Enabled to provide pervasive access to distributed resources in parallel ways, heterogeneous scheduling is applied extensively in large-scale computing systems for high performance. Conventional real-time scheduling algorithms, however, disregard energy efficiency in addition to stringent timing constraints. In recognition of green computing, an energy-aware model is first presented. Second, inspired by multi-disciplines, the meta-heuristic is addressed based on the supercomputer hybrid architecture. On the other hand, some technological breakthroughs are achieved, including boundary conditions for different heterogeneous computing and grid scheduling and descriptions of real-time variation of scheduling indexes (stringent timing and energy constraints). Extensive simulator and simulation experiments highlight higher efficacy and better scalability for the proposed approaches compared with the other three meta-heuristics; the overall improvements achieve 8%, 12% and 14% for high-dimension instances, respectively.
Heterogeneous green scheduling in virtual cloud is an urgent need of human sustainable developments. However, on the one hand, there is still considerable space beyond reach of the hardware energy regulation mode; on the other hand, as the core of green software methods, meta-heuristics algorithms are still underperforming in heterogeneous scheduling, although with many achievements in homogeneous scheduling. In this paper, an efficient new meta-heuristics algorithm is presented (i.e., GHSA_di), including the co-evolutionary dynamics equation emphasizing on and taking advantage of the hardware energy-regulation principles. The experimental results show that compared with the other three meta-heuristic scheduling algorithms, GHSA_di algorithm has obvious advantages in overall performance, energy saving and scalability, for both data intensive and computing intensive instances.
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