BiDAl: Big Data Analyzer for Cluster Traces

Modern data centers that provide Internet-scale services are stadium-size structures housing tens of thousands of heterogeneous devices (server clusters, networking equipment, power and cooling infrastructures) that must operate continuously and reliably. As part of their operation, these devices produce large amounts of data in the form of event and error logs that are essential not only for identifying problems but also for improving data center efficiency and management. These activities employ data analytics and often exploit hidden statistical patterns and correlations among different factors present in the data. Uncovering these patterns and correlations is challenging due to the sheer volume of data to be analyzed. This paper presents BiDAl, a prototype “log-data analysis framework” that incorporates various Big Data technologies to simplify the analysis of data traces from large clusters. BiDAl is written in Java with a modular and extensible architecture so that different storage backends (currently, HDFS and SQLite are supported), as well as different analysis languages (current implementation supports SQL, R and Hadoop MapReduce) can be easily selected as appropriate. We present the design of BiDAl and describe our experience using it to analyze several public traces of Google data clusters for building a simulation model capable of reproducing observed behavior

Microcities: A Platform Based on Microclouds for Neighborhood Services

International audienceThe current datacenter-centralized architecture limits the cloud to the location of the datacenters, generally far from the user. This architecture collides with the latest trend of ubiquity of Cloud computing. Distance leads to increased utilization of the broadband Wide Area Network and poor user experience, especially for interactive applications. A semi-decentralized approach can provide a better Quality of Experience (QoE) in large urban populations in mobile cloud networks, by confining local traffic near the user while maintaining centralized characteristics, running on the users and network devices. In this paper, we propose a novel semi-decentralized cloud architecture based on microclouds. Microclouds are dynamically created and allow users to contribute resources from their computers, mobile and network devices to the cloud. Microclouds provide a dynamic and scalable system without an extra investment in infrastructure. We also provide a description of a realistic mobile cloud use case, and its adaptation to microclouds

Genome-wide association analyses of symptom severity among clozapine-treated patients with schizophrenia spectrum disorders

Clozapine is the most effective antipsychotic for patients with treatment-resistant schizophrenia. However, response is highly variable and possible genetic underpinnings of this variability remain unknown. Here, we performed polygenic risk score (PRS) analyses to estimate the amount of variance in symptom severity among clozapine-treated patients explained by PRSs (R2) and examined the association between symptom severity and genotype-predicted CYP1A2, CYP2D6, and CYP2C19 enzyme activity. Genome-wide association (GWA) analyses were performed to explore loci associated with symptom severity. A multicenter cohort of 804 patients (after quality control N = 684) with schizophrenia spectrum disorder treated with clozapine were cross-sectionally assessed using the Positive and Negative Syndrome Scale and/or the Clinical Global Impression-Severity (CGI-S) scale. GWA and PRS regression analyses were conducted. Genotype-predicted CYP1A2, CYP2D6, and CYP2C19 enzyme activities were calculated. Schizophrenia-PRS was most significantly and positively associated with low symptom severity (p = 1.03 × 10−3; R2 = 1.85). Cross-disorder-PRS was also positively associated with lower CGI-S score (p = 0.01; R2 = 0.81). Compared to the lowest tertile, patients in the highest schizophrenia-PRS tertile had 1.94 times (p = 6.84×10−4) increased probability of low symptom severity. Higher genotype-predicted CYP2C19 enzyme activity was independently associated with lower symptom severity (p = 8.44×10−3). While no locus surpassed the genome-wide significance threshold, rs1923778 within NFIB showed a suggestive association (p = 3.78×10−7) with symptom severity. We show that high schizophrenia-PRS and genotype-predicted CYP2C19 enzyme activity are independently associated with lower symptom severity among individuals treated with clozapine. Our findings open avenues for future pharmacogenomic projects investigating the potential of PRS and genotype-predicted CYP-activity in schizophrenia

Complementarity Between a Docking and a High-Throughput Screen in Discovering New Cruzain Inhibitors†

Virtual and high-throughput screens (HTS) should have complementary strengths and weaknesses, but studies that prospectively and comprehensively compare them are rare. We undertook a parallel docking and HTS screen of 197861 compounds against cruzain, a thiol protease target for Chagas disease, looking for reversible, competitive inhibitors. On workup, 99 % of the hits were eliminated as false positives, yielding 146 well-behaved, competitive ligands. These fell into five chemotypes: two were prioritized by scoring among the top 0.1 % of the docking-ranked library, two were prioritized by behavior in the HTS and by clustering, and one chemotype was prioritized by both approaches. Determination of an inhibitor/cruzain crystal structure and comparison of the high-scoring docking hits to experiment illuminated the origins of docking false-negatives and false-positives. Prioritizing molecules that are both predicted by docking and are HTS-active yields well-behaved molecules, relatively unobscured by the false-positives to which both techniques are individually prone

Introduction of an agent-based multi-scale modular architecture for dynamic knowledge representation of acute inflammation

<p>Abstract</p> <p>Background</p> <p>One of the greatest challenges facing biomedical research is the integration and sharing of vast amounts of information, not only for individual researchers, but also for the community at large. Agent Based Modeling (ABM) can provide a means of addressing this challenge via a unifying translational architecture for dynamic knowledge representation. This paper presents a series of linked ABMs representing multiple levels of biological organization. They are intended to translate the knowledge derived from in vitro models of acute inflammation to clinically relevant phenomenon such as multiple organ failure.</p> <p>Results and Discussion</p> <p>ABM development followed a sequence starting with relatively direct translation from in-vitro derived rules into a cell-as-agent level ABM, leading on to concatenated ABMs into multi-tissue models, eventually resulting in topologically linked aggregate multi-tissue ABMs modeling organ-organ crosstalk. As an underlying design principle organs were considered to be functionally composed of an epithelial surface, which determined organ integrity, and an endothelial/blood interface, representing the reaction surface for the initiation and propagation of inflammation. The development of the epithelial ABM derived from an in-vitro model of gut epithelial permeability is described. Next, the epithelial ABM was concatenated with the endothelial/inflammatory cell ABM to produce an organ model of the gut. This model was validated against in-vivo models of the inflammatory response of the gut to ischemia. Finally, the gut ABM was linked to a similarly constructed pulmonary ABM to simulate the gut-pulmonary axis in the pathogenesis of multiple organ failure. The behavior of this model was validated against in-vivo and clinical observations on the cross-talk between these two organ systems</p> <p>Conclusion</p> <p>A series of ABMs are presented extending from the level of intracellular mechanism to clinically observed behavior in the intensive care setting. The ABMs all utilize cell-level agents that encapsulate specific mechanistic knowledge extracted from in vitro experiments. The execution of the ABMs results in a dynamic representation of the multi-scale conceptual models derived from those experiments. These models represent a qualitative means of integrating basic scientific information on acute inflammation in a multi-scale, modular architecture as a means of conceptual model verification that can potentially be used to concatenate, communicate and advance community-wide knowledge.</p

P2P Cloud System

P2PCS is a Java-based prototype implementation of a Cloud system based on P2P protocols. P2P Clouds allow organizations or even individual to build a computing infrastructure out of existing resources, which can be easily allocated among different tasks. P2PCS aims at demonstrating that gossip-based protocols can be used to maintain an overlay network on top of the computing nodes, and to partition the set of resources into multiple slices in such a way that the failure of individual nodes do not compromise the overall structure

Design and Implementation of a P2P Cloud System

Cloud Computing has gained popularity in both research and industrial communities. Cloud users can acquire computing resources on a need basis, achieving on demand scalability; Cloud providers can maximize resource utilizations of datacenters, increasing their return on investments. While Cloud systems are usually hosted in large datacenters and are centrally managed, other types of Cloud architectures can be imagined. In this paper we describe the design and prototype implementation of a fully decentralized, P2P Cloud. A P2P Cloud allows organizations or even individual to build a computing infrastructure out of existing resources, which can be easily allocated among different tasks. We focus on the problem of maintaining a coherent structure over a set of unreliable computing resources. We show that gossip-based protocols can be used to maintain an overlay network on top of the computing nodes, and to partition the set of resources into multiple slices in such a way that the failure of individual nodes do not compromise the overall structure. Resource partitioning is one of the most important features of a Cloud, and therefore must be supported efficiently and reliably on any Cloud architecture. We describe a prototype Java implementation that is being developed to demonstrate the effectiveness of the proposed approach

The Bootstrapping Service

We outline a lightweight architecture to support novel application scenarios for P2P systems. These scenarios include merging and splitting of large networks, or multiplexing relatively short-lived applications over a pool of shared resources. In such scenarios, the architecture needs to be quickly and efficiently (re)generated frequently, often from scratch. We propose the bootstrap- ping service abstraction as a solution to this problem. We present an instance of the service that can jump-start any prefixtable based routing substrate quickly, cheaply and reliably from scratch. We experimentally analyze the proposed bootstrapping service, demonstrating its scalability and robustness

A Modular Paradigm for Building Self-Organizing Peer-to-Peer Applications

Peer-to-peer (P2P) technology has undergone rapid growth, producing new protocols and applications, many of which enjoy considerable commercial success and academic interest. Yet, P2P applications are often based on com- plex protocols, whose behavior is not completely understood. We believe that in order to enable an even more widespread adoption of P2P systems in commer- cial and scientific applications, what is needed is a modular paradigm in which well-understood, predictable components with clean interfaces can be combined to implement arbitrarily complex functions. The goal of this paper is to promote this idea by describing our initial experiences in this direction. Our recent work has resulted in a collection of simple and robust components, which include ag- gregation and membership management. This paper shows how to combine them to obtain a novel load-balancing algorithm that is close to optimal with respect to load transfer. We also describe briefly our simulation environment, explicitly designed to efficiently support our modular approach to P2P protocol design