Clouds offer significant advantages over traditional cluster computing architectures including flexibility, high-availability, ease of deployments, and on-demand resource allocation – all packed up in an attractive pay-as-you-go economic model for the users. However, cloud users are often forced into vendor lock-in due to the use of incompatible APIs, cloud-specific services, and complex pricing models used by the cloud service providers (CSPs).
Cloud management platforms (CMPs), supporting hybrid and multi-cloud deployments, offer an answer by providing a unified abstract interface to multiple cloud platforms. Nonetheless, modelling applications to use multi-clouds, automated resource selection based on the user requirements from various available CSPs, cost optimization, security, and runtime adaptation of deployed applications and services still remain a challenge.
In this tutorial, we provide a practical introduction to the multi-cloud application modelling, configuration, deployment, and adaptation. We survey existing CMPs, compare their features, modelling methods, and, not the least, provide a practical hands-on training for getting your applications ready for the multi-clouds using selected tools. By the end of this tutorial, attendees should be able to understand various tools and technologies available for the multi-clouds, and prepared to spin-off their first multi-cloud ready application.
Marta Różańska, Alicja Reniewicz, Paweł Skrzypek | 7bulls.com, Poland
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Goal: To extend the MELODIC platform architecture with new solver.
Difficulty level: High
Description: The challenge is to design how can you extend Melodic with new solvers. In the challenge the selection and recommendation of some solvers should be included. It should be explained why do you think this is a good alternative to the implemented solvers. We assume usage of already existing solver, based on available libraries. The key element of the challenge will be to understand the MELODIC platform architecture and API and to properly customise the library to be used with the Melodic platform. We assume some short-cuts in the implementation, like the usage of hardcoded utility function instead of integration with Utility Generator. If the challenge will be successful then the next step would be to fix some short cuts.
Related data: download here »
Goal: Melodic platform uses current values of metrics from application (CPU, Memory, business defined metrics, etc) to adapt the deployment of the application. The goal of the challenge is to add prediction capabilities using chosen method of time series forecasting (Exponential Smoothing, ARMA/ARIMA, logistic regression, linear regression, tree based methods (Random Forest, XGboost, other), neural networks) to allow adaptation based on predicted value of time series instead of current one.
Difficulty level: Intermediate – High
Description: Within the challenge it would be needed to integrate with Melodic messaging system to gather metrics value and making forecast based on the gathered values. Forecast value should be sent as a new type of metrics to given queue and adaptation should include the predicted value. It requires to understand the Melodic messaging subsystem, to implement one forecasting method (could be simple) and to send metric to jms queue.
Requirements for the participant:
Related data: download here »