Sep 5, 2018 from Activeeon
To say it shortly, distributed computing is a model of deployed structure where different parts of applications run simultaneously on multiple computing engines. This structure can be represented as grids, clusters or clouds. Distributed computing was developed to improve and accelerate the execution of calculations.
Distributed computing solution helps to manage scientific and engineering complex problems. Thanks to distributed computing it will be possible to fulfill your operational needs effectively. Big Data Automation integrates with de-facto standards in scientific and engineering environments such as R Language, Spark, Hadoop, Kafka, Matlab, and many more.
The main role of distributed computing in your business will be to serve up the mission-critical needs of business processes across the entire chain. Nowadays, this solution helps to inject efficiencies, effectiveness and agility into the supporting information systems and business processes.
Activeeon gives to your disposal distributed computing solutions that will help you to have a deeper insight into the business goals and objectives. Our experts will help you to manage all transitions and transformations of technological landscape of your network. Thanks to our solutions you will achieve to utilize effectively your computing models, follow your strategy and reach your business goals.
It is possible to distribute parallel applications to parallel computing systems.
ProActive Distributed Matlab will help you run your computations faster and optimize your license costs. ProActive solution is flexible, and can deploy on heterogeneous infrastructures.
It is a free, open source equivalent to Matlab. Choose our grid computing solution to accelerate your computations using all your resources. It offers the same basic functionalities such as easy matrix manipulation, implementation of algorithms, interfacing. It is known as Matlab’s best open source alternative.
ProActive Distributed R integrates with the R Project for Statistical Computing to allow distributed and remote execution of R functions on heterogeneous infrastructures (Linux, Windows, MacOS X) through a powerful and user-friendly API directly from R command-line interpreter.
Dec 5, 2019 from Veranika Tsiareshchanka
A job scheduler executes workloads based on a certain scheduling policy. An advanced job scheduling solution can support several scheduling policies that determine how jobs and tasks will be scheduled. These include First-In-First-Out (FIFO), Earliest deadline first (EDF), and License-based policies....
Jul 2, 2019 from Veranika Tsiareshchanka
Job planner allows to set up custom and recurrent execution of selected workflows based on calendar rules. You can schedule recurring jobs, i.e. every hour, every 1st day of the month, every week day,...