Proactive cloud management for highly heterogeneous multi-cloud infrastructures

Various literature studies demonstrated that the cloud computing paradigm can help to improve availability and performance of applications subject to the problem of software anomalies. Indeed, the cloud resource provisioning model enables users to rapidly access new processing resources, even distributed over different geographical regions, that can be promptly used in the case of, e.g., crashes or hangs of running machines, as well as to balance the load in the case of overloaded machines. Nevertheless, managing a complex geographically-distributed cloud deploy could be a complex and time-consuming task. Autonomic Cloud Manager (ACM) Framework is an autonomic framework for supporting proactive management of applications deployed over multiple cloud regions. It uses machine learning models to predict failures of virtual machines and to proactively redirect the load to healthy machines/cloud regions. In this paper, we study different policies to perform efficient proactive load balancing across cloud regions in order to mitigate the effect of software anomalies. These policies use predictions about the mean time to failure of virtual machines. We consider the case of heterogeneous cloud regions, i.e regions with different amount of resources, and we provide an experimental assessment of these policies in the context of ACM Framework

Machine Learning for Achieving Self-* Properties and Seamless Execution of Applications in the Cloud

Software anomalies are recognized as a major problem affecting the performance and availability of many computer systems. Accumulation of anomalies of different nature, such as memory leaks and unterminated threads, may lead the system to both fail or work with suboptimal performance levels. This problem particularly affects web servers, where hosted applications are typically intended to continuously run, thus incrementing the probability, therefore the associated effects, of accumulation of anomalies. Given the unpredictability of occurrence of anomalies, continuous system monitoring would be required to detect possible system failures and/or excessive performance degradation in order to timely start some recovering procedure. In this paper, we present a Machine Learning-based framework for proactive management of client-server applications in the cloud. Through optimized Machine Learning models and continually measuring system features, the framework predicts the remaining time to the occurrence of some unexpected event (system failure, service level agreement violation, etc.) of a virtual machine hosting a server instance of the application. The framework is able to manage virtual machines in the presence of different types anomalies and with different anomaly occurrence patterns. We show the effectiveness of the proposed solution by presenting results of a set of experiments we carried out in the context of a real world-inspired scenario

A Machine Learning-based Framework for Building Application Failure Prediction Models

In this paper, we present the Framework for building Failure Prediction Models (F2PM), a Machine Learning-based Framework to build models for predicting the Remaining Time to Failure (RTTF) of applications in the presence of software anomalies. F2PM uses measurements of a number of system features in order to create a knowledge base, which is then used to build prediction models. F2PM is application-independent, i.e. It solely exploits measurements of system-level features. Thus, it can be used in differentiated contexts, without the need for any manual modification or intervention to the running applications. To generate optimized models, F2PM can perform a feature selection to identify, among all the measured system features, which have a major impact in the prediction of the RTTF. This allows to produce different models, which use different set of input features. Generated models can be compared by the user by using a set of metrics produced by F2PM, which are related to the model prediction accuracy, as well as to the model building time. We also present experimental results of a successful application of F2PM, using the standard TPC-W e-commerce benchmark

Proactive Scalability and Management of Resources in Hybrid Clouds via Machine Learning

In this paper, we present a novel framework for supporting the management and optimization of application subject to software anomalies and deployed on large scale cloud architectures, composed of different geographically distributed cloud regions. The framework uses machine learning models for predicting failures caused by accumulation of anomalies. It introduces a novel workload balancing approach and a proactive system scale up/scale down technique. We developed a prototype of the framework and present some experiments for validating the applicability of the proposed approache

Prediction and Simulator Verification of Roll/Lateral Adverse Aeroservoelastic Rotorcraft–Pilot Couplings

The involuntary interaction of a pilot with an aircraft can be described as pilot-assisted oscillations. Such phenomena are usually only addressed late in the design process when they manifest themselves during ground/flight testing. Methods to be able to predict such phenomena as early as possible are therefore useful. This work describes a technique to predict the adverse aeroservoelastic rotorcraft–pilot couplings, specifically between a rotorcraft’s roll motion and the resultant involuntary pilot lateral cyclic motion. By coupling linear vehicle aeroservoelastic models and experimentally identified pilot biodynamic models, pilot-assisted oscillations and no-pilot-assisted oscillation conditions have been numerically predicted for a soft-in-plane hingeless helicopter with a lightly damped regressive lead–lag mode that strongly interacts with the roll modeat a frequency within the biodynamic band of the pilots. These predictions have then been verified using real-time flight-simulation experiments. The absence of any similar adverse couplings experienced while using only rigid-body models in the flight simulator verified that the observed phenomena were indeed aeroelastic in nature. The excellent agreement between the numerical predictions and the observed experimental results indicates that the techniques developed in this paper can be used to highlight the proneness of new or existing designs to pilot-assisted oscillation

TRPA1 mediates aromatase inhibitor-evoked pain by the aromatase substrate androstenedione

Aromatase inhibitors (AI) induce painful musculoskeletal symptoms (AIMSS), which are dependent upon the pain transducing receptor TRPA1. However, as the AI concentrations required to engage TRPA1 in mice are higher than those found in the plasma of patients, we hypothesized that additional factors may cooperate to induce AIMSS. Here we report that the aromatase substrate androstenedione, unique among several steroid hormones, targeted TRPA1 in peptidergic primary sensory neurons in rodent and human cells expressing the native or recombinant channel. Androstenedione dramatically lowered the concentration of letrozole required to engage TRPA1. Notably, addition of a minimal dose of androstenedione to physiologically ineffective doses of letrozole and oxidative stress byproducts produces AIMSS-like behaviors and neurogenic inflammatory responses in mice. Elevated androstenedione levels cooperated with low letrozole concentrations and inflammatory mediators were sufficient to provoke AIMSS-like behaviors. The generation of such painful conditions by small quantities of simultaneously administered TRPA1 agonists justifies previous failure to identify a precise link between AIs and AIMSS, underscoring the potential of channel antagonists to treat AIMSS

Calculating coherent light-wave propagation in large heterogeneous media

Understanding the interaction of light with a highly scattering material is essential for optical microscopy of optically thick and heterogeneous biological tissues. Ensemble-averaged analytic solutions cannot provide more than general predictions for relatively simple cases. Yet, biological tissues contain chiral organic molecules and many of the cells' structures are birefringent, a property exploited by polarization microscopy for label-free imaging. Solving Maxwell's equations in such materials is a notoriously hard problem. Here we present an efficient method to determine the propagation of electro-magnetic waves in arbitrary anisotropic materials. We demonstrate how the algorithm enables large scale calculations of the scattered light field in complex birefringent materials, chiral media, and even materials with a negative refractive index

Practical and clinical utility of non-invasive vagus nerve stimulation (nVNS) for the acute treatment of migraine. A post hoc analysis of the randomized, sham-controlled, double-blind PRESTO trial

Background: The PRESTO study of non-invasive vagus nerve stimulation (nVNS; gammaCore®) featured key primary and secondary end points recommended by the International Headache Society to provide Class I evidence that for patients with an episodic migraine, nVNS significantly increases the probability of having mild pain or being pain-free 2 h post stimulation. Here, we examined additional data from PRESTO to provide further insights into the practical utility of nVNS by evaluating its ability to consistently deliver clinically meaningful improvements in pain intensity while reducing the need for rescue medication. Methods: Patients recorded pain intensity for treated migraine attacks on a 4-point scale. Data were examined to compare nVNS and sham with regard to the percentage of patients who benefited by at least 1 point in pain intensity. We also assessed the percentage of attacks that required rescue medication and pain-free rates stratified by pain intensity at treatment initiation. Results: A significantly higher percentage of patients who used acute nVNS treatment (n = 120) vs sham (n = 123) reported a ≥ 1-point decrease in pain intensity at 30 min (nVNS, 32.2%; sham, 18.5%; P = 0.020), 60 min (nVNS, 38.8%; sham, 24.0%; P = 0.017), and 120 min (nVNS, 46.8%; sham, 26.2%; P = 0.002) after the first attack. Similar significant results were seen when assessing the benefit in all attacks. The proportion of patients who did not require rescue medication was significantly higher with nVNS than with sham for the first attack (nVNS, 59.3%; sham, 41.9%; P = 0.013) and all attacks (nVNS, 52.3%; sham, 37.3%; P = 0.008). When initial pain intensity was mild, the percentage of patients with no pain after treatment was significantly higher with nVNS than with sham at 60 min (all attacks: nVNS, 37.0%; sham, 21.2%; P = 0.025) and 120 min (first attack: nVNS, 50.0%; sham, 25.0%; P = 0.018; all attacks: nVNS, 46.7%; sham, 30.1%; P = 0.037). Conclusions: This post hoc analysis demonstrated that acute nVNS treatment quickly and consistently reduced pain intensity while decreasing rescue medication use. These clinical benefits provide guidance in the optimal use of nVNS in everyday practice, which can potentially reduce use of acute pharmacologic medications and their associated adverse events. Trial registration: ClinicalTrials.gov identifier: NCT02686034