Virtual resource monitoring in cloud computing

Cloud computing is a new computing model. The resource monitoring tools are immature compared to traditional distributed computing and grid computing. In order to better monitor the virtual resource in cloud computing, a periodically and event-driven push （PEP） monitoring model is proposed. Taking advantage of the push and event-driven mechanism, the model can provide comparatively adequate information about usage and status of the resources. It can simplify the communication between Master and Work Nodes without missing the important issues happened during the push interval. Besides, we develop ＂mon＂ to make up for the deficiency of Libvirt in monitoring of virtual CPU and memory.

Design and application of electrical fire monitoring system in mining industry

To protect mining areas from electrical fire, it is very important to install electrical nre momtormg system to ensure safety in development of mineral resources and for buildings. In this paper, design for electrical fire monitoring and detection system with optional sensor modules has been proposed. In addition, necessity and suitability of electrical fire monitoring and detection system with optional sensor modules in mining areas have been reviewed. The designed electrical fire monitoring and detection system suit- able for work environment of mining industry is composed by host-computer monitoring software and slave-computer detectors. Monitoring detectors are manufactured by using embedded technology. Exter- nal shells deployed have superior enclosure performances and explosion-proof properties. It is easy to install and maintain the system. In general, the system has reached, or even exceeded standards specified in national standards for performances and appearances of such devices. Test results show application of electrical fire monitoring and detection system can effectively enhance monitoring intensity over the mining areas and provide reliable guarantee to ensure orderly development of mineral resources and to protect physical and property safety of citizens in these areas.

Super Resolution Sensing Technique for Distributed Resource Monitoring on Edge Clouds

With the vigorous development of mobile networks,the number of devices at the network edge is growing rapidly and the massive amount of data generated by the devices brings a huge challenge of response latency and communication burden.Existing resource monitoring systems are widely deployed in cloud data centers,but it is difficult for traditional resource monitoring solutions to handle the massive data generated by thousands of edge devices.To address these challenges,we propose a super resolution sensing(SRS)method for distributed resource monitoring,which can be used to recover reliable and accurate high‑frequency data from low‑frequency sampled resource monitoring data.Experiments based on the proposed SRS model are also conducted and the experimental results show that it can effectively reduce the errors generated when recovering low‑frequency monitoring data to high‑frequency data,and verify the effectiveness and practical value of applying SRS method for resource monitoring on edge clouds.

Prospects for the Development and Application of the Earthquake Monitoring Network

With the rapid development of the economy in China, the seismic network has been changing rapidly, in that the capability of instruments, technological systems and network density are approaching those of developed countries and a large quantity of observation data has been accumulated. How to apply these resources to economic construction and public safety has become an important issue worth studying. In order to improve earthquake prediction and earthquake emergency response, it is suggested in this paper that extracting valuable precursor information, improving earthquake rapid reporting ability and extending rapid intensity reporting function are key issues. Integrating network resources, building unified standards and a multifunction seismic monitoring network are preconditions of establishing a public safety service platform and earthquake observation resources will contribute significantly to the fields of engineering, ocean, meteorology, and environmental protection. Thus, the future directions of the development of the seismic network are exploring monitoring resources, enhancing independent innovation, constructing a technological platform and enlarging the service field.

Improving Scalability of Cloud Monitoring Through PCA-Based Clustering of Virtual Machines

Cloud computing has recently emerged as a leading paradigm to allow customers to run their applications in virtualized large-scale data centers. Existing solutions for monitoring and management of these infrastructures consider virtual machines （VMs） as independent entities with their own characteristics. However, these approaches suffer from scalability issues due to the increasing number of VMs in modern cloud data centers. We claim that scalability issues can bc addressed by leveraging the similarity among VMs behavior in terms of resource usage patterns. In this paper we propose an automated methodology to cluster VMs starting from the usage of multiple resources, assuming no knowledge of the services executed on them. The innovative contribution of the proposed methodology is the use of the statistical technique known as principal component analysis （PCA） to automatically select the most relevant information to cluster similar VMs. We apply the methodology to two case studies, a virtualized testbed and a real enterprise data center. In both case studies, the automatic data selection based on PCA allows us to achieve high performance, with a percentage of correctly clustered VMs between 80% and 100% even for short time series （1 day） of monitored data. Furthermore, we estimate the potential reduction in the amount of collected data to demonstrate how our proposal may address the scalability issues related to monitoring and management in cloud computing data centers.