This paper investigates the use of genetic algorithms in conjunction with the WRF - Weather Research and Forecast numerical weather prediction system in order to optimize the physical parametrization configuration and to improve the forecast of two important atmospheric parameters: 2 meter temperature and relative humidity. Our research showed good results in improving the average prediction error in limited amount of iterations and this could prove helpful in building GA optimized ensemble forecasts, especially when focusing on specific atmospheric parameters. The optimization process performed well in finding optimal physical configurations for humidity prediction, but showed poor results for temperature forecast, more experiments need to be conducted in order to have a clear view over the utility of using GA techniques for physical parametrization optimization.

A novel, generic, framework for supporting self-organisation and self-management in hierarchical systems is presented. The framework allows for the incorporation of local self-organising and self-managing strategies at each level in the hierarchy. These local strategies determine the behaviour of that level and the effects of these strategies can be communicated to, and used by, the strategies in adjacent levels of the hierarchy. Thus, in general, strategies may be viewed as parameterised functions. Information emanating from both the lower and the upper levels in the hierarchy can be used as parameters. Information from below represents the status of the lower levels, whereas information from above can be used to influence the direction and the rate of system evolution. As the component parts of the system evolve to their goal states, the rate of evolution slows. At that point, by definition, a component is maximally contributing to the global goal state of the system as a whole. A novel concept to measure the distance that a component is from this stasis, its Suitability Index is presented and formally defined. Although the proposed framework can be generalised to any hierarchical system, this paper applies it specifically to large scale, hierarchically structured, computer systems. An implementation of this framework and an empirical study of its effectiveness has been conducted as part of the the CloudLightning Project.

The prediction of PV output represents an important task for PV farm operators as it enables them to forecast the energy they will produce and sell on the energy market. Existing approaches rely on a combination of satellite/all-sky images and numerical methods which for high spatial resolutions require considerable processing time and resources. In this paper, we propose a hybrid egde–cloud platform that leverages the performance of edge devices to perform time-critical computations locally, while delegating the rest to the remote cloud infrastructure. The proposed platform relies on novel metaheuristics algorithms for cloud dynamics detection and proposes to forecast irradiance by analyzing pixel values taken with various filters/bands. The results demonstrate the scalability improvement when using GPU-enabled devices and the potential of using pixel information instead of cloud types to infer irradiance.

The applications of Blockchain and Smart Contracts cover all aspects of digital information processing, varying from Asset Management platforms to Online Shops and Digital Identity Management. Several start-up companies and fellow researchers are interested in creating decentralized Clouds using the Blockchain as the mechanism for synchronization, identity management, and payment. This paper presents a conceptual platform exploiting Ethereum Smart Contracts to handle resource scheduling for Cloud Applications, investigates the operational constraints and measures the cost and latency for three scheduling strategies. In addition, it presents the constraints under which instances can be terminated asynchronously, which increases the cost of the transaction. The minimum cost for executing an application is similar to paying 12 hours for a standard Virtual Machine in today’s Clouds if the Smart Contracts are deployed on the Ethereum Network, which limits the interest of users with short running applications.

Predicting the consumption of individual customers using machine learning techniques requires a lot of time due to the size of the data and the increasing number of customers connected to the smart grid. One solution to avoid individual predictions is to cluster customers together based on similar patterns. We investigate the efficiency of using cluster information derived from our proposed Adaptive DBSCAN to predict individual consumption. We compare the results against standard ARIMA and the best found seasonal ARIMA model. Results on real-life data show an average deterioration of 30% with respect to the MAPE of the best found model when having enough clusters and using their center as baseline prediction models.