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Virtualization technology has promoted the fast development and deployment of cloud computing, and is now becoming an enabler of Internet of Everything. Virtual machine monitor (VMM), playing a critical role in a virtualized system, is software and hence it suffers from software aging after a long continuous running as well as software crashes due to elusive faults. Software rejuvenation techniques can be adopted to reduce the impact of software aging. Although there existed analytical model-based approaches for evaluating software rejuvenation techniques, none analyzed both application service (AS) availability and job completion time in a virtualized system with live virtual machine (VM) migration. This paper aims to quantitatively analyze software rejuvenation techniques from service provider and user views in a virtualized system deploying VMM reboot and live VM migration techniques for rejuvenation, under the condition that all the aging time, failure time, VMM fixing time and live VM migration time follow general distributions. We construct an analytical model by using a semi-Markov process (SMP) and derive formulas for calculating AS availability and job completion time. By analytical experiments, we can obtain the optimal migration trigger intervals for achieving the approximate maximum AS availability and the approximate minimum job completion time, and then service providers can make decisions for maximizing the benefits of service providers and users by adjusting parameter values.

Excessive workplace stress affects the individual's health as well as social collaborations, so the management of stressors is essential. However, an individual worker who only subjectively reflects on his or her individual and social stressors may misinterpret them, and thus not be able to manage them. This paper aims at engaging workplace stress reflection on objective stress-related physiological data using a shared display, which provides an anonymous view of the individual stress-related physiological signals (i.e., heart-rate variability) through a collective visualization. A minimalist proof-of-concept system is implemented for investigating the design space and deployed during group collaboration. The user study results show that the visualization successfully drew the participants' awareness and increased their understanding of self and organizational stress. This work highlights the importance of objective physiological data in the reflection process of organizational stress management.

Real-time stereo vision is attractive in many areas such as outdoor mapping and navigation. As a popular accelerator in the image processing field, GPU is widely used for the studies of the stereo vision algorithms. Recently, many stereo vision systems on GPU have achieved low error rate, as a result of the development of deep learning. However, their processing speed is normally far from the real-time requirement. In this paper, we propose a real-time stereo vision system on GPU for the high-resolution images. This system also maintains a low error rate compared with other fast systems. In our approach, the image is resized to reduce the computational complexity and to realize the real-time processing. The low error rate is kept by using the cost aggregation with multiple blocks, secondary matching and sub-pixel estimation. Its processing speed is 41 fps for $2888\times 1920$ pixels images when the maximum disparity is 760.