High-performance embedded systems require the execution of many applications on multicore platforms and are subject to stringent restrictions and constraints. The ACTORS project approach provides temporal isolation through resource reservation over a multicore platform, adapting the available resources on the basis of the overall quality requirements. The architecture is fully operational on both ARM MPCore and x86 multicore platforms

Classic task models for real-time systems focus on execution windows expressing earliest start times and deadlines of tasks for feasibility. Only within these windows the execution of tasks is feasible, and it is considered of uniform utility.Some tasks, however, have target demands in addition: a task should preferably execute at a specific target point within its execution window, but can execute around this point, albeit at lower utility. Examples of such applications include control and media processing.In this paper, we present a task model based on a gravitational analogy to address these issues. Tasks are considered as massive bobs hanging on a pendulum: a single task, left to itself, will execute at the bottom, the target point. If a force, such as the weight of other tasks, is applied, it can be shifted around this point. Thus, tasks' importance and their utility around target points can be expressed. Since the execution of a task cannot be mapped to a point in time, the model allows tasks to express an arbitrary point with its execution to represent the whole execution. This point is called the anchor point.Moreover, we show an example of a scheduling algorithm for this model which finds an approximation to the best compromise of tasks' interests based on the equilibrium state of a pendulum. Nonetheless, this task model is not restricted to a particular scheduling algorithm.Results from a simulation study show the effectiveness of the approach. This is an extended version of the ECRTS'08 paper (Guerra and Fohler 2008).

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Our society has become ever more dependent on large datacenters. Search engines, e-commerce and cloud computing are just some of the broadly used services that rely on large scale datacenters. Datacenter managers are reluctant to non-functional changes on the facilities of a perfectly operational installation as failures can be very expensive. Therefore, one of the big challenges of green computing is how to reduce the energy consumption and environmental impact of such systems without compromising the business. In this work, we propose a thermal monitoring tool for datacenters which is based on a WSN composed of ready-to-use modules. This tool provides a better understanding of the thermal behavior of datacenters and can help datacenter managers, for example, to manually adjust the cooling system in order to avoid energy waste and reduce cost. There is very low intrusiveness to the server facilities, as the tool is 100% independent of the server operability and requires only the setup of small wireless and battery powered sensors. Our tool was implemented and tested on a real datacenter in order to demonstrate the feasibility of our approach.