As the need for cloud services has been growing steadily, the size and energy consumption of datacenters have increased significantly over the past years. Due to economic and environmental constraints, energy efficiency in datacenters and greenhouse emissions have become a major concern. Renewable energy is widely seen as a promising solution to supply datacenters using local energy, without greenhouse gas emissions. However, the intermittent power generation resulting from the use of renewable energy imposes a paradigm change in the way energy and computation activities are managed. On the one hand, service placement and scheduling may be used on the IT (information technologies) side to adapt to the available power. On the other hand, the storage units may be used to lessen power generation variations. Existing literature and actual deployment mainly design optimization algorithms including the entire system (from cloud service to electrical management, the latter often being neglected or simplified). Conversely to these approaches, we propose a solution where each side optimizes its own objectives, both interacting through a negotiation loop process to reach a common agreement. In this paper, we present DATAZERO, a project developing this idea to ensure high availability of IT services, avoiding unnecessary redundancies, under the constraints due to the intermittent nature of electrical and cloud services flows.

Data publishing is a challenging task for privacy preservation constraints. To ensure privacy, many anonymization techniques have been proposed. They differ in terms of the mathematical properties they verify and in terms of the functional objectives expected. Disassociation is one of the techniques that aim at anonymizing of set-valued datasets (e.g., discrete locations, search and shopping items) while guaranteeing the confidentiality property known as $k^m$ -anonymity. Disassociation separates the items of an itemset in vertical chunks to create ambiguity in the original associations. In a previous work, we defined a new ant-based clustering algorithm for the disassociation technique to preserve some items associated together, called utility rules, throughout the anonymization process, for accurate analysis. In this paper, we examine the disassociated dataset in terms of knowledge extraction. To make data analysis easy on top of the anonymized dataset, we define neighbor datasets or in other terms datasets that are the result of a probabilistic re-association process. To assess the neighborhood notion set-valued datasets are formalized into trees and a tree edit distance (TED) is directly applied between these neighbors. Finally, we prove the faithfulness of the neighbors to knowledge extraction for future analysis, in the experiments.

Multi-agent models and simulations are used to describe complex systems in domains such as biological, geographical or ecological sciences. The increasing model complexity results in a growing need for computing resources and motivates the use of new