Pub. online:1 Jan 2018Type:Research ArticleOpen Access
Volume 29, Issue 2 (2018), pp. 187–210
A relevant challenge introduced by decentralized installations of photo-voltaic systems is the mismatch between green energy production and the load curve for domestic use. We advanced an ICT solution that maximizes the self-consumption by an intelligent scheduling of appliances. The predictive approach is complemented with a reactive one to minimize the short term effects due to prediction errors and to unforeseen loads. Using real measures, we demonstrated that such errors can be compensated modulating the usage of continuously running devices such as fridges and heat-pumps. Linear programming is used to dynamically compute in real-time the optimal control of these devices.
Volume 26, Issue 1 (2015), pp. 1–15
The brokering of the best Cloud proposals that optimizes the application requirements allows to exploit the flexibility of the Cloud programming paradigm by a dynamically selection of the best SLA, which is available into the market. We present in this paper ascalable multi-users version of a Broker As A Service solution that uses the available resources of a distributed environment, and addresses related issues. The brokering problem is divided into simpler tasks, which are distributed among independent agents, whose population dynamically scales together the computing infrastructure, to support unforeseeable workloads produced by the interactions with large groups of users. The brokering model and its implementation, which adopts Cloud technologies itself, are described. Performance results and effectiveness of the first prototype implementation are discussed.