24 papers · ranked by Valyu relevance
Patrick McKeever, Varun Mittal, Bryce Fukuda, Ka Yee Yeung + 1 more
The exponential growth of omics data requires novel strategies for storage, transfer, and processing of said data. We present a scheduler based on the Temporal.io workflow framework which enables two key optimizations of bioinformatics workflows. Firstly, we enable users to transparently map workflow steps to diverse…
Zulfiqar Ahmad, Ali Imran Jehangiri, Mohammed Alaa Ala’anzy, Mohamed Othman + 2 more
'Mohamed Othman' 'Arif Iqbal Umar' 'Ching-Hsien Hsu'] Cloud computing is a fully fledged, matured and flexible computing paradigm that provides services to scientific and business applications in a subscription-based environment. Scientific applications such as Montage and CyberShake are organized scientific workflows…
R. Nallakumar, K S Sruthi Priya
Cloud Computing is the latest blooming technology in the era of Computer Science and Information Technology domain. There is an enormous pool of data centres, which are termed as Clouds where the services and associated data are being deployed and users need a constant Internet connection to access them. One of the…
Min Cui, Yipeng Wang
Workflow scheduling in cloud computing is attracting increasing attention. Cloud computing can assign tasks to available virtual machine resources in cloud data centers according to scheduling strategies, providing a powerful computing platform for the execution of workflow tasks. However, developing effective workflow…
Ali Vatankhah Barenji, Reza Vatankhah Barenji
- Mass customization explains the phenomenon to provide a unique designed products and services to all customer by achieving a high process integration and flexibility. It has been used as a competitive approach by many companies. Adequate resource implementation in mass customization-particularly in terms of resource…
Wenzheng Li, Xiaoping Li, Long Chen, Mingjing Wang + 2 more
'Dionisis Kandris' 'Gianni D’Angelo'] With the continuous evolution of microservice architecture and containerization technology, the challenge of efficiently and reliably scheduling large-scale cloud services has become increasingly prominent. In this paper, we present a cost-optimized scheduling approach with…
Kuo-Chan Huang, Wei-Ya Wu, Feng-Jian Wang, Hsiao-Ching Liu + 1 more
'Chun-Hao Hung'] Parallel computation has been widely applied in a variety of large-scale scientific and engineering applications. Many studies indicate that exploiting both task and data parallelisms, i.e. mixed-parallel workflows, to solve large computational problems can get better efficacy compared with either pure…
Abubeker Abdurahman, Abrar Hossain, Kevin A. Brown, Kazutomo Yoshii + 1 more
'Kishwar Ahmed'] Efficient job scheduling and resource management contributes towards system throughput and efficiency maximization in high-performance computing (HPC) systems. In this paper, we introduce a scalable job scheduling and resource management component within the structural simulation toolkit (SST), a…
Laurens Versluis, Alexandru Iosup
Workflows are prevalent in today's computing infrastructures. The workflow model support various different domains, from machine learning to finance and from astronomy to chemistry. Different Quality of Service (QoS) requirements and other desires of both users and providers makes workflow scheduling a tough problem…
Xuejun Li, Jia Xu, Yun Yang
Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow…
Ambika Aggarwal, Sunil Kumar, Ashutosh Bhatt, Mohd Asif Shah
Cloud computing is a procedure of stockpiling as well as retrieval of data or computer services over the Internet that allows all its users to remotely access the data centers. Cloud computing provides all required services to the users, but every platform has its share of pros and cons, and another major problem in…
Authors not listed
This paper presents GLAS (Git-based Lab Automated Scheduler or Get Lab Automation Simplified), an open-source, robust, and highly expandable Git-based architecture designed for laboratory automation. GLAS can be deployed in both partially and fully automated experimental science laboratories, enabling the development…
Bing Lin, Chaowei Lin, Xing Chen
—Workflow decision making is critical to performing many practical workflow applications. Scheduling in edgecloud environments can address the high complexity problem of workflow applications, while decreasing the data transmission delay between the cloud and end devices. However, because of the heterogeneous resources…
Daniel Ortiz-Martínez
Background Bioinformatics data analysis faces significant challenges. As data analysis often takes the form of pipelines or workflows, workflow managers (WfMs) have become essential. Data flow programming constitutes the preferred approach in WfMs, enabling parallel processes activated reactively based on input…
Samuel Lampa, Martin Dahlö, Jonathan Alvarsson, Ola Spjuth
The complex nature of biological data has driven the development of specialized software tools. Scientific workflow management systems simplify the assembly of such tools into pipelines and assist with job automation and aids reproducibility of analyses. Many contemporary workflow tools are specialized and not designed…
Andreas D. Schenk, Simone Cavadini, Nicolas H. Thomä, Christel Genoud
Efficient, reproducible and accountable single-particle cryo-electron microscopy structure determination is tedious and often impeded by lack of a standardized procedure for data analysis and processing. To address this issue, we have developed the FMI Live Analysis and Reconstruction Engine (CryoFLARE). CryoFLARE is a…
Frédéric Suter, Tainã Coleman, İlkay Altintaş, Rosa M. Badía + 22 more
Fred´ eric Suter ´ a,∗ , Taina Coleman ˜ b,∗ , ˙Ilkay Altintas¸ b , Rosa M. Badia c , Bartosz Balis d , Kyle Chard e , Iacopo Colonnelli f , Ewa Deelman g , Paolo Di Tommaso h , Thomas Fahringer i , Carole Goble j , Shantenu Jha k , Daniel S. Katz l , Johannes Koster ¨ m, Ulf Leser n , Kshitij Mehta a , Hilary Oliver o…
Authors not listed
Automated chemistry platforms hold the potential to enable large-scale organic synthesis campaigns, such as producing a library of compounds for biological evaluation. The efficiency of such platforms will depend on the schedule according to which the synthesis operations are executed. In this work, we study the…
Peter Kraus, Edan Bainglass, Francisco F. Ramirez, Enea Svaluto-Ferro + 7 more
Compliance with good research data management practices means trust in the integrity of the data, and it is achievable by a full control of the data gathering process. In this work, we demonstrate tooling which bridges these two aspects, and illustrate its use in a case study of automated battery cycling. We…
Authors not listed
Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…
Authors not listed
Background: Pharmaceutical batch production faces significant scheduling challenges due to operational uncertainties including equipment failures, yield variability, and demand fluctuations. While scheduling heuristics are widely used in practice, their comparative performance under varying uncertainty conditions…
Azza E. Ahmed, Jacob Heldenbrand, Yan Asmann, Faisal M. Fadlelmola + 10 more
Genomic variant discovery is frequently performed using the GATK Best Practices variant calling pipeline, a complex workflow with multiple steps, fans/merges, and conditionals. This complexity makes management of the workflow difficult on a computer cluster, especially when running in parallel on large batches of data…
Jamie Alnasir, Hugh P. Shanahan
The paper reviews the use of the Hadoop platform in Structural Bioinformatics applications. Specifically, we review a number of implementations using Hadoop of high-throughput analyses, e.g. ligand-protein docking and structural alignment, and their scalability in comparison with other batch schedulers and MPI. We find…
Taylor Reiter, Phillip T. Brooks, Luiz Irber, Shannon E.K. Joslin + 4 more
As the scale of biological data generation has increased, the bottleneck of research has shifted from data generation to analysis. Researchers commonly need to build computational workflows that include multiple analytic tools and require incremental development as experimental insights demand tool and parameter…