The project GreenIT aims to provide a holistic autonomic energy-efficient solution to manage, provision, and administer the various resources of Cloud-Computing (CC) data/HPC centers.
The main research challenges that will be tackled to achieve the holistic approach are:
- Development of a multi-objective mathematical meta-model: CC is a complex system of numerous pervasive devices that request services over heterogeneous network infrastructures from a data center that is energy gobbler. Because each computing entity’s performance is defined uniquely, we must develop a multi-objective meta-model that can adequately define a unified and performance metric of the whole system. The multiple constraints and objectives dealing with the quality of service (QoS), cost and environment impact must be formulated and their relationship analyzed.
- Develop resource management and optimization methodologies: With several possible objectives and constraints, the meta-models must result in multi-objective multi-constraint optimization problems (MOP). Green-ICT will develop, refine, and evolve solutions for MOP that will primarily be based on metaheuristics (e.g. multi-objective evolutionary algorithms, multi-objective local search, hybrid metaheuristics).
- Develop autonomic resource management: The anytime anywhere slogan only will be effective when an autonomic management of resources can be achieved. The resource allocation methodologies developed must go further refinement such that the system at hand is self- healing, repairing, and optimizing. In particular, it is our intention to utilize multi-agent systems (MAS) that can learn to adapt (machine learning methodologies) and gracefully evolve to adapt (evolutionary game theoretical methodologies).