File Access Performance of Diskless Workstations

This paper studies the performance of single-user workstations that access files remotely over a local area network. From the environmental, economic, and administrative points of view, workstations that are diskless or that have limited secondary storage are desirable at the present time. Even with changing technology, access to shared data will continue to be important. It is likely that some performance penalty must be paid for remote rather than local file access. Our objectives are to assess this penalty and to explore a number of design alternatives that can serve to minimize it. Our approach is to use the results of measurement experiments to parameterize queuing network performance models. These models then are used to assess performance under load and to evahrate design alternatives. The major conclusions of our study are: (1) A system of diskless workstations with a shared file server can have satisfactory performance. By this, we mean performance comparable to that of a local disk in the lightly loaded case, and the ability to support substantial numbers of client workstations without significant degradation. As with any shared facility, good design is necessary to minimize queuing delays under high load. (2) The key to efficiency is protocols that allow volume transfers at every interface (e.g., between client and server, and between disk and memory at the server) and at every level (e.g., between client and server at the level of logical request/response and at the level of local area network packet size). However, the benefits of volume transfers are limited to moderate sizes (8-16 kbytes) by several factors. (3) From a performance point of view, augmenting the capabilities of the shared file server may be more cost effective than augmenting the capabilities of the client workstations. (4) Network contention should not be a performance problem for a lo-Mbit network and 100 active workstations in a software development environment

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

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

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