Model-driven development of data intensive applications over cloud resources

The proliferation of sensors over the last years has generated large amounts of raw data, forming data streams that need to be processed. In many cases, cloud resources are used for such processing, exploiting their flexibility, but these sensor streaming applications often need to support operational and control actions that have real-time and low-latency requirements that go beyond the cost effective and flexible solutions supported by existing cloud frameworks, such as Apache Kafka, Apache Spark Streaming, or Map-Reduce Streams. In this paper, we describe a model-driven and stepwise refinement methodological approach for streaming applications executed over clouds. The central role is assigned to a set of Petri Net models for specifying functional and non-functional requirements. They support model reuse, and a way to combine formal analysis, simulation, and approximate computation of minimal and maximal boundaries of non-functional requirements when the problem is either mathematically or computationally intractable. We show how our proposal can assist developers in their design and implementation decisions from a performance perspective. Our methodology allows to conduct performance analysis: The methodology is intended for all the engineering process stages, and we can (i) analyse how it can be mapped onto cloud resources, and (ii) obtain key performance indicators, including throughput or economic cost, so that developers are assisted in their development tasks and in their decision taking. In order to illustrate our approach, we make use of the pipelined wavefront array.Comment: Preprin

Model-driven development of data intensive applications over cloud resources

The proliferation of sensors over the last years has generated large amounts of raw data, forming data streams that need to be processed. In many cases, cloud resources are used for such processing, exploiting their flexibility, but these sensor streaming applications often need to support operational and control actions that have real-time and low-latency requirements that go beyond the cost effective and flexible solutions supported by existing cloud frameworks, such as Apache Kafka, Apache Spark Streaming, or Map-Reduce Streams. In this paper, we describe a model-driven and stepwise refinement methodological approach for streaming applications executed over clouds. The central role is assigned to a set of Petri Net models for specifying functional and non-functional requirements. They support model reuse, and a way to combine formal analysis, simulation, and approximate computation of minimal and maximal boundaries of non-functional requirements when the problem is either mathematically or computationally intractable. We show how our proposal can assist developers in their design and implementation decisions from a performance perspective. Our methodology allows to conduct performance analysis: The methodology is intended for all the engineering process stages, and we can (i) analyse how it can be mapped onto cloud resources, and (ii) obtain key performance indicators, including throughput or economic cost, so that developers are assisted in their development tasks and in their decision taking. In order to illustrate our approach, we make use of the pipelined wavefront array

A Specification Language for Performance and Economical Analysis of Short Term Data Intensive Energy Management Services

Requirements of Energy Management Services include short and long term processing of data in a massively interconnected scenario. The complexity and variety of short term applications needs methodologies that allow designers to reason about the models taking into account functional and non-functional requirements. In this paper we present a component based specification language for building trustworthy continuous dataflow applications. Component behaviour is defined by Petri Nets in order to translate to the methodology all the advantages derived from a mathematically based executable model to support analysis, verification, simulation and performance evaluation. The paper illustrates how to model and reason with specifications of advanced dataflow abstractions such as smart grids

Enforcing QoS in scientific workflow systems enacted over Cloud infrastructures

AbstractThe ability to support Quality of Service (QoS) constraints is an important requirement in some scientific applications. With the increasing use of Cloud computing infrastructures, where access to resources is shared, dynamic and provisioned on-demand, identifying how QoS constraints can be supported becomes an important challenge. However, access to dedicated resources is often not possible in existing Cloud deployments and limited QoS guarantees are provided by many commercial providers (often restricted to error rate and availability, rather than particular QoS metrics such as latency or access time). We propose a workflow system architecture which enforces QoS for the simultaneous execution of multiple scientific workflows over a shared infrastructure (such as a Cloud environment). Our approach involves multiple pipeline workflow instances, with each instance having its own QoS requirements. These workflows are composed of a number of stages, with each stage being mapped to one or more physical resources. A stage involves a combination of data access, computation and data transfer capability. A token bucket-based data throttling framework is embedded into the workflow system architecture. Each workflow instance stage regulates the amount of data that is injected into the shared resources, allowing for bursts of data to be injected while at the same time providing isolation of workflow streams. We demonstrate our approach by using the Montage workflow, and develop a Reference net model of the workflow

Catalytic and molecular insights of the esterification of ibuprofen and ketoprofen with glycerol

The esterification of rac-ibuprofen and rac-ketoprofen with glycerol catalyzed with the commercial biocatalyst Novozym® 435 was investigated at 45 °C with various profen: glycerol molar ratios using 2-propanol as co-solvent in a batch type reaction. The conversion of rac-ibuprofen reached 46%, with an enantiomeric excess towards the S-enantiomer of 42%. When 1:4 ibuprofen:glycerol molar ratio was assayed, 75% of the R-ibuprofen reacted with glycerol towards the monoglyceride with 99% selectivity, which is highly relevant in the field of prodrugs synthesis. The conversion of rac-ketoprofen was lower, 17 % vs. 46 % of rac-ibuprofen, and the esterification afforded both the monoglyceride (70%) and diglyceride (30%) regardless of the ketoprofen:glycerol molar ratio. Investigations of the esterification at molecular level through concentration-modulated infrared spectroscopy, static ATR-FTIR and in situ Raman spectroscopy showed the continuous decay of the species belonging to rac-ibuprofen and glycerol providing further evidences of the reaction. Moreover, the interaction of CALB with ibuprofen modifies the contribution of the ordered structures of the lipase, which might be related with the improved catalytic performance in the esterification of that profen.Fil: Toledo, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Ciencias Aplicadas "Dr. Jorge J. Ronco". Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Ciencias Aplicadas; ArgentinaFil: José, Carla. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Ciencias Aplicadas "Dr. Jorge J. Ronco". Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Ciencias Aplicadas; ArgentinaFil: Llerena Suster, Carlos Rafael. Facultad de Ciencias Exactas, Universidad Nacional de la Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Ciencias Aplicadas "Dr. Jorge J. Ronco". Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Ciencias Aplicadas; ArgentinaFil: Collins, Sebastián Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Portela, Raquel. Consejo Superior de Investigaciones Científicas. Instituto de Tecnología Química; EspañaFil: Bañares, Miguel A.. Consejo Superior de Investigaciones Científicas. Instituto de Tecnología Química; EspañaFil: Briand, Laura Estefania. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Ciencias Aplicadas "Dr. Jorge J. Ronco". Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Ciencias Aplicadas; Argentin

Modelling serverless function behaviours

The serverless computing model extends potential deployment options for cloud applications, by allowing users to focus on building and deploying their code without needing to configure or manage the underlying computational resources. Cost and latency constraints in stream processing user applications often push computations closer to the sources of data, leading to challenges for dynamically distributing stream operators across the edge/fog/cloud heterogeneous nodes and the routing of data flows. Various approaches to support operator placement across edge and cloud resources and data routing are beginning to be addressed through the serverless model. Understanding how stream processing operators can be mapped into serverless functions also offers cost incentives for users – as charging is now on a subsecond basis (rather than hourly). A dynamic Petri net model of serverless functions is proposed in this work, which takes account of the computational requirements of functions, the resources on which these functions are hosted, and key parameters that impact the behaviour of serverless functions – such as warm/cold start up times. The model can be used by developers/users of serverless functions to understand how deployment optimisation can be used to reduce application time, and to analyse various scenarios on choosing function granularity, data size and cost

Enoxaparin does not ameliorate liver fibrosis or portal hypertension in rats with advanced cirrhosis

Background & Aims Recent studies suggest that heparins reduce liver fibrosis and the risk of decompensation of liver disease. Here, we evaluated the effects of enoxaparin in several experimental models of advanced cirrhosis. Methods Cirrhosis was induced in male Sprague‐Dawley (SD) rats by: (i) Oral gavage with carbon tetrachloride (CCl4ORAL), (ii) Bile duct ligation (BDL) and (iii) CCl4 inhalation (CCl4INH). Rats received saline or enoxaparin s.c. (40 IU/Kg/d or 180 IU/Kg/d) following various protocols. Blood biochemical parameters, liver fibrosis, endothelium‐ and fibrosis‐related genes, portal pressure, splenomegaly, bacterial translocation, systemic inflammation and survival were evaluated. Endothelial dysfunction was assessed by in situ bivascular liver perfusions. Results Enoxaparin did not ameliorate liver function, liver fibrosis, profibrogenic gene expression, portal hypertension, splenomegaly, ascites development and infection, serum IL‐6 levels or survival in rats with CCl4ORAL or BDL‐induced cirrhosis. Contrarily, enoxaparin worsened portal pressure in BDL rats and decreased survival in CCl4ORAL rats. In CCl4INH rats, enoxaparin had no effects on hepatic endothelial dysfunction, except for correcting the hepatic arterial dysfunction when enoxaparin was started with the CCl4 exposure. In these rats, however, enoxaparin increased liver fibrosis and the absolute values of portal venous and sinusoidal resistance. Conclusions Our results do not support a role of enoxaparin for improving liver fibrosis, portal hypertension or endothelial dysfunction in active disease at advanced stages of cirrhosis. These disease‐related factors and the possibility of a limited therapeutic window should be considered in future studies evaluating the use of anticoagulants in cirrhosis

Characterising resource management performance in Kubernetes

A key challenge for supporting elastic behaviour in cloud systems is to achieve a good performance in automated (de-)provisioning and scheduling of computing resources. One of the key aspects that can be significant is the overheads associated with deploying, terminating and maintaining resources. Therefore, due to their lower start up and termination overhead, containers are rapidly replacing Virtual Machines (VMs) in many cloud deployments, as the computation instance of choice. In this paper, we analyse the performance of Kubernetes achieved through a Petri net-based performance model. Kubernetes is a container management system for a distributed cluster environment. Our model can be characterised using data from a Kubernetes deployment, and can be exploited for supporting capacity planning and designing Kubernetes-based elastic applications

Genetic variants of innate immunity receptors are associated with mortality in cirrhotic patients with bacterial infection

BACKGROUND & AIMS: Acute-on-chronic liver failure (ACLF) is characterized by acute decompensation of cirrhosis (AD), organ failure(s) and high risk of short-term mortality with bacterial infection frequently as precipitating event. Innate immune pattern recognition receptors and members of the lectin pathway of complement activation are crucial to the innate immune response to pathogens. The aim of this study was to investigate whether single nucleotide polymorphisms (SNPs) of innate immune components are associated with the occurrence of bacterial infections or mortality in patients with cirrhosis hospitalized for AD or ACLF. METHODS: Twenty-one innate immunity SNPs with known functional implications were genotyped in 826 AD/ACLF patients included in the CANONIC study. Associations between baseline characteristics of the patients, the occurrence of bacterial infections and survival rate at 90 days of follow-up in relation to the innate immunity genetic variants were analysed. RESULTS: The NOD2-G908R genetic variant was associated with mortality (HR 2.25, P = .004) independently of age and MELD Score. This association was also found in a predefined subgroup analysis in patients with bacterial infections (HR 2.78, P < .001) along with MBL_Yx (HR 1.72, P = .008) and MASP2_371 (HR 1.67, P = .012) genetic variants. None of the analysed SNPs were significantly associated with the occurrence of acute bacterial infections or spontaneous bacterial peritonitis in particular. CONCLUSIONS: Innate immune system-specific NOD2-G908R, MBL_Yx and MASP2_371 genetic variants were independently associated with increased risk of short-term mortality in AD/ACLF patients with bacterial infection