Approximations and Bounds for (n, k) Fork-Join Queues: A Linear Transformation Approach

Compared to basic fork-join queues, a job in (n, k) fork-join queues only needs its k out of all n sub-tasks to be finished. Since (n, k) fork-join queues are prevalent in popular distributed systems, erasure coding based cloud storages, and modern network protocols like multipath routing, estimating the sojourn time of such queues is thus critical for the performance measurement and resource plan of computer clusters. However, the estimating keeps to be a well-known open challenge for years, and only rough bounds for a limited range of load factors have been given. In this paper, we developed a closed-form linear transformation technique for jointly-identical random variables: An order statistic can be represented by a linear combination of maxima. This brand-new technique is then used to transform the sojourn time of non-purging (n, k) fork-join queues into a linear combination of the sojourn times of basic (k, k), (k+1, k+1), ..., (n, n) fork-join queues. Consequently, existing approximations for basic fork-join queues can be bridged to the approximations for non-purging (n, k) fork-join queues. The uncovered approximations are then used to improve the upper bounds for purging (n, k) fork-join queues. Simulation experiments show that this linear transformation approach is practiced well for moderate n and relatively large k.Comment: 10 page

Sustaining a federation of future internet experimental facilities

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Technology as tool to overcome barriers of using fitness facilities: A health behavioural perspective

Underlying health conditions have been highlighted throughout the literature preventing several populations from engaging in physical activity. There have been little to no attempts made in addressing these populations directly in fitness facilities or indirectly using information technology (IT). The current research aimed at exploring current barriers and practices regarding IT and technological support in a fitness facility environment, using health behaviour theories (HBT) to explain member experiences. The sample was composed of 66 participants selected from 5 fitness facilities in Manchester, UK, of which there were 60.6% males and 39.4% females aged from 18-59. The instrument used was a survey. Health motives were reported by 71.2% of the participants, while ‘injury’ (reported by 70.2%), ‘lack of knowledge about exercise and health’ (reported by 42.4%), and ‘illness’ (reported by 28.1%) as main barriers to use the facilities. The main support mechanisms provided by the facilities management were staff support (59%), with online and technological support only accounting for 38.6% of facility support. The use of personal IT within the facilities were utilised by over half the participants (50.2%). The study revealed the need of additional IT support by fitness facilities in the form of applications and digital platforms. The findings are discussed with HBT as the theoretical underpinnings and suggestions are made for future research regarding IT advancements as support mechanisms

"Mothers as Candy Wrappers": Critical Infrastructure Supporting the Transition into Motherhood

Copyright © ACM. The transition into motherhood is a complicated and often unsupported major life disruption. To alleviate mental health issues and to support identity re-negotiation, mothers are increasingly turning to online mothers\u27 groups, particularly private and secret Facebook groups; these can provide a complex system of social, emotional, and practical support for new mothers. In this paper we present findings from an exploratory interview study of how new mothers create, find, use, and participate in ICTs, specifically online mothers\u27 groups, to combat the lack of formal support systems by developing substitute networks. Utilizing a framework of critical infrastructures, we found that these online substitute networks were created by women, for women, in an effort to fill much needed social, political, and medical gaps that fail to see \u27woman and mother\u27 as a whole being, rather than simply as a \u27discarded candy wrapper\u27. Our study contributes to the growing literature on ICT use by mothers for supporting and negotiating new identities, by illustrating how these infrastructures can be re-designed and appropriated in use, for critical utilization

Massively Parallel Sort-Merge Joins in Main Memory Multi-Core Database Systems

Two emerging hardware trends will dominate the database system technology in the near future: increasing main memory capacities of several TB per server and massively parallel multi-core processing. Many algorithmic and control techniques in current database technology were devised for disk-based systems where I/O dominated the performance. In this work we take a new look at the well-known sort-merge join which, so far, has not been in the focus of research in scalable massively parallel multi-core data processing as it was deemed inferior to hash joins. We devise a suite of new massively parallel sort-merge (MPSM) join algorithms that are based on partial partition-based sorting. Contrary to classical sort-merge joins, our MPSM algorithms do not rely on a hard to parallelize final merge step to create one complete sort order. Rather they work on the independently created runs in parallel. This way our MPSM algorithms are NUMA-affine as all the sorting is carried out on local memory partitions. An extensive experimental evaluation on a modern 32-core machine with one TB of main memory proves the competitive performance of MPSM on large main memory databases with billions of objects. It scales (almost) linearly in the number of employed cores and clearly outperforms competing hash join proposals - in particular it outperforms the "cutting-edge" Vectorwise parallel query engine by a factor of four.Comment: VLDB201

Higher-order perturbation solutions to dynamic, discrete-time rational expectations models

We present an algorithm and software routines for computing nth order Taylor series approximate solutions to dynamic, discrete-time rational expectations models around a nonstochastic steady state. The primary advantage of higher-order (as opposed to first- or second-order) approximations is that they are valid not just locally, but often globally (i.e., over nonlocal, possibly very large compact sets) in a rigorous sense that we specify. We apply our routines to compute first- through seventh-order approximate solutions to two standard macroeconomic models, a stochastic growth model and a life-cycle consumption model, and discuss the quality and global properties of these solutions.Macroeconomics - Econometric models ; Business cycles ; Monetary policy

Initiating and Sustaining Female Networks in Computer Science and IT

Over the last decade, several networks and communities for women in IT have been initiated. It has been known that specific needs for support exist where members of a minority have difficulties in finding like-minded people in their everyday environment. This paper investigates different forms of female networks in Computer Science and IT. In particular, it analyses forms of network initiation, which often involve face-to-face meetings at regular events like conferences or, increasingly, at summer universities for female students. We conducted three studies to identify the attendees' expectations and needs for support using questionnaires, interviews, and a wiki analysis. This paper aims at identifying effective strategies for initiating female networks

Adaptive Threshold Sampling and Estimation

Sampling is a fundamental problem in both computer science and statistics. A number of issues arise when designing a method based on sampling. These include statistical considerations such as constructing a good sampling design and ensuring there are good, tractable estimators for the quantities of interest as well as computational considerations such as designing fast algorithms for streaming data and ensuring the sample fits within memory constraints. Unfortunately, existing sampling methods are only able to address all of these issues in limited scenarios. We develop a framework that can be used to address these issues in a broad range of scenarios. In particular, it addresses the problem of drawing and using samples under some memory budget constraint. This problem can be challenging since the memory budget forces samples to be drawn non-independently and consequently, makes computation of resulting estimators difficult. At the core of the framework is the notion of a data adaptive thresholding scheme where the threshold effectively allows one to treat the non-independent sample as if it were drawn independently. We provide sufficient conditions for a thresholding scheme to allow this and provide ways to build and compose such schemes. Furthermore, we provide fast algorithms to efficiently sample under these thresholding schemes