No abstract

Chronic mental illnesses pose a great burden on the lives of citizens worldwide. In modern health-care, decentralization and enabling the self management of patients at home are crucial factors in improving the every-day lives of patients and the people close to them. People in general tend to dislike obtrusive monitoring on their daily activities, so how can we implement a platform that can provide clinicians with adequate and concise information on their patients health status and at the same time be unobtrusive and easy to use. Moreover, how can we make such an unobtrusive system capable of providing the doctor with highimpact warnings on the patient's health status only when it is needed, thus relieving him of unnecessary workload? In this paper, the authors present a reconfigurable Event Detection mechanism used in the ALADDIN platform for Risk Assessment and Analysis. .

One of the important issues in cloud computing is an advanced management of large scale server clusters enabling efficient energy use and SLA compliance. That includes smart placement of virtual machines to appropriate hosts and thereby, efficient allocation of physical resources to virtual machines. One of the promising approaches is to optimize the placement based on predicting future requested physical resources for each virtual machine. However, often predictions cannot always be accurate and might cause increasing rates of SLA violation. In this paper we present an adaptive algorithm for predictive resource allocation and optimized VM placement that offers a solution to this problem.