XecMe - Task based execution framework

  •        162

XecMe is a hosting and execution framework. It follows the task oriented design approach for solving the business problems.

http://xecme.codeplex.com/

Tags
Implementation
License
Platform

   




Related Projects

azure-batch-samples - Azure Batch and HPC Code Samples


This GitHub repository contains a set of HPC and Batch related samples that demonstrate the usage of Microsoft Azure Batch services along with some general purpose utilities. See http://azure.microsoft.com/services/batch/ for more information on the Azure Batch service.Before you can interact with the Batch service, you will need a Batch service account. For detailed information on creating a Batch account, see Create and manage an Azure Batch account in the Azure portal.

azure-event-hubs-dotnet - ☁️ .NET Standard client library for Azure Event Hubs


This library is built using .NET Standard 1.3. For more information on what platforms are supported see .NET Platforms Support.Azure Event Hubs is a highly scalable publish-subscribe service that can ingest millions of events per second and stream them into multiple applications. This lets you process and analyze the massive amounts of data produced by your connected devices and applications. Once Event Hubs has collected the data, you can retrieve, transform and store it by using any real-time analytics provider or with batching/storage adapters.

batch-shipyard - Execute batch and HPC Dockerized workloads on Azure Batch with shared file system provisioning and linking support


Additionally, Batch Shipyard provides the ability to provision and manage entire standalone remote file systems (storage clusters) in Azure, independent of any integrated Azure Batch functionality.Batch Shipyard is now integrated directly into Azure Cloud Shell and you can execute any Batch Shipyard workload using your web browser or the Microsoft Azure Android and iOS app.

doAzureParallel - A R package that allows users to submit parallel workloads in Azure


The doAzureParallel package is a parallel backend for the widely popular foreach package. With doAzureParallel, each iteration of the foreach loop runs in parallel on an Azure Virtual Machine (VM), allowing users to scale up their R jobs to tens or hundreds of machines.doAzureParallel is built to support the foreach parallel computing package. The foreach package supports parallel execution - it can execute multiple processes across some parallel backend. With just a few lines of code, the doAzureParallel package helps create a cluster in Azure, register it as a parallel backend, and seamlessly connects to the foreach package.

aztk - On-demand, Dockerized, Spark Jobs on Azure (powered by Azure Batch)


Azure Distributed Data Engineering Toolkit is a python CLI application for provisioning on-demand Spark on Docker clusters in Azure. It's a cheap and easy way to get up and running with a Spark cluster, and a great tool for Spark users who want to experiment and start testing at scale.This toolkit is built on top of Azure Batch but does not require any Azure Batch knowledge to use.



azure-batch-maya - Cloud rendering from Maya using Azure Batch


This project demonstrates cloud rendering using the Azure Batch service with integrated licensing for Maya, VRay and Arnold.Please note that the Azure Batch licensing service for Maya is currently in preview. For more information and to register your interest, please see rendering.azure.com.

Azure-Functions


Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems. Azure Functions allows developers to take action by connecting to data sources or messaging solutions, thus making it easy to process and react to events. Azure Functions scale based on demand and you pay only for the resources you consume.This repository acts as a directory for folks looking for the various resources we have for Azure Functions.

jstorm - Enterprise Stream Process Engine


Alibaba JStorm is an enterprise fast and stable streaming process engine. It runs program up to 4x faster than Apache Storm. It is easy to switch from record mode to mini-batch mode. It is not only a streaming process engine. It means one solution for real time requirement, whole realtime ecosystem.

BatchLabs - A client tool to help create, debug and monitor Azure Batch Applications


Note: BatchLabs is in preview.Batch Labs is a tool to manage your Azure Batch accounts. The goal is to implement a great user experience that will help you debug, monitor and manage your pools, jobs and tasks. It will also include expermiental features such as Batch Templates in the aim to improve your Batch experience. We are open to any feedback, ideas and contributions you might have.

azure-batch-apps-blender


This sample is based on the now-deprecated Azure Batch Apps service. The Blender sample is currently being re-written to work directly against Azure Batch. This updated version of the code can be accessed in the following fork while it is under-going development: https://github.com/annatisch/azure-batch-apps-blender/tree/dev/.Please check the issues forum for guidance on using the in-development code and to report any bugs.

Camunda - Platform for Workflow and Business Process Management


Camunda is an open source platform for workflow and business process management. You can model and execute BPMN 2.0, CMMN 1.1 and DMN 1.1. Camunda's core is a rock-solid, freaking fast execution engine that is horizontally scalable and comes with helpful web applications.

BPM-Engine - Business process execution engine


Business process execution engine

node-rupert - robust, event-emitting parallel task execution engine with logging support


robust, event-emitting parallel task execution engine with logging support

bexee BPEL Execution Engine


bexee (BPEL Execution Engine) is an open source Java engine that allows deploying and executing business processes described in the Business Process Execution Language (BPEL).

azure-event-hubs - ☁️ Azure Event Hubs service issue tracking and samples


To learn more about Azure Event Hubs, please visit our marketing page.See our Contribution Guidelines.

azure-batch-apps-python


The package is to enable Azure Batch Apps customers to interact with the Management API using Python.This client module is designed to work with the applications set up within an existing Batch Apps service. You can upload your Application Image and Cloud Assembly via the Batch Apps Portal. For more information on setting this up, check out this article.

Azure-TDSP-ProjectTemplate - Data science project template repository with standardized directory structure and document templates to support efficient project execution and collaboration


This is a general project directory structure for Team Data Science Process developed by Microsoft. It also contains templates for various documents that are recommended as part of executing a data science project when using TDSP.Team Data Science Process (TDSP) is an agile, iterative, data science methodology to improve collaboration and team learning. It is supported through a lifecycle definition, standard project structure, artifact templates, and tools for productive data science.

Azure-TDSP-Utilities - Utilities and scripts developed as part of Microsoft's Team Data Science Process for productive data science


This repository contains the Data Science Utilities developed by Team Data Science Process (TDSP) from Microsoft.Shared data science utility is a key component of TDSP. Shared data science utilities can make the execution of data science projects more efficient.

Lokad.CQRS - build scalable enterprise apps on Windows Azure


C#/.NET stand-alone library that features a CQRS engine (Command Query Responsibility Segregation) for Windows Azure, CQRS clients, cloud views and event sourcing. Lokad.CQRS also emphasizes DDD (Domain Driven Design). Tier 1 project used in production in three products at Lokad.