Desktop grids have proved to be a suitable platform for the execution of bag-of-tasks applications but, being characterized by a high resource volatility, require the availability of scheduling techniques able to effectively deal with resource failures and/or unplanned periods of unavailability. In this paper we present a set of fault-aware scheduling policies that, rather than just tolerating faults as done by traditional fault-tolerant schedulers, exploit the information concerning resource availability to improve application performance. The performance of these strategies have been compared via simulation with those attained by traditional fault-tolerant schedulers. Our results, obtained by considering a set of realistic scenarios modeled after real desktop grids, show that our approach results in better application performance and resource utilization

Cloud computing is growing in popularity among computing paradigms for its appealing property of considering "Everything as a Service". The goal of a Cloud infrastructure provider is to maximize its profit by minimizing the amount of violations of Quality-of-Service (QoS) levels agreed with service providers, and, at the same time, by lowering infrastructure costs. Among these costs, the energy consumption induced by the Cloud infrastructure, for running Cloud services, plays a primary role. Unfortunately, the minimization of QoS violations and, at the same time, the reduction of energy consumption is a conflicting and challenging problem. In this paper, we propose a framework to automatically manage computing resources of Cloud infrastructures in order to simultaneously achieve suitable QoS levels and to reduce as much as possible the amount of energy used for providing services. We show, through simulation, that our approach is able to dynamically adapt to time-varying workloads (without any prior knowledge) and to significantly reduce QoS violations and energy consumption with respect to traditional static approaches.