User-perceived performance continues to be the most important QoS indicator in cloud-based data centers today. Effective allocation of virtual machines (VMs) to handle both CPU intensive and I/O intensive workloads is a crucial performance management capability in virtualized clouds. Although a fair amount of researches have dedicated to measuring and scheduling jobs among VMs, there still lacks of in-depth understanding of performance factors that impact the efficiency and effectiveness of resource multiplexing and scheduling among VMs. In this paper, we present the experimental research on performance interference in parallel processing of CPU-intensive and network-intensive workloads on Xen virtual machine monitor (VMM). Based on our study, we conclude with five key findings which are critical for effective performance management and tuning in virtualized clouds. First, colocating network-intensive workloads in isolated VMs incurs high overheads of switches and events in Dom0 and VMM. Second, colocating CPU-intensive workloads in isolated VMs incurs high CPU contention due to fast I/O processing in I/O channel. Third, running CPU-intensive and network-intensive workloads in conjunction incurs the least resource contention, delivering higher aggregate performance. Fourth, performance of network-intensive workload is insensitive to CPU assignment among VMs, whereas adaptive CPU assignment among VMs is critical to CPU-intensive workload. The more CPUs pinned on Dom0 the worse performance is achieved by CPU-intensive workload. Last, due to fast I/O processing in I/O channel, limitation on grant table is a potential bottleneck in Xen. We argue that identifying the factors that impact the total demand of exchanged memory pages is important to the in-depth understanding of interference costs in Dom0 and VMM.

A novel computable visual attention model (VAM) for video skimming algorithm is proposed. Videos bear more motion features than images do. Objects in videos cause different attention effects, depending on various situations, positions, motions, and appearances. The static visual attention model is based on spatial distribution, visual object, or both, but fall short in solving temporal attention effects. The proposed VAM model adopts the alive-time(AT) of a visual object as a new descriptor to improve the accuracy of locating highlight in a video clip, then produces better video skimming results. The model is represented by a set of descriptors to be computable and provide a generic framework for video analysis. The temporal variations of attention value in a video clip are weighted by non-linear Chi-square distribution. Then the highlights of the frames in the video are represented by the attention window (AW) and the attention values of the visual objects (AOs) are tracked and used to generate the attention curve of the video. At last, a video skimming strategy is used to select the highlights of the video by analyzing the attention curve. The experiment result shows that the proposed model makes the skimming results 15%~25% shorter than previous methods.