Analysis, classification and comparison of scheduling techniques for software transactional memories

Transactional Memory (TM) is a practical programming paradigm for developing concurrent applications. Performance is a critical factor for TM implementations, and various studies demonstrated that specialised transaction/thread scheduling support is essential for implementing performance-effective TM systems. After one decade of research, this article reviews the wide variety of scheduling techniques proposed for Software Transactional Memories. Based on peculiarities and differences of the adopted scheduling strategies, we propose a classification of the existing techniques, and we discuss the specific characteristics of each technique. Also, we analyse the results of previous evaluation and comparison studies, and we present the results of a new experimental study encompassing techniques based on different scheduling strategies. Finally, we identify potential strengths and weaknesses of the different techniques, as well as the issues that require to be further investigated

An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System

Incorporating speed probability distribution to the computation of the route planning in car navigation systems guarantees more accurate and precise responses. In this paper, we propose a novel approach for dynamically selecting the number of samples used for the Monte Carlo simulation to solve the Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the computation efficiency. The proposed method is used to determine in a proactive manner the number of simulations to be done to extract the travel-time estimation for each specific request while respecting an error threshold as output quality level. The methodology requires a reduced effort on the application development side. We adopted an aspect-oriented programming language (LARA) together with a flexible dynamic autotuning library (mARGOt) respectively to instrument the code and to take tuning decisions on the number of samples improving the execution efficiency. Experimental results demonstrate that the proposed adaptive approach saves a large fraction of simulations (between 36% and 81%) with respect to a static approach while considering different traffic situations, paths and error requirements. Given the negligible runtime overhead of the proposed approach, it results in an execution-time speedup between 1.5x and 5.1x. This speedup is reflected at infrastructure-level in terms of a reduction of around 36% of the computing resources needed to support the whole navigation pipeline

Predicting Policy Violations in Policy Based Proactive Systems Management

The continuous development and advancement in networking, computing, software and web technologies have led to an explosive growth in distributed systems. To ensure better quality of service (QoS), management of large scale distributed systems is important. The increasing complexity of distributed systems requires significantly higher levels of automation in system management. The core of autonomie computing is the ability to analyze data about the distributed system and to take actions. Such autonomic management should include some ability to anticipate potential problems and take action to avoid them that is, it should be proactive. System management should be proactive in order to be able to identify possible faults before they occur and before they can result in severe degradation in performance. In this thesis, our goal is to predict policy violations and take actions ahead of time in order to achieve proactive management in a policy based system.We implemented different prediction algorithm to predict policy violations. Based on the prediction decision, proactive actions are implemented in the system. Adaptive proactive action approach is also introduced to increase the performance of the proactive management system

Trading-Off Machine Learning Algorithms towards Data-Driven Administrative-Socio-Economic Population Health Management

Together with population ageing, the number of people suffering from multimorbidity is increasing, up to more than half of the population by 2035. This part of the population is composed by the highest-risk patients, who are, at the same time, the major users of the healthcare systems. The early identification of this sub-population can really help to improve people’s quality of life and reduce healthcare costs. In this paper, we describe a population health management tool based on state-of-the-art intelligent algorithms, starting from administrative and socio-economic data, for the early identification of high-risk patients. The study refers to the population of the Local Health Unit of Central Tuscany in 2015, which amounts to 1,670,129 residents. After a trade-off on machine learning models and on input data, Random Forest applied to 1-year of historical data achieves the best results, outperforming state-of-the-art models. The most important variables for this model, in terms of mean minimal depth, accuracy decrease and Gini decrease, result to be age and some group of drugs, such as high-ceiling diuretics. Thanks to the low inference time and reduced memory usage, the resulting model allows for real-time risk prediction updates whenever new data become available, giving General Practitioners the possibility to early adopt personalised medicine

Utilizing Data Mining Techniques and Ensemble Learning to Predict Development of Surgical Site Infections in Gynecologic Cancer Patients

Surgical site infections are costly to both patients and hospitals, increase patient mortality, and are the most common form of a hospital acquired infection. Gynecological cancer surgery patients are already at higher risk of developing an infection due to the suppression of their immune system. This research leverages popular data mining techniques to create a prediction model to identify high risk patients. Implemented techniques include logistic regression, naive Bayes, recursive partitioning and regression trees, random forest, feed forward neural network, k-nearest neighbor, and support vector machines with linear kernel. Weighted stacked generalization was implemented to improve upon the individual base level model’s performance. The chosen meta level classifiers were support vector machines with linear kernel, logistic regression, and k-nearest neighbor. The result is a model that identifies high-risk patients immediately following a surgical procedure with an AUC of 0.6864, accuracy of 0.6744, sensitivity of 0.7, and specificity of 0.6728

Trade-off among timeliness, messages and accuracy for large-Ssale information management

The increasing amount of data and the number of nodes in large-scale environments require new techniques for information management. Examples of such environments are the decentralized infrastructures of Computational Grid and Computational Cloud applications. These large-scale applications need different kinds of aggregated information such as resource monitoring, resource discovery or economic information. The challenge of providing timely and accurate information in large scale environments arise from the distribution of the information. Reasons for delays in distributed information system are a long information transmission time due to the distribution, churn and failures. A problem of large applications such as peer-to-peer (P2P) systems is the increasing retrieval time of the information due to the decentralization of the data and the failure proneness. However, many applications need a timely information provision. Another problem is an increasing network consumption when the application scales to millions of users and data. Using approximation techniques allows reducing the retrieval time and the network consumption. However, the usage of approximation techniques decreases the accuracy of the results. Thus, the remaining problem is to offer a trade-off in order to solve the conflicting requirements of fast information retrieval, accurate results and low messaging cost. Our goal is to reach a self-adaptive decision mechanism to offer a trade-off among the retrieval time, the network consumption and the accuracy of the result. Self-adaption enables distributed software to modify its behavior based on changes in the operating environment. In large-scale information systems that use hierarchical data aggregation, we apply self-adaptation to control the approximation used for the information retrieval and reduces the network consumption and the retrieval time. The hypothesis of the thesis is that approximation techniquescan reduce the retrieval time and the network consumption while guaranteeing an accuracy of the results, while considering user’s defined priorities. First, this presented research addresses the problem of a trade-off among a timely information retrieval, accurate results and low messaging cost by proposing a summarization algorithm for resource discovery in P2P-content networks. After identifying how summarization can improve the discovery process, we propose an algorithm which uses a precision-recall metric to compare the accuracy and to offer a user-driven trade-off. Second, we propose an algorithm that applies a self-adaptive decision making on each node. The decision is about the pruning of the query and returning the result instead of continuing the query. The pruning reduces the retrieval time and the network consumption at the cost of a lower accuracy in contrast to continuing the query. The algorithm uses an analytic hierarchy process to assess the user’s priorities and to propose a trade-off in order to satisfy the accuracy requirements with a low message cost and a short delay. A quantitative analysis evaluates our presented algorithms with a simulator, which is fed with real data of a network topology and the nodes’ attributes. The usage of a simulator instead of the prototype allows the evaluation in a large scale of several thousands of nodes. The algorithm for content summarization is evaluated with half a million of resources and with different query types. The selfadaptive algorithm is evaluated with a simulator of several thousands of nodes that are created from real data. A qualitative analysis addresses the integration of the simulator’s components in existing market frameworks for Computational Grid and Cloud applications. The proposed content summarization algorithm reduces the information retrieval time from a logarithmic increase to a constant factor. Furthermore, the message size is reduced significantly by applying the summarization technique. For the user, a precision-recall metric allows defining the relation between the retrieval time and the accuracy. The self-adaptive algorithm reduces the number of messages needed from an exponential increase to a constant factor. At the same time, the retrieval time is reduced to a constant factor under an increasing number of nodes. Finally, the algorithm delivers the data with the required accuracy adjusting the depth of the query according to the network conditions.La gestió de la informació exigeix noves tècniques que tractin amb la creixent quantitat de dades i nodes en entorns a gran escala. Alguns exemples d’aquests entorns són les infraestructures descentralitzades de Computacional Grid i Cloud. Les aplicacions a gran escala necessiten diferents classes d’informació agregada com monitorització de recursos i informació econòmica. El desafiament de proporcionar una provisió ràpida i acurada d’informació en ambients de grans escala sorgeix de la distribució de la informació. Una raó és que el sistema d’informació ha de tractar amb l’adaptabilitat i fracassos d’aquests ambients. Un problema amb aplicacions molt grans com en sistemes peer-to-peer (P2P) és el creixent temps de recuperació de l’informació a causa de la descentralització de les dades i la facilitat al fracàs. No obstant això, moltes aplicacions necessiten una provisió d’informació puntual. A més, alguns usuaris i aplicacions accepten inexactituds dels resultats si la informació es reparteix a temps. A més i més, el consum de xarxa creixent fa que sorgeixi un altre problema per l’escalabilitat del sistema. La utilització de tècniques d’aproximació permet reduir el temps de recuperació i el consum de xarxa. No obstant això, l’ús de tècniques d’aproximació disminueix la precisió dels resultats. Així, el problema restant és oferir un compromís per resoldre els requisits en conflicte d’extracció de la informació ràpida, resultats acurats i cost d’enviament baix. El nostre objectiu és obtenir un mecanisme de decisió completament autoadaptatiu per tal d’oferir el compromís entre temps de recuperació, consum de xarxa i precisió del resultat. Autoadaptacío permet al programari distribuït modificar el seu comportament en funció dels canvis a l’entorn d’operació. En sistemes d’informació de gran escala que utilitzen agregació de dades jeràrquica, l’auto-adaptació permet controlar l’aproximació utilitzada per a l’extracció de la informació i redueixen el consum de xarxa i el temps de recuperació. La hipòtesi principal d’aquesta tesi és que els tècniques d’aproximació permeten reduir el temps de recuperació i el consum de xarxa mentre es garanteix una precisió adequada definida per l’usari. La recerca que es presenta, introdueix un algoritme de sumarització de continguts per a la descoberta de recursos a xarxes de contingut P2P. Després d’identificar com sumarització pot millorar el procés de descoberta, proposem una mètrica que s’utilitza per comparar la precisió i oferir un compromís definit per l’usuari. Després, introduïm un algoritme nou que aplica l’auto-adaptació a un ordre per satisfer els requisits de precisió amb un cost de missatge baix i un retard curt. Basat en les prioritats d’usuari, l’algoritme troba automàticament un compromís. L’anàlisi quantitativa avalua els algoritmes presentats amb un simulador per permetre l’evacuació d’uns quants milers de nodes. El simulador s’alimenta amb dades d’una topologia de xarxa i uns atributs dels nodes reals. L’algoritme de sumarització de contingut s’avalua amb mig milió de recursos i amb diferents tipus de sol·licituds. L’anàlisi qualitativa avalua la integració del components del simulador en estructures de mercat existents per a aplicacions de Computacional Grid i Cloud. Així, la funcionalitat implementada del simulador (com el procés d’agregació i la query language) és comprovada per la integració de prototips. L’algoritme de sumarització de contingut proposat redueix el temps d’extracció de l’informació d’un augment logarítmic a un factor constant. A més, també permet que la mida del missatge es redueix significativament. Per a l’usuari, una precision-recall mètric permet definir la relació entre el nivell de precisió i el temps d’extracció de la informació. Alhora, el temps de recuperació es redueix a un factor constant sota un nombre creixent de nodes. Finalment, l’algoritme reparteix les dades amb la precisió exigida i ajusta la profunditat de la sol·licitud segons les condicions de xarxa. Els algoritmes introduïts són prometedors per ser utilitzats per l’agregació d’informació en nous sistemes de gestió de la informació de gran escala en el futur