This research was funded by the Spanish Research Agency, grant numbers PID2021-122392OB-I00, PID2019-106623RB-C41/AEI/10.13039/501100011033 and PID2019-107768RA-I00; and by Universidad Rey Juan Carlos (URJC) and Community of Madrid, Spain , grant number 2020-66.

Abstract Radial axes plots are projection methods that represent high‐dimensional data samples as points on a two‐dimensional plane. These techniques define mappings through a set of axis vectors, each associated with a data variable, which users can manipulate interactively to create different plots and analyze data from multiple points of view. However, updating the direction and length of an axis vector is far from trivial. Users must consider the data analysis task, domain knowledge, the directions in which values should increase, the relative importance of each variable, or the correlations between variables, among other factors. Another issue is the difficulty to approximate high‐dimensional data values in the two‐dimensional visualizations, which can hamper searching for data with particular characteristics, analyzing the most common data values in clusters, inspecting outliers, etc. In this paper we present and analyze several optimization approaches for enhancing radial axes plots regarding their ability to represent high‐dimensional data values. The techniques can be used not only to approximate data values with greater accuracy, but also to guide users when updating axis vectors or extending visualizations with new variables, since they can reveal poor choices of axis vectors. The optimal axes can also be included in nonlinear plots. In particular, we show how they can be used within RadViz to assess the quality of a variable ordering. The in‐depth analysis carried out is useful for visualization designers developing radial axes techniques, or planning to incorporate axes into other visualization methods.

GPUs have enabled a drastic change to computing environments, making massively parallel computing possible. Molecular dynamics is a perfect candidate problem for massively parallel computing, but to date it has not taken full advantage of multi-GPU environments due to the difficulty of partitioning molecular dynamics problems and exchanging problem data among compute nodes. These difficulties restrict the use of GPUs to only some of the computations in a full molecular dynamics problem, and hence prevent scalability beyond just a few GPUs. This work presents a scalable parallelization solution for the bonded and short-range forces present in a molecular dynamics problem. Together with existing solutions for long-range forces, it enables highly scalable, parallel molecular dynamics on multi-GPU computing environments. Specifically, the proposed solution divides the molecular volume into independent parts assigned to different GPUs, but it maintains a global bond structure that is efficiently exchanged when atoms move across GPUs. We demonstrate close-to-linear speedup of the proposed solution, simulating the dynamics of gigamolecules with 1 billion atoms on a computing environment with 96 GPUs, and obtaining superior performance to the well known molecular dynamics simulator NAMD.

BACKGROUND: Chronic diseases are becoming more widespread each year in developed countries, mainly due to increasing life expectancy. Among them, diabetes mellitus (DM) and essential hypertension (EH) are two of the most prevalent ones. Furthermore, they can be the onset of other chronic conditions such as kidney or obstructive pulmonary diseases. The need to comprehend the factors related to such complex diseases motivates the development of interpretative and visual analysis methods, such as classification trees, which not only provide predictive models for diagnosing patients, but can also help to discover new clinical insights. RESULTS: In this paper, we analyzed healthy and chronic (diabetic, hypertensive) patients associated with the University Hospital of Fuenlabrada in Spain. Each patient was classified into a single health status according to clinical risk groups (CRGs). The CRGs characterize a patient through features such as age, gender, diagnosis codes, and drug codes. Based on these features and the CRGs, we have designed classification trees to determine the most discriminative decision features among different health statuses. In particular, we propose to make use of statistical data visualizations to guide the selection of features in each node when constructing a tree. We created several classification trees to distinguish among patients with different health statuses. We analyzed their performance in terms of classification accuracy, and drew clinical conclusions regarding the decision features considered in each tree. As expected, healthy patients and patients with a single chronic condition were better classified than patients with comorbidities. The constructed classification trees also show that the use of antipsychotics and the diagnosis of chronic airway obstruction are relevant for classifying patients with more than one chronic condition, in conjunction with the usual DM and/or EH diagnoses. CONCLUSIONS: We propose a methodology for constructing classification trees in a visually guided manner. The approach allows clinicians to progressively select the decision features at each of the tree nodes. The process is guided by exploratory data analysis visualizations, which may provide new insights and unexpected clinical information.

Monitoring has always been a key element on ensuring the performance of complex distributed systems, being a first step to control quality of service, detect anomalies, or make decisions about resource allocation and job scheduling, to name a few. Edge computing is a new type of distributed computing, where data processing is performed by a large number of heterogeneous devices close to the place where the data is generated. Some of the differences between this approach and more traditional architectures, like cloud or high performance computing, are that these devices have low computing power, have unstable connectivity, and are geo‐distributed or even mobile. All of these aforementioned characteristics establish new requirements for monitoring tools, such as customized monitoring workflows or choosing different back‐ends for the metrics, depending on the device hosting them. In this paper, we present a study of the requirements that an edge monitoring tool should meet, based on motivating scenarios drawn from literature. Additionally, we implement these requirements in a monitoring tool named FMonE. This framework allows deploying monitoring workflows that conform to the specific demands of edge computing systems. We evaluate FMonE by simulating a fog environment in the Grid’5000 testbed and we demonstrate that it fulfills the requirements we previously enumerated.

Abstract Radial axes plots are multivariate visualization techniques that extend scatterplots in order to represent high‐dimensional data as points on an observable display. Well‐known methods include star coordinates or principal component biplots, which represent data attributes as vectors that define axes, and produce linear dimensionality reduction mappings. In this paper we propose a hybrid approach that bridges the gap between star coordinates and principal component biplots, which we denominate “adaptable radial axes plots”. It is based on solving convex optimization problems where users can: (a) update the axis vectors interactively, as in star coordinates, while producing mappings that enable to estimate attribute values optimally through labeled axes, similarly to principal component biplots; (b) use different norms in order to explore additional nonlinear mappings of the data; and (c) include weights and constraints in the optimization problems for sorting the data along one axis. The result is a flexible technique that complements, extends, and enhances current radial methods for data analysis.

RadViz and star coordinates are two of the most popular projection-based multivariate visualization techniques that arrange variables in radial layouts. Formally, the main difference between them consists of a nonlinear normalization step inherent in RadViz. In this paper we show that, although RadViz can be useful when analyzing sparse data, in general this design choice limits its applicability and introduces several drawbacks for exploratory data analysis. In particular, we observe that the normalization step introduces nonlinear distortions, can encumber outlier detection, prevents associating the plots with useful linear mappings, and impedes estimating original data attributes accurately. In addition, users have greater flexibility when choosing different layouts and views of the data in star coordinates. Therefore, we suggest that analysts and researchers should carefully consider whether RadViz's normalization step is beneficial regarding the data sets' characteristics and analysis tasks.

Star coordinates is a well-known multivariate visualization method that produces linear dimensionality reduction mappings through a set of radial axes defined by vectors in an observable space. One of its main drawbacks concerns the difficulty to recover attributes of data samples accurately, which typically lie in the [0], [1] interval, given the locations of the low-dimensional embeddings and the vectors. In this paper we show that centering the data can considerably increase attribute estimation accuracy, where data values can be read off approximately by projecting embedded points onto calibrated (i.e., labeled) axes, similarly to classical statistical biplots. In addition, this idea can be coupled with a recently developed orthonormalization process on the axis vectors that prevents unnecessary distortions. We demonstrate that the combination of both approaches not only enhances the estimates, but also provides more faithful representations of the data.

Complexity has always been one of the most important issues in distributed computing. From the first clusters to grid and now cloud computing, dealing correctly and efficiently with system complexity is the key to taking technology a step further. In this sense, global behavior modeling is an innovative methodology aimed at understanding the grid behavior. The main objective of this methodology is to synthesize the grid's vast, heterogeneous nature into a simple but powerful behavior model, represented in the form of a single, abstract entity, with a global state. Global behavior modeling has proved to be very useful in effectively managing grid complexity but, in many cases, deeper knowledge is needed. It generates a descriptive model that could be greatly improved if extended not only to explain behavior, but also to predict it. In this paper we present a prediction methodology whose objective is to define the techniques needed to create global behavior prediction models for grid systems. This global behavior prediction can benefit grid management, specially in areas such as fault tolerance or job scheduling. The paper presents experimental results obtained in real scenarios in order to validate this approach.

The cloud computing model aims to make large-scale data-intensive computing affordable even for users with limited financial resources, that cannot invest into expensive infrastructures necesssary to run them. In this context, MapReduce is emerging as a highly scalable programming paradigm that enables high-throughput data-intensive processing as a cloud service. Its performance is highly dependent on the underlying storage service, responsible to efficiently support massively parallel data accesses by guaranteeing a high throughput under heavy access concurrency. In this context, quality of service plays a crucial role: the storage service needs to sustain a stable throughput for each individual accesss, in addition to achieving a high aggregated throughput under concurrency. In this paper we propose a technique to address this problem using component monitoring, application-side feedback and behavior pattern analysis to automatically infer useful knowledge about the causes of poor quality of service and provide an easy way to reason in about potential improvements. We apply our proposal to Blob Seer, a representative data storage service specifically designed to achieve high aggregated throughputs and show through extensive experimentation substantial improvements in the stability of individual data read accesses under MapReduce workloads.

Grid systems have proved to be one of the most important new alternatives to face challenging problems but, to exploit its benefits, dependability and fault tolerance are key aspects. However, the vast complexity of these systems limits the efficiency of traditional fault tolerance techniques. It seems necessary to distinguish between resource-level fault tolerance (focused on every machine) and service-level fault tolerance (focused on global behavior). Techniques based on these concepts can handle system complexity and increase dependability. We present an autonomous, self-adaptive fault tolerance framework for grid systems, based on a new approach to model distributed environments. The grid is considered as a single entity, instead of a set of independent resources. This point of view focuses on service-level fault tolerance, allowing us to see the big picture and understand the system's global behavior. The resulting model's simplicity is the key to provide system-wide fault tolerance.

Abstract Over the last decade, grid computing has paved the way for a new level of large‐scale‐distributed systems. However, this new step in distributed computing comes along with a completely new level of complexity. Grid management mechanisms play a key role, and a correct analysis and understanding of the grid behavior is needed. Traditional‐distributed computing management mechanisms analyze each resource separately and adjust specific parameters of each one of them. When trying to adapt the same procedures to grid computing, the vast complexity of the system can complicate this task. But grid complexity could only be a matter of perspective. It is possible to understand the grid behavior as a single system, instead of a set of resources. This abstraction could provide a deeper understanding of the system, describing large‐scale behavior and global events that probably would not be detected while analyzing each resource separately. In this paper a specific methodology is presented and described in order to create a global behavior model of the grid, analyzing it as a single entity. Both real and simulated case studies are also presented, in order to provide a proper validation and illustrate the benefits of this approach. Copyright © 2009 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.

Abstract Nowadays TV channels generate a large amount of video data each day. A very huge number of videos of news, shows, series, movies, and so on have to be stored with the aim of being accessed later on. Moreover, channels have a clear need of sharing videos so as to settle a real collaboration among them that minimizes the cost of information acquisition. These features demand a huge storage capacity and a sharing information environment. Both requirements can be solved by using grid computing. It provides both computing and storage capacities to store that great volume of data required, as well as the resource sharing capabilities for the cooperation of different TV channels. This paper presents a video retrieval system that covers these needs and suggests a work allocation (WA) broker to improve the performance of video accesses. An evaluation shows the feasibility and the scalability of this approach showing the benefits of the WA made to store and retrieve large video data. Copyright © 2009 John Wiley & Sons, Ltd.

MAPFS is a parallel file system integrated with a multiagent system responsible for the information retrieval. One of the fields where the agents can be very useful is precisely in the development of information recovery systems. The usage of a multiagent system implies coordination among the agents that belong to such system. The main goal of the agent cooperation is the interaction among them for achieving a common objective in a distributed system. Thus, a communication framework must be provided. This paper shows the MAPFS cooperation model and its communication framework, emphasizing its relation with the whole system.