Flexible Deployment of Social Media Analysis Tools, Flexible, Policy-Oriented and Multi-Cloud deployment of Social Media Analysis Tools in the COLA Project

The relationship between companies and customers and among public authorities and citizens has changed dramatically with the widespread utilisation of the Internet and Social Networks. To help governments to keep abreast of these changes, Inycom has developed Eccobuzz and Magician, a set of web applications for Social Media data mining. The unpredictable load of these applications requires flexible user-defined policies and automated scalability during deployment and execution time. Even more importantly, privacy norms require that data is restricted to certain physical locations. This paper explains how such applications are described with Application Description Templates (ADTs). ADTs define complex topology descriptions and various deployment, scalability and security policies, and how these templates are used by a submitter that translates this generic information into executable format for submission to the reference framework of the COLA European projec

Scalable Multi-cloud Platform to Support Industry and Scientific Applications

Cloud computing offers resources on-demand and without large capital investments. As such, it is attractive to many industry and scientific application areas that require large computation and storage facilities. Although Infrastructure as a Service (IaaS) clouds provide elasticity and on demand resource access, the challenges represented by multi-cloud capabilities and application level scalability are still largely unsolved. The CloudSME Simulation Platform (CSSP) extended with the Microservices-based Cloud Application-level Dynamic Orchestrator (MiCADO) addresses such issues. CSSP is a generic multi-cloud access platform for the development and execution of large scale industry and scientific simulations on heterogeneous cloud resources. MiCADO provides application level scalability to optimise execution time and costs. This paper outlines how these technologies have been developed in various European research projects, and showcases several application case-studies from manufacturing, engineering and life-sciences where these tools have been successfully utilised to execute large-scale simulations in an optimised way on heterogeneous cloud infrastructures

Towards a Cloud Native Big Data Platform using MiCADO

In the big data era, creating self-managing scalable platforms for running big data applications is a fundamental task. Such self-managing and self-healing platforms involve a proper reaction to hardware (e.g., cluster nodes) and software (e.g., big data tools) failures, besides a dynamic resizing of the allocated resources based on overload and underload situations and scaling policies. The distributed and stateful nature of big data platforms (e.g., Hadoop-based cluster) makes the management of these platforms a challenging task. This paper aims to design and implement a scalable cloud native Hadoop-based big data platform using MiCADO, an open-source, and a highly customisable multi-cloud orchestration and auto-scaling framework for Docker containers, orchestrated by Kubernetes. The proposed MiCADO-based big data platform automates the deployment and enables an automatic horizontal scaling (in and out) of the underlying cloud infrastructure. The empirical evaluation of the MiCADO-based big data platform demonstrates how easy, efficient, and fast it is to deploy and undeploy Hadoop clusters of different sizes. Additionally, it shows how the platform can automatically be scaled based on user-defined policies (such as CPU-based scaling)

Enabling modular design of an application-level auto-scaling and orchestration framework using tosca-based application description templates

This paper presents a novel approach to writing TOSCA templates for application reusability and portability in a modular auto-scaling and orchestration framework (MiCADO). The approach defines cloud resources as well as application containers in a flexible and generic way, and allows for those definitions to be extended with specific properties related to a desired container orchestrator chosen at deployment time. The approach is demonstrated in a proof-of-concept where only a minor change was required to a previously used application template in order to achieve the successful deployment and lifecycle management of the popular web authoring tool Wordpress on a new realization of the MiCADO framework featuring a different container orchestrator

A cloud-agnostic queuing system to support the implementation of deadline-based application execution policies

There are many scientific and commercial applications that require the execution of a large number of independent jobs resulting in significant overall execution time. Therefore, such applications typically require distributed computing infrastructures and science gateways to run efficiently and to be easily accessible for end-users. Optimising the execution of such applications in a cloud computing environment by keeping resource utilisation at minimum but still completing the experiment by a set deadline has paramount importance. As container-based technologies are becoming more widespread, support for job-queuing and auto-scaling in such environments is becoming important. Current container management technologies, such as Docker Swarm or Kubernetes, while provide auto-scaling based on resource consumption, do not support job queuing and deadline-based execution policies directly. This paper presents JQueuer, a cloud-agnostic queuing system that supports the scheduling of a large number of jobs in containerised cloud environments. The paper also demonstrates how JQueuer, when integrated with a cloud application-level orchestrator and auto-scaling framework, called MiCADO, can be used to implement deadline-based execution policies. This novel technical solution provides an important step towards the cost-optimisation of batch processing and job submission applications. In order to test and prove the effectiveness of the solution, the paper presents experimental results when executing an agent-based simulation application using the open source REPAST simulation framework