The scalability of a computing system can be identified by at least three components: (a) size, (b) geographical 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 performance 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 efficiently as related approaches, demonstrating their applicability and scalability for the considered problem.
['Cesar O. Diaz', 'Mateusz Guzek', 'Johnatan E. Pecero', 'Pascal Bouvry', 'Samee Ullah Khan']
Scalable and Energy-Efficient Scheduling Techniques for Large-Scale Systems