Tuning the Level of Concurrency in Software Transactional Memory: An Overview of Recent Analytical, Machine Learning and Mixed Approaches

Synchronization transparency offered by Software Transactional Memory (STM) must not come at the expense of run-time efficiency, thus demanding from the STM-designer the inclusion of mechanisms properly oriented to performance and other quality indexes. Particularly, one core issue to cope with in STM is related to exploiting parallelism while also avoiding thrashing phenomena due to excessive transaction rollbacks, caused by excessively high levels of contention on logical resources, namely concurrently accessed data portions. A means to address run-time efficiency consists in dynamically determining the best-suited level of concurrency (number of threads) to be employed for running the application (or specific application phases) on top of the STM layer. For too low levels of concurrency, parallelism can be hampered. Conversely, over-dimensioning the concurrency level may give rise to the aforementioned thrashing phenomena caused by excessive data contention—an aspect which has reflections also on the side of reduced energy-efficiency. In this chapter we overview a set of recent techniques aimed at building “application-specific” performance models that can be exploited to dynamically tune the level of concurrency to the best-suited value. Although they share some base concepts while modeling the system performance vs the degree of concurrency, these techniques rely on disparate methods, such as machine learning or analytic methods (or combinations of the two), and achieve different tradeoffs in terms of the relation between the precision of the performance model and the latency for model instantiation. Implications of the different tradeoffs in real-life scenarios are also discussed

Self-Assembly of Polyhedral Hybrid Colloidal Particles

We have developed a new method to produce hybrid particles with polyhedral shapes in very high yield (liter quantities at up to 70% purity) using a combination of emulsion polymerization and inorganic surface chemistry. The procedure has been generalized to create complex geometries, including hybrid line segments, triangles, tetrahedra, octahedra, and more. The optical properties of these particles are tailored for studying their dynamics and self-assembly. For example, we produce systems that consist of index-matched spheres allowing us to define the position of each elementary particle in three-dimensional space. We present some preliminary studies on the self-assembly of these complex shaped systems based on electron and optical microscopy.Engineering and Applied SciencesPhysic

The DNA Damage Response Pathway Contributes to the Stability of Chromosome III Derivatives Lacking Efficient Replicators

In eukaryotic chromosomes, DNA replication initiates at multiple origins. Large inter-origin gaps arise when several adjacent origins fail to fire. Little is known about how cells cope with this situation. We created a derivative of Saccharomyces cerevisiae chromosome III lacking all efficient origins, the 5ORIΔ-ΔR fragment, as a model for chromosomes with large inter-origin gaps. We used this construct in a modified synthetic genetic array screen to identify genes whose products facilitate replication of long inter-origin gaps. Genes identified are enriched in components of the DNA damage and replication stress signaling pathways. Mrc1p is activated by replication stress and mediates transduction of the replication stress signal to downstream proteins; however, the response-defective mrc1AQ allele did not affect 5ORIΔ-ΔR fragment maintenance, indicating that this pathway does not contribute to its stability. Deletions of genes encoding the DNA-damage-specific mediator, Rad9p, and several components shared between the two signaling pathways preferentially destabilized the 5ORIΔ-ΔR fragment, implicating the DNA damage response pathway in its maintenance. We found unexpected differences between contributions of components of the DNA damage response pathway to maintenance of ORIΔ chromosome derivatives and their contributions to DNA repair. Of the effector kinases encoded by RAD53 and CHK1, Chk1p appears to be more important in wild-type cells for reducing chromosomal instability caused by origin depletion, while Rad53p becomes important in the absence of Chk1p. In contrast, RAD53 plays a more important role than CHK1 in cell survival and replication fork stability following treatment with DNA damaging agents and hydroxyurea. Maintenance of ORIΔ chromosomes does not depend on homologous recombination. These observations suggest that a DNA-damage-independent mechanism enhances ORIΔ chromosome stability. Thus, components of the DNA damage response pathway contribute to genome stability, not simply by detecting and responding to DNA template damage, but also by facilitating replication of large inter-origin gaps

PARAPARETIC SYNDROME OF LOWER LIMBS

PARAPARESIS OF LOWER LIMBS WITH HYPOESTHESIA D8 LEVEL. ALL TESTS WERE NORMAL, IN PARTICULAR MRI AND EMG,PEM PESS. DIAGNOSIS WAS SOMATOFORM SYNDROME AFTER ABORTIO

Machine learning-based management of cloud applications in hybrid clouds: A Hadoop case study

This paper illustrates the effort to integrate a machine learning-based framework which can predict the remaining time to failure of computing nodes with Hadoop applications. This work is part of a larger effort targeting the development of a cloud-oriented autonomic framework to increase the availability of applications subject to software anomalies, and to jointly improve their performance. The framework uses machine-learning, software rejuvenation, and load distribution techniques to proactively prevent failures. We believe that this work allows to set a possible path towards the definition of best practices for the development of systems to support autonomic management of cloud applications, illustrating what are the issues that should be addressed by the research community. Indeed, given the scale and the complexity of modern computing infrastructures, effective autonomic management approaches of cloud applications are becoming mandatory

Overweight, thinness, body self-image and eating strategies of 2121 Italian teen agers

This study describes the prevalence rate of overweight and thinness in a population of teens living in two different areas of Italy and explores the body self-image perception and unhealthy eating behaviours and strategies to lose weight. A questionnaire was administered to a sample of 2,121 teenage students (1,084 males, 1,037 females). Results showed that teen females and males build and perceive their body images in very different ways. Most of the overall sample perceived their weight as normal, while a relevant 31.6% defined themselves as overweight and another 4.4% as heavily overweight. Analysis based on BMI (calculated through self-referred weight and height) showed that only 9.2% of our sample could be considered overweight and 1.7% obese. Most of female teen students (485 out of 1,037) were trying to lose weight, demonstrating that strategies to lose weight were undertaken also by girls perceiving themselves as normal in relation to body weight. 46.8% girls were using strategies to lose weight compared with 21.9% boys. These strategies included very problematic behaviours like self-induced vomiting (3.3% F vs 1.7% M) and dieting pills (2.8% F vs 1.5% M) undertaken along with more usual thinning strategies like dieting and exercising. Girls were more prone than boys to exercise as a way to lose weight (41% vs 31.7%). This study showed that there is a deep gap between actual weight and perceived body-image and weight. This study is one of the first of this kind in Italy and calls for primary prevention and health education programs aimed at improving teen body-image as a strategy to reduce the eating disorder epidemics spreading among young people

Automated Workload Characterization in Cloud-based Transactional Data Grids

Cloud computing represents a cost-effective paradigm to deploy a wide class of large-scale distributed applications, for which the pay-per-use model combined with automatic resource provisioning promise to reduce the cost of dependability and scalability. However, a key challenge to be addressed to materialize the advantages promised by Cloud computing is the design of effective auto-scaling and self-tuning mechanisms capable of ensuring pre-determined QoS levels at minimum cost in face of changing workload conditions. This is one of the keys goals that are being pursued by the Cloud-TM project, a recent EU project that is developing a novel, self optimizing transactional data platform for the cloud. In this paper we present the key design choices underlying the development of Cloud-TM’s Workload Analyzer (WA), a crucial component of the Cloud-TM platform that is change of three key functionalities: aggregating, filtering and correlating the streams of statistical data gathered from the various nodes of the Cloud- TM platform; building detailed workload profiles of applications deployed on the Cloud-TM platform, characterizing their present and future demands in terms of both logical (i.e. data) and physical (e.g. hardware-related) resources; triggering alerts in presence of violations (or risks of future violations) of predetermined SLAs