Data-intensive Science Research Articles

A Study on Traffic Prediction for the Backbone of Korea’s Research and Science Network Using Machine Learning

To fix network congestion resulting from the increase in high volume traffic in data-intensive science and the increase in internet traffic due to COVID-19, there has been a necessity of traffic engineering through traffic prediction. For this, there have been various attempts from a statistical method such as ARIMA to machine learning including LSTM and GRU. This study aimed to collect and learn KREOENT backbone and subscribers’ traffic volume through diverse machine learning techniques (e.g., SVR, LSTM, GRU, etc.) and predict maximum traffic on the following day.

African Centers of Excellence in Bioinformatics and Data Intensive Science: Building Capacity for Enhancing Data Intensive Infectious Diseases Research in Africa.

Africa faces both a disproportionate burden of infectious diseases coupled with unmet needs in bioinformatics and data science capabilities which impacts the ability of African biomedical researchers to vigorously pursue research and partner with institutions in other countries. The African Centers of Excellence in Bioinformatics and Data Intensive Science are collaborating with African academic institutions, industry partners, the Foundation for the National Institutes of Health (FNIH) and the National Institute of Allergy and Infectious Diseases (NIAID) at the National Institutes of Health (NIH) in a public-private partnership to address these challenges through enhancing computational infrastructure, fostering the development of advanced bioinformatics and data science skills among local researchers and students and providing innovative emerging technologies for infectious diseases research.

APOGEE-centric Ananke Simulations in a SciServer SQL Database

Modern galaxy simulations have reached the complexity required to create sophisticated mock catalogs. Ananke is a set of nine mock catalogs constructed from synthetic surveys of three Milky Way-like galaxies from the Latte suite of FIRE simulations. Ananke provides observed quantities for comparison with modern large-scale stellar surveys. In SDSS-IV DR17, mock catalogs for the Apache Point Galactic Evolution Experiment (APOGEE) were built from Ananke synthetic surveys as a Value-Added Catalog, but were only provided as large flat files (∼>10's GB). Here we announce an >40 Tb SQL database for nine APOGEE-specific mock catalogs and describe additions to the data model necessary for effective user queries. The catalogs can be accessed on the free, science platform, SciServer—supported by the Institute for Data Intensive Engineering and Science at the Johns Hopkins University (IDIES); SciServer supports server-side analysis with commonly used coding languages and tools.

A Crash Course in Good and Bad Controls

Many students of statistics and econometrics express frustration with the way a problem known as “bad control” is treated in the traditional literature. The issue arises when the addition of a variable to a regression equation produces an unintended discrepancy between the regression coefficient and the effect that the coefficient is intended to represent. Avoiding such discrepancies presents a challenge to all analysts in the data intensive sciences. This note describes graphical tools for understanding, visualizing, and resolving the problem through a series of illustrative examples. By making this “crash course” accessible to instructors and practitioners, we hope to avail these tools to a broader community of scientists concerned with the causal interpretation of regression models.

Data-Rich Spatial Profiling of Cancer Tissue: Astronomy Informs Pathology.

Astronomy was among the first disciplines to embrace Big Data and use it to characterize spatial relationships between stars and galaxies. Today, medicine, in particular pathology, has similar needs with regard to characterizing the spatial relationships between cells, with an emphasis on understanding the organization of the tumor microenvironment. In this article, we chronicle the emergence of data-intensive science through the development of the Sloan Digital Sky Survey and describe how analysis patterns and approaches similarly apply to multiplex immunofluorescence (mIF) pathology image exploration. The lessons learned from astronomy are detailed, and the new AstroPath platform that capitalizes on these learnings is described. AstroPath is being used to generate and display tumor-immune maps that can be used for mIF immuno-oncology biomarker development. The development of AstroPath as an open resource for visualizing and analyzing large-scale spatially resolved mIF datasets is underway, akin to how publicly available maps of the sky have been used by astronomers and citizen scientists alike. Associated technical, academic, and funding considerations, as well as extended future development for inclusion of spatial transcriptomics and application of artificial intelligence, are also addressed.

Big Data Health Care Platform With Multisource Heterogeneous Data Integration and Massive High-Dimensional Data Governance for Large Hospitals: Design, Development, and Application.

BackgroundWith the advent of data-intensive science, a full integration of big data science and health care will bring a cross-field revolution to the medical community in China. The concept big data represents not only a technology but also a resource and a method. Big data are regarded as an important strategic resource both at the national level and at the medical institutional level, thus great importance has been attached to the construction of a big data platform for health care.ObjectiveWe aimed to develop and implement a big data platform for a large hospital, to overcome difficulties in integrating, calculating, storing, and governing multisource heterogeneous data in a standardized way, as well as to ensure health care data security.MethodsThe project to build a big data platform at West China Hospital of Sichuan University was launched in 2017. The West China Hospital of Sichuan University big data platform has extracted, integrated, and governed data from different departments and sections of the hospital since January 2008. A master–slave mode was implemented to realize the real-time integration of multisource heterogeneous massive data, and an environment that separates heterogeneous characteristic data storage and calculation processes was built. A business-based metadata model was improved for data quality control, and a standardized health care data governance system and scientific closed-loop data security ecology were established.ResultsAfter 3 years of design, development, and testing, the West China Hospital of Sichuan University big data platform was formally brought online in November 2020. It has formed a massive multidimensional data resource database, with more than 12.49 million patients, 75.67 million visits, and 8475 data variables. Along with hospital operations data, newly generated data are entered into the platform in real time. Since its launch, the platform has supported more than 20 major projects and provided data service, storage, and computing power support to many scientific teams, facilitating a shift in the data support model—from conventional manual extraction to self-service retrieval (which has reached 8561 retrievals per month).ConclusionsThe platform can combine operation systems data from all departments and sections in a hospital to form a massive high-dimensional high-quality health care database that allows electronic medical records to be used effectively and taps into the value of data to fully support clinical services, scientific research, and operations management. The West China Hospital of Sichuan University big data platform can successfully generate multisource heterogeneous data storage and computing power. By effectively governing massive multidimensional data gathered from multiple sources, the West China Hospital of Sichuan University big data platform provides highly available data assets and thus has a high application value in the health care field. The West China Hospital of Sichuan University big data platform facilitates simpler and more efficient utilization of electronic medical record data for real-world research.

An Ameliorated Multiattack Network Anomaly Detection in Distributed Big Data System-Based Enhanced Stacking Multiple Binary Classifiers

The growth of the internet of things (IoT) generates new processing, networking infrastructure, data storage, and management capabilities. This massive data volume may be used to provide high-value information for decision support, forecasting, business intelligence, data-intensive science research, etc. However, due to the nature of IoT in distribution, virtualization, cloud integration, internet connectivity, the IoT environment is prone to various cyber-attacks and security issues, including an inability to access data chunks at the in/outcoming stream like worms, malware, virtual machine escape, distributed denial of service attack (DDoS), etc. Hence, the increasing frequency and potency of recent attacks and the constantly evolving attack vectors necessitate the development of improved detection approaches. Therefore, this paper proposes a distributed computing-based security model to safeguard big data systems. The proposed ensemble multi binary attack model (EMBAM) is an Intrusion Detection System (IDS) that offers a unique anomaly-based IDS to detect normal behaviour and abnormal attack(s), e.g., threats in a network. The EMBAM ensemble multiple binary classifiers into a single model by stacking. The core binary model is a decision tree classifier with hyperparameters optimized using the grid search method. Multiple binary classifiers’ usage allows each binary classifier to adopt the limitations of the others in determining the intercorrelation of the available features and the attack to achieve a high detection rate and a low false alarm rate. The improvement suggestions and outcomes insights by the EMBAM confirm its superiority in inaccuracy, detection rate, and efficiency. Two datasets have been used to validate the experiments, i.e., UNSW-NB15 and CICIDS2017. Empirical analysis of the experimental profile of the EMBAM has been discussed with eight-plus state-of-the-art methods using performance metrics like accuracy, precision, detection rate, false alarm rate, and F1-score. The EMBAM can recognize multiple attack types as a star plug and play advantage in a highly dynamic scheme. The proposed approach outperforms existing approaches over the UNSW-NB15 dataset and yields competitive results over the CICIDS2017 dataset.

Simulation and Evaluation of Cloud Storage Caching for Data Intensive Science

A common task in scientific computing is the data reduction. This workflow extracts the most important information from large input data and stores it in smaller derived data objects. The derived data objects can then be used for further analysis. Typically, these workflows use distributed storage and computing resources. A straightforward setup of storage media would be low-cost tape storage and higher-cost disk storage. The large, infrequently accessed input data are stored on tape storage. The smaller, frequently accessed derived data is stored on disk storage. In a best-case scenario, the large input data is only accessed very infrequently and in a well-planned pattern. However, practice shows that often the data has to be processed continuously and unpredictably. This can significantly reduce tape storage performance. A common approach to counter this is storing copies of the large input data on disk storage. This contribution evaluates an approach that uses cloud storage resources to serve as a flexible cache or buffer, depending on the computational workflow. The proposed model is explored for the case of continuously processed data. For the evaluation, a simulation tool was developed, which can be used to analyse models related to storage and network resources. We show that using commercial cloud storage can reduce on-premises disk storage requirements, while maintaining an equal throughput of jobs. Moreover, the key metrics of the model are discussed, and an approach is described, which uses the simulation to assist with the decision process of using commercial cloud storage. The goal is to investigate approaches and propose new evaluation methods to overcome future data challenges.

From biobank and data silos into a data commons: convergence to support translational medicine

BackgroundTo drive translational medicine, modern day biobanks need to integrate with other sources of data (clinical, genomics) to support novel data-intensive research. Currently, vast amounts of research and clinical data remain in silos, held and managed by individual researchers, operating under different standards and governance structures; a framework that impedes sharing and effective use of data. In this article, we describe the journey of British Columbia’s Gynecological Cancer Research Program (OVCARE) in moving a traditional tumour biobank, outcomes unit, and a collection of data silos, into an integrated data commons to support data standardization and resource sharing under collaborative governance, as a means of providing the gynecologic cancer research community in British Columbia access to tissue samples and associated clinical and molecular data from thousands of patients.ResultsThrough several engagements with stakeholders from various research institutions within our research community, we identified priorities and assessed infrastructure needs required to optimize and support data collections, storage and sharing, under three main research domains: (1) biospecimen collections, (2) molecular and genomics data, and (3) clinical data. We further built a governance model and a resource portal to implement protocols and standard operating procedures for seamless collections, management and governance of interoperable data, making genomic, and clinical data available to the broader research community.ConclusionsProper infrastructures for data collection, sharing and governance is a translational research imperative. We have consolidated our data holdings into a data commons, along with standardized operating procedures to meet research and ethics requirements of the gynecologic cancer community in British Columbia. The developed infrastructure brings together, diverse data, computing frameworks, as well as tools and applications for managing, analyzing, and sharing data. Our data commons bridges data access gaps and barriers to precision medicine and approaches for diagnostics, treatment and prevention of gynecological cancers, by providing access to large datasets required for data-intensive science.

An active human role is essential in big data-led decisions and data-intensive science.

Big data is transforming many sectors, with far-reaching consequences to how decisions are made and how knowledge is produced and shared. In the current move toward more data-led decisions and data-intensive science, we aim here to examine three issues that are changing the way data are read and used. First, there is a shift toward paradigms that involve a large amount of data. In such paradigms, the creation of complex data-led models becomes tractable and appealing to generate predictions and explanations. This necessitates for instance a rethinking of Occam's razor principle in the context of knowledge discovery. Second, there is a growing erosion of the human role in decision making and knowledge discovery processes. Human users' involvement is decreasing at an alarming rate, with no say on how to read, process, and summarize data. This makes legal responsibility and accountability hard to define. Third, thanks to its increasing popularity, big data is gaining a seductive allure, where volume and complexity of big data can de facto confer more persuasion and significance to knowledge or decisions that result from big-data-based processes. These issues call for an active human role by creating opportunities to incorporate, in the most unbiased way, human expertise and prior knowledge in decision making and knowledge production. This also requires putting in place robust monitoring and appraisal mechanisms to ensure that relevant data is answering the right questions. As the proliferation of data continues to grow, we need to rethink the way we interact with data to serve human needs.

Identification of Targeted Proteins by Jamu Formulas for Different Efficacies Using Machine Learning Approach.

Background: We performed in silico prediction of the interactions between compounds of Jamu herbs and human proteins by utilizing data-intensive science and machine learning methods. Verifying the proteins that are targeted by compounds of natural herbs will be helpful to select natural herb-based drug candidates. Methods: Initially, data related to compounds, target proteins, and interactions between them were collected from open access databases. Compounds are represented by molecular fingerprints, whereas amino acid sequences are represented by numerical protein descriptors. Then, prediction models that predict the interactions between compounds and target proteins were constructed using support vector machine and random forest. Results: A random forest model constructed based on MACCS fingerprint and amino acid composition obtained the highest accuracy. We used the best model to predict target proteins for 94 important Jamu compounds and assessed the results by supporting evidence from published literature and other sources. There are 27 compounds that can be validated by professional doctors, and those compounds belong to seven efficacy groups. Conclusion: By comparing the efficacy of predicted compounds and the relations of the targeted proteins with diseases, we found that some compounds might be considered as drug candidates.

X-インフォマティクス：第四パラダイムに基づく科学研究の変化とデータ中心科学の発展

X-インフォマティクス：第四パラダイムに基づく科学研究の変化とデータ中心科学の発展

Data-driven smart sustainable cities of the future: urban computing and intelligence for strategic, short-term, and joined-up planning

Sustainable cities are quintessential complex systems—dynamically changing environments and developed through a multitude of individual and collective decisions from the bottom up to the top down. As such, they are full of contestations, conflicts, and contingencies that are not easily captured, steered, and predicted respectively. In short, they are characterized by wicked problems. Therefore, they are increasingly embracing and leveraging what smart cities have to offer as to big data technologies and their novel applications in a bid to effectively tackle the complexities they inherently embody and to monitor, evaluate, and improve their performance with respect to sustainability—under what has been termed “data-driven smart sustainable cities.” This paper analyzes and discusses the enabling role and innovative potential of urban computing and intelligence in the strategic, short-term, and joined-up planning of data-driven smart sustainable cities of the future. Further, it devises an innovative framework for urban intelligence and planning functions as an advanced form of decision support. This study expands on prior work done to develop a novel model for data-driven smart sustainable cities of the future. I argue that the fast-flowing torrent of urban data, coupled with its analytical power, is of crucial importance to the effective planning and efficient design of this integrated model of urbanism. This is enabled by the kind of data-driven and model-driven decision support systems associated with urban computing and intelligence. The novelty of the proposed framework lies in its essential technological and scientific components and the way in which these are coordinated and integrated given their clear synergies to enable urban intelligence and planning functions. These utilize, integrate, and harness complexity science, urban complexity theories, sustainability science, urban sustainability theories, urban science, data science, and data-intensive science in order to fashion powerful new forms of simulation models and optimization methods. These in turn generate optimal designs and solutions that improve sustainability, efficiency, resilience, equity, and life quality. This study contributes to understanding and highlighting the value of big data in regard to the planning and design of sustainable cities of the future.

Fuzzy Rough Set Theory (FRST) Classifier for Pest Prediction in India

Total cotton production is affected by an important factor called cotton pests occurrence. During the growth of cotton, environmental factors having great dependence especially during climate change. In multi-disciplinary agri-technologies domain, for data intensive science, for creating new opportunities, machine learning with big data technologies is used as a high-performance computing technique and good results are produced using this. However, running time and accuracy are highly important task. Affected crop pests are only predicted using available machine learning techniques. But there wont be any accurate level of pest affection by cotton. In this paper, proposed a Fuzzy Rough Set Theory (FRST) for handling this pest prediction level. There are four major steps in this proposed work, namely, results evaluation, prediction, pre-processing and dataset collection. From 2010-2020, from All India Coordinated Research Project (AICRP) collected the dataset in the first stage. Irrelevant features are removed by performing pre-processing in the second stage and for prediction, needed features only considered. However, for prediction, weather parameters like Rainfall (RF), Relative Humidity-Evening(RH-E), Relative Humidity-Morning(RH-M), Minimum Temperature(Min-Temp) and Maximum Temperature(Max-Temp) are used. For pest prediction, only these weather parameters are used and from original dataset removed the remaining parameters. This produces a pre-processed dataset. For pest prediction, proposed a Fuzzy Rough Set Theory (FRST) classifier in third step. Cotton pest pre-processed dataset is used for training FRST. Activation corresponds to high approximation and lower approximation functions. Weather factors belonging to higher cotton pests are predicted in this result. For future pest, FRST network can be used as a better predictor according to pest records. Moreover, better performance is shown by proposed FRST network, when compared with traditional machine learning techniques like Random Forest (RF) and Multi-Layer Perceptron (MLP). At last, these pest prediction techniques effectiveness is measured using metrics like F-measure, Area Under the Curve (AUC), and Accuracy (ACC).

SOIL ANALYSIS AND CROP FERTILITY PREDICTION USING MACHINE LEARNING

Soil is a critical part of successful agriculture and is the source of the nutrients that we use to grow crops. There are different types of soil and there are different properties of each soil. On these different properties, several types of crops grow. We need to know the properties and characteristics of various soil types to understand which crops sow in certain soil types. Machine Learning allows the user to feed a computer algorithm on an immense amount of data and have the computer analyze, make data-driven recommendations and decisions based to analyze the input data. Machine Learning techniques are used to model this process. Machine Learning has come into the picture with the big data technologies and high-performance computing that create new opportunities for data-intensive science in the multi-disciplinary agri-technology domain. In this paper, we have proposed a model that can find whether the soil is fertile or not, Sowing crop seed on fertile soil, and at last predicting the crop yield on different soil features. According to prediction, it can be suggested and recommended which crops grow more. Various Machine Learning algorithms such as Support Vector Machine (SVM), Random Forest, Naive Bayes, Linear Regression, Multilayer perceptron (MLP), and ANN are used for soil classification and crop yield. Test results show that the proposed ANN method follows a deep learning architecture which means it has several layers for input and output are connected to achieve better accuracy than numerous existing methods.

SOIL ANALYSIS AND CROP FERTILITY PREDICTION USING MACHINE LEARNING

Soil is a critical part of successful agriculture and is the source of the nutrients that we use to grow crops. There are different types of soil and there are different properties of each soil. On these different properties, several types of crops grow. We need to know the properties and characteristics of various soil types to understand which crops sow in certain soil types. Machine Learning allows the user to feed a computer algorithm on an immense amount of data and have the computer analyze, make data-driven recommendations and decisions based to analyze the input data. Machine Learning techniques are used to model this process. Machine Learning has come into the picture with the big data technologies and high-performance computing that create new opportunities for data-intensive science in the multi-disciplinary agri-technology domain. In this paper, we have proposed a model that can find whether the soil is fertile or not, Sowing crop seed on fertile soil, and at last predicting the crop yield on different soil features. According to prediction, it can be suggested and recommended which crops grow more. Various Machine Learning algorithms such as Support Vector Machine (SVM), Random Forest, Naive Bayes, Linear Regression, Multilayer perceptron (MLP), and ANN are used for soil classification and crop yield. Test results show that the proposed ANN method follows a deep learning architecture which means it has several layers for input and output are connected to achieve better accuracy than numerous existing methods.

Comments to Jean-Claude Burgelman's article Politics and Open Science: How the European Open Science Cloud Became Reality (the Untold Story)

Is the evolution of how science is done primarily heuristic or driven by policy makers? Reading the intriguing story of the inception of the European Open Science Cloud (EOSC) by J-C Burgelman one could assume the latter. But the story also reveals that the policy makers' shaping of concepts and planning of funding models is a mere reaction to the turns of the broad stream of research. Looking from the science side the trend towards data intensive science started for sure already in the 90's with advancements in particularly sensor technologies rapidly increasing the amount of data collected. The development of Charge-coupled Devices (CCDs) and its impact on astronomy is the often-cited example but it was a trend building up all over experimental research taking benefit of developments within fast electronics and communication, or leaps in efficiency when shifting experimental technology, e.g. going from chromatography methods to fluoresces based methods for genome sequencing. On top of it the introduction of parallel supercomputers almost completely built out of commercial off-the-shelf components enabled the possibility to actually analyse and produce data in an unprecedented cost-efficient way.The resulting obvious need to combine and interoperate various networked resources was met by computer science and maybe the most successful work was concluded in the “The GRID, Blueprint for a New Computing Infrastructure”①. This result of work during the 90's by Foster and Kesselman became immensely popular even in industry. Though, on the other side of the IT-bubble industry took another route. Introduction of Service Oriented Architecture (SOA), Web-services, and cloud computing became the industry standard implementation of the ubiquitous computing promise in “The GRID”. Soon it also became part of the researchers' toolbox. Meaning that more complex workflows could be realised in connecting remote experiments, data resources, computing facilities, and whatever needed to reach the objectives of and advanced the research enterprise.The European Commission supported a large number of Grid-projects during the first decade of the millennium and the technology and concepts were particularly adopted by the high-energy physics community which still use them for analysing and simulating data related to the Large Hadron Collider (LHC) through a global network, or grid of sites.It deserves a more careful investigation but it is my feeling that particularly the life-science community was leading in taking another route and jumped on the train of SOA, Web-services, and cloud computing when it started to move with some speed somewhere after 2005. The inherit need of the specific research and the smart utilisation of the available technologies have resulted in a very well organised community when it comes to gathering of common open core data resources; sharing results that are findable, accessible, interoperable, and reusable; and very much of everything else that we are seeking with EOSC. In this aspect, a life-science, or definitely a bioinformatics EOSC implementation is already in place and also heavily used by the pharmaceutical industry which can access many of the core data resources at the same terms and conditions as publicly funded research.What is the point in looking backwards for some more than a decade old advances? Just to show that where we are now is not a new place. Yes, we experience another turn in how research is done and we are now looking for a societal response to that change. How can the society at large support, but also take optimal benefit of where research is right now? EOSC is, if we succeed, part of the answer, but we need to make sure that it can stay creative and innovative in enabling opportunities for research and society at large to thrive together.High-energy physics and life-sciences are mentioned as areas driving the development of concepts for enabling of research, but examples can be found elsewhere. More important is what research areas and research needs will show the direction for the next leap, maybe humanities or environmental sciences? I would put my bet there somewhere but the important message is that inspiration and innovation will, and must come from the research needs. Only then can growth minded policy makers, in their best moments, accelerate the development by introducing new frameworks and steering of funding, that will ultimately benefit research and the whole society.

BiotoolsSchema: a formalized schema for bioinformatics software description.

BackgroundLife scientists routinely face massive and heterogeneous data analysis tasks and must find and access the most suitable databases or software in a jungle of web-accessible resources. The diversity of information used to describe life-scientific digital resources presents an obstacle to their utilization. Although several standardization efforts are emerging, no information schema has been sufficiently detailed to enable uniform semantic and syntactic description—and cataloguing—of bioinformatics resources.FindingsHere we describe biotoolsSchema, a formalized information model that balances the needs of conciseness for rapid adoption against the provision of rich technical information and scientific context. biotoolsSchema results from a series of community-driven workshops and is deployed in the bio.tools registry, providing the scientific community with >17,000 machine-readable and human-understandable descriptions of software and other digital life-science resources. We compare our approach to related initiatives and provide alignments to foster interoperability and reusability.ConclusionsbiotoolsSchema supports the formalized, rigorous, and consistent specification of the syntax and semantics of bioinformatics resources, and enables cataloguing efforts such as bio.tools that help scientists to find, comprehend, and compare resources. The use of biotoolsSchema in bio.tools promotes the FAIRness of research software, a key element of open and reproducible developments for data-intensive sciences.

What machine learning can do for developmental biology.

Developmental biology has grown into a data intensive science with the development of high-throughput imaging and multi-omics approaches. Machine learning is a versatile set of techniques that can help make sense of these large datasets with minimal human intervention, through tasks such as image segmentation, super-resolution microscopy and cell clustering. In this Spotlight, I introduce the key concepts, advantages and limitations of machine learning, and discuss how these methods are being applied to problems in developmental biology. Specifically, I focus on how machine learning is improving microscopy and single-cell 'omics' techniques and data analysis. Finally, I provide an outlook for the futures of these fields and suggest ways to foster new interdisciplinary developments.

Exploitation of HPC Resources for data intensive sciences

The Large Hadron Collider (LHC) will enter a new phase beginning in 2027 with the upgrade to the High Luminosity LHC (HL-LHC). The increase in the number of simultaneous collisions coupled with a more complex structure of a single event will result in each LHC experiment collecting, storing, and processing exabytes of data per year. The amount of generated and/or collected data greatly outweighs the expected available computing resources. In this paper, we discuss effcient usage of HPC resources as a prerequisite for data-intensive science at exascale. First, we discuss the experience of porting CMS Hadron and Electromagnetic calorimeters reconstruction code to utilize Nvidia GPUs within the DEEP-EST project; second, we look at the tools and their adoption in order to perform benchmarking of a variety of resources available at HPC centers. Finally, we touch on one of the most important aspects of the future of HEP - how to handle the flow of petabytes of data to and from computing facilities, be it clouds or HPCs, for exascale data processing in a flexible, scalable and performant manner. These investigations are a key contribution to technical work within the HPC collaboration among CERN, SKA, GEANT and PRACE.