Paper presents and evaluates various mechanisms for remote access to memory in distributed systems based on two distinct HPC clusters. We are comparing solutions based on the shared storage and MPI (over Infiniband and Slingshot) to the local memory access. This paper also mentions medical use-cases that would mostly benefit from the described solution. We have found out that results for remote access esp. backed by MPI are similar to local memory access.

The baroreflex is one of the most important control mechanisms in the human cardiovascular system. This work utilises a closed-loop in silico model of baroreflex regulation, coupled to pulsatile mechanical models with (i) one heart chamber and 36-parameters and (ii) four chambers and 51 parameters. We perform the first global sensitivity analysis of these closed-loop systems which considers both cardiovascular and baroreflex parameters, and compare the models with their respective unregulated equivalents. Results show the reduced influence of regulated parameters compared to unregulated equivalents and that, in the physiological resting state, model outputs (pressures, heart rate, cardiac output etc.) are most sensitive to parasympathetic arc parameters. This work provides insight into the effects of regulation and model input parameter influence on clinical metrics, and constitutes a first step to understanding the role of regulation in models for personalised healthcare.

Digital twins are virtual representations of physical objects or systems used for the purpose of analysis, most often via computer simulations, in many engineering and scientific disciplines. Recently, this approach has been introduced to computational medicine, within the concept of Digital Twin in Healthcare (DTH). Such research requires verification and validation of its models, as well as the corresponding sensitivity analysis and uncertainty quantification (VVUQ). From the computing perspective, VVUQ is a computationally intensive process, as it requires numerous runs with variations of input parameters. Researchers often use high-performance computing (HPC) solutions to run VVUQ studies where the number of parameter combinations can easily reach tens of thousands. However, there is a viable alternative to HPC for a substantial subset of computational models - serverless computing. In this paper we hypothesize that using the serverless computing model can be a practical and efficient approach to selected cases of running VVUQ calculations. We show this on the example of the EasyVVUQ library, which we extend by providing support for many serverless services. The resulting library - CloudVVUQ - is evaluated using two real-world applications from the computational medicine domain adapted for serverless execution. Our experiments demonstrate the scalability of the proposed approach.

BACKGROUND: MicroRNAs (miRNAs) are short, non-coding RNA molecules that regulate gene expression by binding to specific mRNAs, inhibiting their translation. They play a critical role in regulating various biological processes and are implicated in many diseases, including cardiovascular, oncological, gastrointestinal diseases, and viral infections. Computational methods that can identify potential miRNA-mRNA interactions from raw data use one-dimensional miRNA-mRNA duplex representations and simple sequence encoding techniques, which may limit their performance. RESULTS: We have developed GraphTar, a new target prediction method that uses a novel graph-based representation to reflect the spatial structure of the miRNA-mRNA duplex. Unlike existing approaches, we use the word2vec method to accurately encode RNA sequence information. In conjunction with the novel encoding method, we use a graph neural network classifier that can accurately predict miRNA-mRNA interactions based on graph representation learning. As part of a comparative study, we evaluate three different node embedding approaches within the GraphTar framework and compare them with other state-of-the-art target prediction methods. The results show that the proposed method achieves similar performance to the best methods in the field and outperforms them on one of the datasets. CONCLUSIONS: In this study, a novel miRNA target prediction approach called GraphTar is introduced. Results show that GraphTar is as effective as existing methods and even outperforms them in some cases, opening new avenues for further research. However, the expansion of available datasets is critical for advancing the field towards real-world applications.

The Large Hadron Collider (LHC) at CERN has generated in the last decade an unprecedented volume of data for the High-Energy Physics (HEP) field. Scientific collaborations interested in analysing such data very often require computing power beyond a single machine. This issue has been tackled traditionally by running analyses in distributed environments using stateful, managed batch computing systems. While this approach has been effective so far, current estimates for future computing needs of the field present large scaling challenges. Such a managed approach may not be the only viable way to tackle them and an interesting alternative could be provided by serverless architectures, to enable an even larger scaling potential. This work describes a novel approach to running real HEP scientific applications through a distributed serverless computing engine. The engine is built upon ROOT, a well-established HEP data analysis software, and distributes its computations to a large pool of concurrent executions on Amazon Web Services Lambda Serverless Platform. Thanks to the developed tool, physicists are able to access datasets stored at CERN (also those that are under restricted access policies) and process it on remote infrastructures outside of their typical environment. The analysis of the serverless functions is monitored at runtime to gather performance metrics, both for data- and computation-intensive workloads.

Modern HPC workload managers and their careful tuning contribute to the high utilization of HPC clusters. However, due to inevitable uncertainty it is impossible to completely avoid node idleness. Although such idle slots are usually too short for any HPC job, they are too long to ignore them. Function-as-a-Service (FaaS) paradigm promisingly fills this gap, and can be a good match, as typical FaaS functions last seconds, not hours. Here we show how to build a FaaS infrastructure on idle nodes in an HPC cluster in such a way that it does not affect the performance of the HPC jobs significantly. We dynamically adapt to a changing set of idle physical machines, by integrating open-source software Slurm and OpenWhisk. We designed and implemented a prototype solution that allowed us to cover up to 90% of the idle time slots on a 50k-core cluster that runs production workloads.

Serverless computing is a novel cloud computing paradigm where the cloud provider manages the underlying infrastructure, while users are only required to upload the code of the application. Function as a Service (FaaS) is a serverless computing model where short-lived methods are executed in the cloud. One of the promising use cases for FaaS is running scientific workflow applications, which represent a scientific process composed of related tasks. Due to the distinctive features of FaaS, which include rapid resource provisioning, indirect infrastructure management, and fine-grained billing model a need arises to create dedicated scheduling methods to effectively use the novel infrastructures as an environment for workflow applications. In this paper we propose two novel scheduling algorithms SMOHEFT and SML, which are designed to create a schedule for executing scientific workflows on serverless infrastructures concerning time and cost constraints. We evaluated proposed algorithms by performing experiments, where we planned the execution of three applications: Ellipsoids, Vina and Montage. SDBWS and SDBCS algorithms were used as a baseline. SML achieved the best results when executing Ellipsoids workflow, with a success rate above 80%, while other algorithms were below 60%. In the case of Vina, all the algorithms, except SDBWS, had a success rate above 87.5% and in the case of Montage, the success rate of all algorithms was similar, over 87.5%. The proposed algorithms' success rate is comparable or better than offered by other studied solutions.

The increasing popularity of the serverless computing approach has led to the emergence of new cloud infrastructures working in Container-as-a-Service (CaaS) model like AWS Fargate, Google Cloud Run, or Azure Container Instances. New infrastructures facilitate an innovative approach to running cloud containers where developers are freed from managing underlying resources. In this paper, we focus on evaluating the capabilities of elastic containers and their usefulness for scientific computing in the scientific workflow paradigm using AWS Fargate and Google Cloud Run infrastructures. For the experimental evaluation of our approach, we extended the HyperFlow engine to support these CaaS platforms, together with adapting four scientific workflows composed of several dozen to hundreds of tasks organized into a dependency graph. Studied applications are used to create cost-performance benchmarks and flow execution plots, delay, elasticity, and scalability measurements. Results show that serverless containers can be successfully utilized for running scientific workflows. Moreover, the results allow for gaining insight into the specific advantages and limits of the studied platforms.

Serverless computing, also known as Function-as-a-Service (FaaS) or Cloud Functions, is a new method of running distributed applications by executing functions on the infrastructure of cloud providers. Although it frees the developers from managing servers, there are still decisions to be made regarding selection of function configurations based on the desired performance and cost. The billing model of this approach considers time of execution, measured in 100ms units, as well as the size of the memory allocated per function. In this paper, we look into the problem of scheduling scientific workflows, which are applications consisting of multiple tasks connected into a dependency graph. We discuss challenges related to workflow scheduling and propose the Serverless Deadline-Budget Workflow Scheduling (SDBWS) algorithm adapted to serverless platforms. We present preliminary experiments with a small-scale Montage workflow run on the AWS Lambda infrastructure.

Summary In large‐scale and distributed actor systems, there are situations where processing messages within one of the actors fails, often due to failures that had occurred earlier in the system. In such cases, tracing down the origin of the failure is difficult since existing monitoring tools only provide ways to collect metrics and statistical information about system execution. In this paper, we describe a new tool fortracingdistributed actor systems,Akka Tracing Tool, a library that allows users to generate a trace graph of messages. To address the distributed nature of the environment, we proposed an efficient data collection mechanism based on the one‐way replication technique implemented in CouchDB, a popular document database. The tool was evaluated in a distributed environment of up to 50 nodes set up in the Amazon Web Services (AWS) computing cloud on a real application: car traffic simulation. The measured overhead when tracing all messages was between 39% to 45% on average. The library also proved to be scalable with respect to the number of nodes in the actor system and to be user‐friendly. Owing to these properties, we expect that the tool can simplify finding errors and speed up the development process of actor systems.

Summary Cloud Functions, often called Function‐as‐a‐Service (FaaS), pioneered by AWS Lambda, are an increasingly popular method of running distributed applications. As in other cloud offerings, cloud functions are heterogeneous due to variations in underlying hardware, runtime systems, as well as resource management and billing models. In this paper, we focus on performance evaluation of cloud functions, taking into account heterogeneity aspects. We developed a cloud function benchmarking framework, consisting of one suite based on Serverless Framework and one based on HyperFlow. We deployed the CPU‐intensive benchmarks: Mersenne Twister and Linpack. We measured the data transfer times between cloud functions and storage, and we measured the lifetime of the runtime environment. We evaluated all the major cloud function providers: AWS Lambda, Azure Functions, Google Cloud Functions, and IBM Cloud Functions. We made our results available online and continuously updated. We report on the results of the performance evaluation, and we discuss the discovered insights into resource allocation policies.

We address the topology-aware job scheduling and placement problems on 3D torus-based high performance computing systems, with the objective of reducing system fragmentation. In our previous work, we proposed a job placement algorithm based on a local migration process, which aims at reducing the internal fragmentation due to using a convex prism shape for job allocation. However, HPC systems are prone to suffer from external fragmentation as well. Hence, in this paper, we majorly strive to reduce the external fragmentation brought in by the job scheduling and placement processes. Firstly, from the job scheduling aspect of view, we propose a packing-based job scheduling strategy, which reduces the external fragmentation by using the First Come First Served + backfilling strategy. Secondly, we give a review of the migration-based job placement algorithm in our previous work. Thirdly, in order to reduce the external fragmentation resulting from running jobs scattered across the system, we propose a job placement reconfiguration algorithm, which uses a global migration process to rearrange the placement of the running jobs across the system. Both local and global migration are emulated virtual processes under the off-line scenario, which have no migration overhead. However, under the on-line scenario, migration is a real process and leads to a migration delay. Therefore, we propose a buffer-based on-line scheduling model, which helps to avoid the delay of local migration. The evaluation results validate the efficiency of our approach in reducing system fragmentation and improving system utilization.

This paper focuses on data-intensive workflows and addresses the problem of scheduling workflow ensembles under cost and deadline constraints in Infrastructure as a Service (IaaS) clouds. Previous research in this area ignores file transfers between workflow tasks, which, as we show, often have a large impact on workflow ensemble execution. In this paper we propose and implement a simulation model for handling file transfers between tasks, featuring the ability to dynamically calculate bandwidth and supporting a configurable number of replicas, thus allowing us to simulate various levels of congestion. The resulting model is capable of representing a wide range of storage systems available on clouds: from in-memory caches (such as memcached), to distributed file systems (such as NFS servers) and cloud storage (such as Amazon S3 or Google Cloud Storage). We observe that file transfers may have a significant impact on ensemble execution; for some applications up to 90 % of the execution time is spent on file transfers. Next, we propose and evaluate a novel scheduling algorithm that minimizes the number of transfers by taking advantage of data caching and file locality. We find that for data-intensive applications it performs better than other scheduling algorithms. Additionally, we modify the original scheduling algorithms to effectively operate in environments where file transfers take non-zero time.

Infrastructure as a Service (IaaS) clouds are considered interesting sources of computing and storage resources for scientific applications. However, given the large number of cloud vendors and their diverse offerings, it is not trivial for research projects to select an appropriate service provider. In this paper, we present the results of evaluation of public cloud providers, taking into account the requirements of the biomedical applications within the VPH-Share project. We performed a broad analysis of nearly 50 cloud providers and analyzed the performance and cost of 26 virtual machine instance types offered by the top three providers who meet our criteria: Amazon EC2, Rack Space and Soft Layer. We hope our results will be helpful for other research projects that are considering clouds as a potential source of computing and storage resources.

Cloud computing with its on-demand access to resources has emerged as a tool used by researchers from a wide range of domains to run computer-based experiments. In this paper we introduce a flexible experiment management API, written in Python that simplifies and formalizes the execution of scientific experiments on cloud infrastructures. We describe the features and functionality of PRECIP (Pegasus Repeatable Experiments for the Cloud in Python), and how PRECIP can be used to set up experiments on academic clouds such as OpenStack Eucalyptus, Nimbus, and commercial clouds such as Amazon EC2.

Large-scale applications expressed as scientific workflows are often grouped into ensembles of inter-related workflows. In this paper, we address a new and important problem concerning the efficient management of such ensembles under budget and deadline constraints on Infrastructure- as-aService (IaaS) clouds. We discuss, develop, and assess algorithms based on static and dynamic strategies for both task scheduling and resource provisioning. We perform the evaluation via simulation using a set of scientific workflow ensembles with a broad range of budget and deadline parameters, taking into account uncertainties in task runtime estimations, provisioning delays, and failures. We find that the key factor determining the performance of an algorithm is its ability to decide which workflows in an ensemble to admit or reject for execution. Our results show that an admission procedure based on workflow structure and estimates of task runtimes can significantly improve the quality of solutions.

The subject of this paper is a detailed analysis and development of security in MOCCA, a CCA-compliant Grid component framework build over H2O, a Java-based distributed computing platform. The approach is to extend H2O with an authentication mechanism that will be both secure and compliant with solutions commonly used in modern Grid systems. The proposed authenticator is based on asymmetric cryptography with additional features provided by the Grid Security Infrastructure - proxy certificates that are used for Single Sign-On and delegation. The developed GSI Authenticator was subjected to threat analysis and performance tests, which proved its safety and usability.

The ViroLab Virtual Laboratory is a collaborative platform for scientists representing multiple fields of expertise while working together on common scientific goals. This environment makes it possible to combine efforts of computer scientists, virology and epidemiology experts and experienced physicians to support future advances in HIV-related research and treatment. The paper explains the challenges involved in building a modern, inter-organizational platform to support science and gives an overview of solutions to these challenges. Examples of real-world problems applied in the presented environment are also described to prove the feasibility of the solution.

The idea of an application described through its workflow is becoming popular in the Grid community as a natural method of functional decomposition of an application. It shows all the important dependencies as a set of connections of data flow and/or control flow. As scientific workflows grow in size and complexity, a tool to assist end users is becoming necessary. In this paper we describe the formal basis, design and implementation of such a tool -- an assistant which analyzes user requirements regarding application results and works with information registries that provide information on resources available in the Grid. The Workflow Composition Tool (WCT) provides the functionality of automatic workflow construction based on the process of semantic service discovery and matchmaking. It uses a well-designed construction algorithm together with specific heuristics in order to provide useful solutions for application users.

Collaborative environments where users can interact with running simulations are an important aspect of e- Science. To achieve efficient execution of High Level Architecture (HLA)-based collaborative environments on the Grid, we introduce a Grid HLA Management System (GHLAM) for their management. This is done by introducing migration and monitoring mechanisms for such applications. In this paper we present how G-HLAM can be applied to the collaborative environment supporting surgeons with simulations of vascular reconstruction, using distributed federations on the Grid for the communication among simulation and visualization components.

This paper presents the design of a system that supports execution of HLA (high level architecture) distributed interactive simulations in an unreliable grid environment. The design of the architecture is based on the OGSA (Open Grid Services Architecture) concept that allows for modularity and compatibility with grid services already being developed. First of all, we focus on the part of the system which is responsible for migration of a HLA-connected component or components of the distributed application in the grid environment. We present a runtime support library for easily plugging HLA simulations into the grid services framework. We also present the impact of execution management (namely migration) on overall system performance.