Related papers: Strategies for Development of a Distributed Framew…
Creation of the information systems and tools for scientific research and development support has always been one of the central directions of the development of computer science. The main features of the modern evolution of scientific…
In the field of computational science and engineering, workflows often entail the application of various software, for instance, for simulation or pre- and postprocessing. Typically, these components have to be combined in arbitrarily…
Distributed computing, such as cloud computing, provides promising platforms to execute multiple workflows. Workflow scheduling plays an important role in multi-workflow execution with multi-objective requirements. Although there exist many…
This paper introduces a systematic methodological framework to design and analyze distributed algorithms for optimization and games over networks. Starting from a centralized method, we identify an aggregation function involving all the…
Grid computing is the next logical step to distributed computing. Main objective of grid computing is an innovative approach to share resources such as CPU usage; memory sharing and software sharing. Data Grids provide transparent access to…
This paper discusses the deployment of existing render farm manager in a typical compute cluster environment such as a university. Usually, both a render farm and a compute cluster use different queue managers and assume total control over…
Grids aim at exploiting synergies that result from cooperation of autonomous distributed entities. The synergies that result from grid cooperation include the sharing, exchange, selection, and aggregation of geographically distributed…
Task graphs provide a simple way to describe scientific workflows (sets of tasks with dependencies) that can be executed on both HPC clusters and in the cloud. An important aspect of executing such graphs is the used scheduling algorithm.…
With the rapid development in wide area networks and low cost, powerful computational resources, grid computing has gained its popularity. With the advent of grid computing, space limitations of conventional distributed systems can be…
Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper…
There is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling approaches with state-of-the-art machine learning (ML)…
Large-scale distributed computing infrastructures such as the Worldwide LHC Computing Grid (WLCG) require comprehensive simulation tools for evaluating performance, testing new algorithms, and optimizing resource allocation strategies.…
The growing demand for large-scale quantum computers is pushing research on Distributed Quantum Computing (DQC). Recent experimental efforts have demonstrated some of the building blocks for such a design. DQC systems are clusters of…
In the AI-for-science era, scientific computing scenarios such as concurrent learning and high-throughput computing demand a new generation of infrastructure that supports scalable computing resources and automated workflow management on…
Deep Learning has made a great progress for these years. However, it is still difficult to master the implement of various models because different researchers may release their code based on different frameworks or interfaces. In this…
In this paper we describe an architecture which: Permits the deployment and execution of components in appropriate geographical locations. Provides security mechanisms that prevent misuse of the architecture. Supports a programming model…
Service orientation fosters a high-level model for distributed applications development, which is based on the discovery, composition and reuse of existing software services. However, the heterogeneity among current service-oriented…
Distributed software development is more difficult than co-located software development. One of the main reasons is that communication is more difficult in distributed settings. Defined processes and artifacts help, but cannot cover all…
Management of data in education sector particularly management of data for big universities with several employees, departments and students is a very challenging task. There are also problems such as lack of proper funds and manpower for…
The article highlights the promising ways of providing access to the cloud-based learning and research software in higher educational institutions. It is emphasized that the cloud computing services implementation is the actual trend of…