Lending Petri nets and contracts

Choreography-based approaches to service composition typically assume that, after a set of services has been found which correctly play the roles prescribed by the choreography, each service respects his role. Honest services are not protected against adversaries. We propose a model for contracts based on a extension of Petri nets, which allows services to protect themselves while still realizing the choreography. We relate this model with Propositional Contract Logic, by showing a translation of formulae into our Petri nets which preserves the logical notion of agreement, and allows for compositional verification

Cloud service localisation

The essence of cloud computing is the provision of software and hardware services to a range of users in dierent locations. The aim of cloud service localisation is to facilitate the internationalisation and localisation of cloud services by allowing their adaption to dierent locales. We address the lingual localisation by providing service-level language translation techniques to adopt services to dierent languages and regulatory localisation by providing standards-based mappings to achieve regulatory compliance with regionally varying laws, standards and regulations. The aim is to support and enforce the explicit modelling of aspects particularly relevant to localisation and runtime support consisting of tools and middleware services to automating the deployment based on models of locales, driven by the two localisation dimensions. We focus here on an ontology-based conceptual information model that integrates locale specication in a coherent way

The Case for Learned Index Structures

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of lookup keys and use this signal to effectively predict the position or existence of records. We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show, that by using neural nets we are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. More importantly though, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible

A cost-effective methodology applied to videoconference services over hybrid clouds

This paper tackles the optimization of applications in multi-provider hybrid cloud scenarios from an economic point of view. In these scenarios the great majority of solutions offer the automatic allocation of resources on different cloud providers based on their current prices. However our approach is intended to introduce a novel solution by making maximum use of divide and rule. This paper describes a methodology to create cost aware cloud applications that can be broken down into the three most important components in cloud infrastructures: computation, network and storage. A real videoconference system has been modified in order to evaluate this idea with both theoretical and empirical experiments. This system has become a widely used tool in several national and European projects for e-learning and collaboration purposes

Continuous-action reinforcement learning for memory allocation in virtualized servers

In a virtualized computing server (node) with multiple Virtual Machines (VMs), it is necessary to dynamically allocate memory among the VMs. In many cases, this is done only considering the memory demand of each VM without having a node-wide view. There are many solutions for the dynamic memory allocation problem, some of which use machine learning in some form. This paper introduces CAVMem (Continuous-Action Algorithm for Virtualized Memory Management), a proof-of-concept mechanism for a decentralized dynamic memory allocation solution in virtualized nodes that applies a continuous-action reinforcement learning (RL) algorithm called Deep Deterministic Policy Gradient (DDPG). CAVMem with DDPG is compared with other RL algorithms such as Q-Learning (QL) and Deep Q-Learning (DQL) in an environment that models a virtualized node. In order to obtain linear scaling and be able to dynamically add and remove VMs, CAVMem has one agent per VM connected via a lightweight coordination mechanism. The agents learn how much memory to bid for or return, in a given state, so that each VM obtains a fair level of performance subject to the available memory resources. Our results show that CAVMem with DDPG performs better than QL and a static allocation case, but it is competitive with DQL. However, CAVMem incurs significant less training overheads than DQL, making the continuous-action approach a more cost-effective solution.This research is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 754337 (EuroEXA) and the European Union’s 7th Framework Programme under grant agreement number 610456 (Euroserver). It also received funding from the Spanish Ministry of Science and Technology (project TIN2015-65316-P), Generalitat de Catalunya (contract 2014-SGR-1272), and the Severo Ochoa Programme (SEV-2015-0493) of the Spanish Government.Peer ReviewedPostprint (author's final draft

Physical and psychosocial health in pediatric uveitis patients

Background: To investigate the possible associations between childhood noninfectious uveitis and cardio-respiratory fitness, physical activity, health related quality of life and fatigue. Methods: Cross-sectional analysis of 23 patients with noninfectious uveitis, aged 8-18 years. BMI, exercise capacity, muscle strength and physical activity were measured. Health-related quality of life and fatigue were assessed. The results were compared to standardized values for age matched healthy children. Results: Twenty-three patients were included. Children with uveitis had a higher bodyweight and body mass index. Children with uveitis had lower cardio-respiratory fitness and they were less physically active, but they experienced a normal quality of life and normal fatigue. Parents of children with uveitis reported a lower quality of life and more fatigue for their children than parents of healthy children. Conclusion: Our study indicates that children with noninfectious uveitis are at risk of developing lower physical and psychosocial health

Identifying Potential Risks and Benefits of Using Cloud in Distributed Software Development

Cloud-based infrastructure has been increasingly adopted by the industry in distributed software development (DSD) environments. Its proponents claim that its several benefits include reduced cost, increased speed and greater productivity in software development. Empirical evaluations, however, are in the nascent stage of examining both the benefits and the risks of cloud-based in-frastructure. The objective of this paper is to identify potential benefits and risks of using cloud in a DSD project conducted by teams based in Helsinki and Ma-drid. A cross-case qualitative analysis is performed based on focus groups con-ducted at the Helsinki and Madrid sites. Participants’ observations are used to supplement the analysis. The results of the analysis indicated that the main ben-efits of using cloud are rapid development, continuous integration, cost savings, code sharing, and faster ramp-up. The key risks determined by the project are dependencies, unavailability of access to the cloud, code commitment and inte-gration, technical debt, and additional support costs. The results revealed that if such environments are not planned and set up carefully, the benefits of using cloud in DSD projects might be overshadowed by the risks associated with it.Peer reviewe

An effective scalable SQL engine for NoSQL databases

NoSQL databases were initially devised to support a few concrete extreme scale applications. Since the specificity and scale of the target systems justified the investment of manually crafting application code their limited query and indexing capabilities were not a major im- pediment. However, with a considerable number of mature alternatives now available there is an increasing willingness to use NoSQL databases in a wider and more diverse spectrum of applications and, to most of them, hand-crafted query code is not an enticing trade-off. In this paper we address this shortcoming of current NoSQL databases with an effective approach for executing SQL queries while preserving their scalability and schema flexibility. We show how a full-fledged SQL engine can be integrated atop of HBase leading to an ANSI SQL compli- ant database. Under a standard TPC-C workload our prototype scales linearly with the number of nodes in the system and outperforms a NoSQL TPC-C implementation optimized for HBase.(undefined