Intelligent scheduling with deep fusion of hardware-software energy-saving principles for greening stochastic nonlinear heterogeneous super-systems

Abstract

Green computing of stochastic nonlinear heterogeneous super-systems, represented by the cloud, is a new demand for sustainable human developments. However, the scheduling middleware is now in urgent need of a series of theoretical breakthroughs from homogeneity to heterogeneity, linearity to non-linearity, and even fuzzy decision-making to scientific decision-making based on mathematical model. Focusing on deep fusion of hardware-software energy-saving principles, an energy-aware intelligent scheduling model and algorithm are proposed in this paper; throughout the stages of model preparation, composition and algorithm designs, three features and innovations are included, which are [InlineMediaObject not available: see fulltext.] formalizing hardware energy-saving principles via nonlinear regression quantization, [InlineMediaObject not available: see fulltext.] a comprehensive evaluation model of adaptive green scheduling for stochastic nonlinear heterogeneous super-systems, and [InlineMediaObject not available: see fulltext.] a scheduling algorithm with distributed evolutionary intelligence. Extensive simulator and simulation experiments highlight obvious superiorities in the proposed scheduler such as higher efficacy and better scalability, which fully considers nonlinear diversities of heterogeneous super-systems whether for data or computing intensive stochastic tasks.