HCOS:A Unified Model and Architecture for Cloud Operating System

Currently, Infrastructure as a Service(IaaS) and Platform as a Service(PaaS) platforms play a role as a cloud operating system(COS).They are separated from each other in resource management,which may cause inconsistent resource status and result in the decrease of performance. Moreover, heterogeneous resources are not managed well in existing cloud computing platforms. Referring to the theory of operating system, we propose a unified architecture model of cloud operating system, which has six layers corresponding to the layered architecture of legacy operating system. Based on this architecture, a new cloud operating system called Hua-Cloud Computing System(HCOS) is realized. In HCOS, the hybrid resources are managed in a unified way. This method improves the unified scheduling capability of heterogeneous resources and eliminates the problem of resource status inconsistency.The main characteristics of HCOS are introduced and two typical applications are illustrated in this paper.

A Ka-band Solid-state Transmitter Cloud Radar and Data Merging Algorithm for Its Measurements

This study concerns a Ka-band solid-state transmitter cloud radar, made in China, which can operate in three different work modes, with different pulse widths, and coherent and incoherent integration numbers, to meet the requirements for cloud remote sensing over the Tibetan Plateau. Specifically, the design of the three operational modes of the radar（i.e., boundary mode M1, cirrus mode M2, and precipitation mode M3） is introduced. Also, a cloud radar data merging algorithm for the three modes is proposed. Using one month＇s continuous measurements during summertime at Naqu on the Tibetan Plateau,we analyzed the consistency between the cloud radar measurements of the three modes. The number of occurrences of radar detections of hydrometeors and the percentage contributions of the different modes＇ data to the merged data were estimated.The performance of the merging algorithm was evaluated. The results indicated that the minimum detectable reflectivity for each mode was consistent with theoretical results. Merged data provided measurements with a minimum reflectivity of -35 dBZ at the height of 5 km, and obtained information above the height of 0.2 km. Measurements of radial velocity by the three operational modes agreed very well, and systematic errors in measurements of reflectivity were less than 2 dB. However,large discrepancies existed in the measurements of the linear depolarization ratio taken from the different operational modes.The percentage of radar detections of hydrometeors in mid- and high-level clouds increased by 60% through application of pulse compression techniques. In conclusion, the merged data are appropriate for cloud and precipitation studies over the Tibetan Plateau.

An Uncertainty Enhanced Trust Evolution Strategy for e-Science

Resources shared in e-Science have critical requirements on security.Thus subjective trust management is essential to guarantee users＇ collaborations and communications on such a promising infrastructure.As an important nature of subjective trust,uncertainty should be preserved and exhibited in trust definition,representation and evolution.Consider the drawbacks of existing mechanisms based on random mathematics and fuzzy theory,this paper designs an uncertainty enhanced trust evolution strategy based on cloud model theory.We define subjective trust as trust cloud.Then we propose new algorithms to propagate,aggregate and update trust.Furthermore,based on the concept of similar cloud,a method to assess trust level is put forward.The simulation results show the effiectiveness,rationality and efficiency of our proposed strategy.