A novel energy-driven computing paradigm for e-health scenarios

A first-rate e-Health system saves lives, provides better patient care, allows complex but useful epidemiologic analysis and saves money. However, there may also be concerns about the costs and complexities associated with e-health implementation, and the need to solve issues about the energy footprint of the high-demanding computing facilities. This paper proposes a novel and evolved computing paradigm that: (i) provides the required computing and sensing resources; (ii) allows the population-wide diffusion; (iii) exploits the storage, communication and computing services provided by the Cloud; (iv) tackles the energy-optimization issue as a first-class requirement, taking it into account during the whole development cycle. The novel computing concept and the multi-layer top-down energy-optimization methodology obtain promising results in a realistic scenario for cardiovascular tracking and analysis, making the Home Assisted Living a reality

Scalable and Energy-Efficient Scheduling Techniques for Large-Scale Systems

The scalability of a computing system can be identified by at least three components: (a) size, (b) geograph- ical distribution, and (c) administrative constraints. Newer paradigms, such as clouds, grids, and clusters bring in more parameters to the aforementioned list, namely heterogeneity, energy consumption, and transparency. To optimize the per- formance of a computing system, it is manner that exploits heterogeneity and is scalable. Moreover, newer systems also demand energy efficiency as an integral part of schedulers. In this paper, we evaluate the behavior of low complexity energy- efficient algorithms for scheduling. The set of experimental results showed that the evaluated heuristics perform as effi- ciently as related approaches; demonstrating their applicability and scalability for the considered problem