CoMe4ACloud is an Atlanstic2020 funded project whose objective is to provide an end-to-end solution for autonomic Cloud services. To that end, we rely on techniques of Constraint Programming so as a decision-making tool and Model-driven Engineering to ease the automatic generation of the so-called autonomic managers as well as their communication with the managed system. For further information, please read the project description.
The project is led by ASCOLA research team and involves also AtlanModels and TASC, all of them from the LINA (Nantes Computer Science Laboratory) and situated at Ecole des Mines de Nantes.
We are glad to announce that our paper entitled Towards a generic autonomic model to manage Cloud Services (Jonathan Lejeune, Frederico Alvares and Thomas Ledoux) got the best paper award of CLOSER 2017 (the 7th International Conference on Cloud Computing and Services Science).
Over the last few years, Cloud Computing has revolutionized the way IT resources are managed. Its service-oriented model enables the allocation of resources on demand: consumers are able to request compute/storage/network resources almost instantaneously over the network. From the supplier's perspective, a negative consequence of this model is that it quickly generates a level of dynamicity that makes manual management of Cloud difficult to enforce service level agreements (SLAs). Autonomic Computing has been widely adopted in many fields in which systems require to be adapted dynamically, including Cloud-based systems. The MAPE-K reference architecture is used as a guide to design autonomic managers (AMs) to alleviate Cloud administrators from the burden to manually manage those complex infrastructures.
We believe that Cloud services, regardless of the layer that carries them, share many characteristics and objectives. Cloud architectures inherently expose services located in a software stack, which means that services can play the role of consumer (other services in the cloud stack) and / or provider (to end users or other services). Therefore, the objectives of any service XaaS (Anything-as-a-Service) are very similar: (i) find an optimal balance between costs and revenues (minimizing costs because of purchased services and penalties for SLA violations, while maximizing revenues related to services provided to customers); (ii) comply with all SLA constraints or internal constraints to the concerned XaaS layer (e.g., the maximum capacity of resources). In other words, any AM should be designed to find the optimal configurations of XaaS layer based on these objectives.
This project aims to design and implement a declarative approach, generic and extensible for autonomic management of Cloud services, potentially covering any layer of the Cloud services stack. The basic idea is that from a model describing such a Cloud system, Cloud administrators would be be able to automatically derive an AM for the concerned system. For this purpose, three different research challenges will be explored and discussed: