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In scientific computing, more computational power generally implies faster and possibly more detailed results. The goal of this study was to develop a framework to submit computational jobs to powerful workstations underused by nonintensive tasks. This is achieved by using a virtual machine in each of these workstations, where the computations are done. This group of virtual machines is called the Gridlan. The Gridlan framework is intermediate between the cluster and grid computing paradigms. The Gridlan is able to profit from existing cluster software tools, such as resource managers like Torque, so a user with previous experience in cluster operation can dispatch jobs seamlessly. A benchmark test of the Gridlan implementation shows the systems suitability for computational tasks, principally in embarrassingly parallel computations.
High Energy Physics (HEP) and other scientific communities have adopted Service Oriented Architectures (SOA) as part of a larger Grid computing effort. This effort involves the integration of many legacy applications and programming libraries into a
Cloud Computing has become another buzzword after Web 2.0. However, there are dozens of different definitions for Cloud Computing and there seems to be no consensus on what a Cloud is. On the other hand, Cloud Computing is not a completely new concep
To harness the potential of advanced computing technologies, efficient (real time) analysis of large amounts of data is as essential as are front-line simulations. In order to optimise this process, experts need to be supported by appropriate tools t
Deep learning (DL), a form of machine learning, is becoming increasingly popular in several application domains. As a result, cloud-based Deep Learning as a Service (DLaaS) platforms have become an essential infrastructure in many organizations. Thes
Container technologies have been evolving rapidly in the cloud-native era. Kubernetes, as a production-grade container orchestration platform, has been proven to be successful at managing containerized applications in on-premises datacenters. However