardless of any data characteristics. Exploring our novel feature vector, we are able to connect mapped network features to properties inherent in diversified time series models, showing that NetF can be useful to characterize time data. Furthermore, we also demonstrate the applicability of our methodology in clustering synthetic and benchmark time series sets, comparing its performance with more conventional features, showcasing how NetF can achieve high-accuracy clusters. Our results are very promising, with network features from different mapping methods capturing different properties of the time series, adding a different and rich feature set to the literature.

Abstract
One important problem in the design of novel logic programming systems is the support of several forms of implicit parallelism. A new binding model, the Sparse Binding Array (SBA), has been proposed for the efficient and simplified integration of Independent-And, Determinate-And and Or-parallelism. In this paper we report on the use of this model for pure Or-parallelism. The work discusses the major implementation issues in supporting this binding model for pure Or-parallelism. We show that an implementation based on this Binding model is more efficient then the original Aurora using tbe traditional Binding Array model [16]. Moreover, we explain how the notion of a variable level can be used to reduce overheads of the Orparallel system. Our results in supporting pure or-parallelism show that the approach is very promissing for combined paralell systems.

Abstract
In this paper we present the architecture of RTPM, a middle-ware framework aimed at supporting the development and management of information systems for high-speed public transportation systems. The framework is based on a peer-to-peer overlay infrastructure with the main focus being on providing a scalable, resilient, reconfigurable, highly available platform for real-time and QoS computing. Copyright 2008 ACM.