D-SPACE4Cloud: A Design Tool for Big Data Applications

The last years have seen a steep rise in data generation worldwide, with the development and widespread adoption of several software projects targeting the Big Data paradigm. Many companies currently engage in Big Data analytics as part of their core business activities, nonetheless there are no tools and techniques to support the design of the underlying hardware configuration backing such systems. In particular, the focus in this report is set on Cloud deployed clusters, which represent a cost-effective alternative to on premises installations. We propose a novel tool implementing a battery of optimization and prediction techniques integrated so as to efficiently assess several alternative resource configurations, in order to determine the minimum cost cluster deployment satisfying QoS constraints. Further, the experimental campaign conducted on real systems shows the validity and relevance of the proposed method

Performance Degradation and Cost Impact Evaluation of Privacy Preserving Mechanisms in Big Data Systems

Big Data is an emerging area and concerns managing datasets whose size is beyond commonly used software tools ability to capture, process, and perform analyses in a timely way. The Big Data software market is growing at 32% compound annual rate, almost four times more than the whole ICT market, and the quantity of data to be analyzed is expected to double every two years. Security and privacy are becoming very urgent Big Data aspects that need to be tackled. Indeed, users share more and more personal data and user-generated content through their mobile devices and computers to social networks and cloud services, losing data and content control with a serious impact on their own privacy. Privacy is one area that had a serious debate recently, and many governments require data providers and companies to protect users’ sensitive data. To mitigate these problems, many solutions have been developed to provide data privacy but, unfortunately, they introduce some computational overhead when data is processed. The goal of this paper is to quantitatively evaluate the performance and cost impact of multiple privacy protection mechanisms. A real industry case study concerning tax fraud detection has been considered. Many experiments have been performed to analyze the performance degradation and additional cost (required to provide a given service level) for running applications in a cloud system

Ecotopia: An Ecological Framework for Change Management in Distributed Systems

Abstract. Dynamic change management in an autonomic, service-oriented infrastructure is likely to disrupt the critical services delivered by the infrastructure. Furthermore, change management must accommodate complex real-world systems, where dependability and performance objectives are managed across multiple distributed service components and have specific criticality/value models. In this paper, we present Ecotopia, a framework for change management in complex service-oriented architectures (SOA) that is ecological in its intent: it schedules change operations with the goal of minimizing the service-delivery disruptions by accounting for their impact on the SOA environment. The change-planning functionality of Ecotopia is split between multiple objective-advisors and a system-level change-orchestrator component. The objective advisors assess the change-impact on service delivery by estimating the expected values of the Key Performance Indicators (KPIs), during and after change. The orchestrator uses the KPI estimations to assess the per-objective and overall business-value changes over a long time-horizon and to identify the scheduling plan that maximizes the overall business value. Ecotopia handles both external change requests, like software upgrades, and internal changes requests, like fault-recovery actions. We evaluate the Ecotopia framework using two realistic change-management scenarios in distributed enterprise systems

Modeling performance of Hadoop applications: A journey from queueing networks to stochastic well formed nets

Nowadays, many enterprises commit to the extraction of actionable knowledge from huge datasets as part of their core business activities. Applications belong to very different domains such as fraud detection or one-to-one marketing, and encompass business analytics and support to decision making in both private and public sectors. In these scenarios, a central place is held by the MapReduce framework and in particular its open source implementation, Apache Hadoop. In such environments, new challenges arise in the area of jobs performance prediction, with the needs to provide Service Level Agreement guarantees to the enduser and to avoid waste of computational resources. In this paper we provide performance analysis models to estimate MapReduce job execution times in Hadoop clusters governed by the YARN Capacity Scheduler. We propose models of increasing complexity and accuracy, ranging from queueing networks to stochastic well formed nets, able to estimate job performance under a number of scenarios of interest, including also unreliable resources. The accuracy of our models is evaluated by considering the TPC-DS industry benchmark running experiments on Amazon EC2 and the CINECA Italian supercomputing center. The results have shown that the average accuracy we can achieve is in the range 9–14%

Exhaled nitric oxide and urinary EPX levels in infants: a pilot study

<p>Abstract</p> <p>Background</p> <p>Objective markers of early airway inflammation in infants are not established but are of great interest in a scientific setting. Exhaled nitric oxide (FeNO) and urinary eosinophilic protein X (uEPX) are a two such interesting markers.</p> <p>Objective</p> <p>To investigate the feasibility of measuring FeNO and uEPX in infants and their mothers and to determine if any relations between these two variables and environmental factors can be seen in a small sample size. This was conducted as a pilot study for the ongoing Swedish Environmental Longitudinal Mother and child Asthma and allergy study (SELMA).</p> <p>Methods</p> <p>Consecutive infants between two and six months old and their mothers at children's health care centres were invited, and 110 mother-infant pairs participated. FeNO and uEPX were analysed in both mothers and infants. FeNO was analyzed in the mothers online by the use of the handheld Niox Mino device and in the infants offline from exhaled air sampled during tidal breathing. A 33-question multiple-choice questionnaire that dealt with symptoms of allergic disease, heredity, and housing characteristics was used.</p> <p>Results</p> <p>FeNO levels were reduced in infants with a history of upper respiratory symptoms during the previous two weeks (p < 0.002). There was a trend towards higher FeNO levels in infants with windowpane condensation in the home (p < 0.05). There was no association between uEPX in the infants and the other studied variables.</p> <p>Conclusion</p> <p>The use of uEPX as a marker of early inflammation was not supported. FeNO levels in infants were associated to windowpane condensation. Measuring FeNO by the present method may be an interesting way of evaluating early airway inflammation. In a major population study, however, the method is difficult to use, for practical reasons.</p

Surgical Management of Crohn Disease in Children: Guidelines From the Paediatric IBD Porto Group of ESPGHAN

The incidence of Crohn disease (CD) has been increasing and surgery needs to be contemplated in a substantial number of cases. The relevant advent of biological treatment has changed but not eliminated the need for surgery in many patients. Despite previous publications on the indications for surgery in CD, there was a need for a comprehensive review of existing evidence on the role of elective surgery and options in pediatric patients affected with CD. We present an expert opinion and critical review of the literature to provide evidence-based guidance to manage these patients. Indications, surgical options, risk factors, and medications in pre-and perioperative period are reviewed in the light of available evidence. Risks and benefits of surgical options are addressed. An algorithm is proposed for the management of postsurgery monitoring, timing for follow-up endoscopy, and treatment options

Distributed Operating Systems

Distributed operating systems have many aspects in common with centralized ones, but they also differ in certain ways. This paper is intended as an introduction to distributed operating systems, and especially to current university research about them. After a discussion of what constitutes a distributed operating system and how it is distinguished from a computer network, various key design issues are discussed. Then several examples of current research projects are examined in some detail, namely, the Cambridge Distributed Computing System, Amoeba, V, and Eden. © 1985, ACM. All rights reserved

On the relations between Markov chain lumpability and reversibility

In the literature, the notions of lumpability and time reversibility for large Markov chains have been widely used to efficiently study the functional and non-functional properties of computer systems. In this paper we explore the relations among different definitions of lumpability (strong, exact and strict) and the notion of time-reversed Markov chain. Specifically, we prove that an exact lumping induces a strong lumping on the reversed Markov chain and a strict lumping holds both for the forward and the reversed processes. Based on these results we introduce the class of λρ-reversible Markov chains which combines the notions of lumping and time reversibility modulo state renaming. We show that the class of autoreversible processes, previously introduced in Marin and Rossi (Proceedings of the IEEE 21st international symposium on modeling, analysis and simulation of computer and telecommunication systems MASCOTS, pp. 151–160, 2013), is strictly contained in the class of λρ-reversible chains