针对云计算的高能耗问题,从系统级节能角度,提出一种节能的资源调度算法。首先,建立云计算的两级资源调度模型;综合考虑主机的工作、空闲和休眠等多种状态建立能耗模型,并用多功能计量插座加以验证。然后,提出基于遗传算法的最小能耗资源调度算法(minimum energy consumption based on genetic algorithm,MECGA),根据云任务的服务质量(quality of service,QoS)需求产生初始种群,以系统能耗最小为调度目标设计适应度函数,并根据染色体适应度的正态分布函数和种群的进化代数设计遗传算子。仿真结果表明,所提算法能够有效降低系统总能耗、缩短任务完成时间。
Side information has a significant influence on the rate-distortion（RD） performance of distributed video coding（DVC）. In the conventional motion compensated frame interpolation scheme, all blocks adopt the same side-information generation method regardless of the motion intensity inequality at different regions. In this paper, an improved method is proposed. The image blocks are classified into two modes, fast motion and slow motion, by simply computing the discrete cosine transformation（DCT） coefficients at the encoder. On the decoder, it chooses the direct interpolation and refined motion compensated interpolation correspondingly to generate side information. Experimental results show that the proposed method, without increasing the encoder complexity, can increase the average peak signal-to-noise ratio（PSNR） by up to 1~ 2 dB compared with the existing algorithm. Meanwhile, the proposed algorithm significantly improves the subjective quality of the side information.
