We demonstrate progress on the deployment of two sets of technologies to support distribution grid operators integrating high shares of renewable energy sources, based on a market for trading local energy flexibilities. An artificial-intelligence (AI) grid modelling tool, based on probabilistic graphs, predicts congestions and estimates the amount and location of energy flexibility required to avoid such events. A scalable timeseries forecasting system delivers large numbers of short-term predictions of distributed energy demand and generation. We discuss the deployment of the technologies at three trial demonstration sites across Europe, in the context of a research project carried out in a consortium with energy utilities, technology providers and research institutions.

We demonstrate Castor, a cloud-based system for contextual IoT time series data and model management at scale. Castor is designed to assist Data Scientists in (a) exploring and retrieving all relevant time series and contextual information that is required for their predictive modelling tasks; (b) seamlessly storing and deploying their predictive models in a cloud production environment; (c) monitoring the performance of all predictive models in production and (semi-)automatically retraining them in case of performance deterioration. The main features of Castor are: (1) an efficient pipeline for ingesting IoT time series data in real time; (2) a scalable, hybrid data management service for both time series and contextual data; (3) a versatile semantic model for contextual information which can be easily adapted to different application domains; (4) an abstract framework for developing and storing predictive models in R or Python; (5) deployment services which automatically train and/or score predictive models upon user-defined conditions. We demonstrate Castor for a real-world Smart Grid use case and discuss how it can be adapted to other application domains such as Smart Buildings, Telecommunications, Retail or Manufacturing.

For most multi-threaded applications, data structures must be shared between threads. Ensuring thread safety on these data structures incurs overhead in the form of locking and other synchronization mechanisms. Where data is shared among multiple threads these costs are unavoidable. However, a common access pattern is that data is accessed primarily by one dominant thread, and only very rarely by the other, non-dominant threads. Previous research has proposed biased locks, which are optimized for a single dominant thread, at the cost of greater overheads for non-dominant threads. In this paper we propose a new family of biased synchronization mechanisms that, using a modified interface, push accesses to shared data from the non-dominant threads to the dominant one, via a novel set of message passing mechanisms. We present mechanisms for protecting critical sections, for queueing work, for caching shared data in registers where it is safe to do so, and for asynchronous critical section accesses. We present results for the conventional Intel R Sandy Bridge processor and for the emerging network-optimized many-core IBM R PowerEN TM processor. We find that our algorithms compete well with existing biased locking algorithms, and, in particular, perform better than existing algorithms as accesses from non-dominant threads increase.

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