The computing evolution from high performance to high efficiency of the virtual cloud is an urgent need of environmental protection and human sustainable developments. However, on the one hand, nowadays there are moderate extension demands of the hardware energy-saving space; on the other hand, meta-heuristics scheduling algorithms, such as genetic algorithms and artificial immune algorithms, underperform in the optimization dynamics with the balance conflict between convergence and distribution. In fact, there are some inevitable and logical relationships between every candidate solution (scheduling scheme) and some physical feedback; and nonlinearity and heterogeneity of the allocated resources means a big discrepancy in the feedback effects between different scheduling schemes, such as the energy-efficiencies related. Therefore, the research methods of this study are to respect the scientific laws, and to ingeniously follow the hardware energy-saving principle, in order for injecting new energy into the algorithm optimization power, and also for further enhancing the energy-saving dominance of software methods. Then, the green heterogeneous scheduling algorithm through deep integration of hardware and software energy saving principles, is presented in this paper (i.e., GHSA_di/II), with the multi angle and all-round improvements of the internal drive of co-evolutionary simulation in the meta-heuristics algorithms. The experimental results show that compared with the other three meta-heuristic heterogeneous scheduling algorithms, GHSA_di/II algorithm has obvious advantages in overall performance, energy saving, and scalability, for both data intensive and computing intensive instances.