摘要:虚拟云高性能向高效能计算演进，已是环境保护、人类可持续发展的迫切需求.然而目前，一方面，硬件级物理节能空间需要适度延展；另一方面，以遗传或人工免疫算法为代表的元启发式调度中间件大多存在进化动力不足，以致收敛性和分布性冲突难平衡等瓶颈.事实上，每个候选解（调度方案）都蕴含一定的物理反馈效应，而拟配资源的非线性和异构性，则意味着不同方案间与能效相关的实时动态反馈的巨大差异化.因此，尊重科学规律，巧妙地借力于硬件节能原理，给算法优化动力注入新能量，并进一步增强软件方法的节能主导性，是本文研究方法；继而提出一种着眼于软硬件节能原理深度融合的新的绿色异构调度算法（GHSA_di/Ⅱ），以多角度、全方位提升元启发式算法之协同进化模拟的内驱力.大量仿真实验结果显示：无论对于数据密集型还是计算密集型实例，GHSA_di/Ⅱ算法较其他3种元启发式异构调度算法，在整体性能、节能降耗以及可扩展性等方面都具明显优势.



Abstract: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.



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