Lin Yuan

Online Self-Reconfiguration with Performance Guarantee for Energy-Efficient Large-Scale Cloud Computing Data Centers

In a typical large-scale data center, a set of applications are hosted over virtual machines (VMs) running on a large number of physical machines (PMs). Such a virtualization technique can be used for conserving power consumption by minimizing the number of PMs that should be turned on according to the application requirements to resource. However, the resource demands for VMs is dynamic in nature since the number of user requests the applications should handle is rapidly changing in practice. It is a great challenge to online reconfigure the VMs (i.e., optimize the number and the locations for the VMs) according to the dynamic resource demands. Especially for the emerging applications of large-scale data centers for cloud computing systems, existing approaches either fails to find a best configuration of VMs or cannot produce a result in an acceptable time. In this paper, we propose an online self-reconfiguration approach for reallocating VMs in large-scale data centers. It first accurately predicts the future workloads of the applications with Brown’s quadratic exponential smoothing. Based on such a prediction, it adopts a genetic algorithm to efficiently find the optimal reconfiguration policy. The resource utilization of large-scale cloud computing data centers can thus be improved and their energy consumption can be greatly conserved. We conduct extensive experiments and the results verify that our approach can effectively switch off more unnecessary running PMs comparing with current approaches without a performance degradation of the whole system.

Mining Frequent Development Patterns of roles in Open Source Software

Participants of a software project have a significant impact on whether the project could achieve success, and the relevant information can reflect some trustworthy properties of software. By studying a large number of OSS projects in SourceForge, the role configuration of these projects is analyzed, and some latent frequent patterns are discovered in this paper. It prepares the ground for quantification and utilization of the software trustworthiness evidence from the roles information.

An indent shape based approach for web lists mining

Mining repeated patterns from HTML documents is a key step for typical applications of Web information extraction, which require efficient techniques of patterns mining to generate wrappers automatically. Existing approaches such as tree matching and string matching can detect repeated patterns with a high precision, but their efficiency is still a challenge. In this paper, we present a novel approach for Web lists mining based on the indent shape of HTML documents. Indent shape is a simplified abstraction of HTML documents in which tandem repeated waves indicate the potential repeated patterns to be detected. By identifying the tandem repeated waves efficiently with a horizontal line scanning along an indent shape, the repeated patterns in the documents can be recognized, from which the lists of the target Web page can be extracted. Extensive experiments show that our approach achieves better performance and efficiency compared with existing approaches.

Exploiting attribute redundancy for web entity data extraction

Web entities are often associated with many attributes that describe them. It is essential to extract these attributes for Web entity data extraction. This paper proposes a novel approach using duplicated attribute value pairs. We start by constructing a initial seed set of attributes including names and enumerable values, and a training set of Web pages from target website; After that we locate the position of each attribute by matching attribute values within the pages of the site with values contained in the seed set; Thirdly we choose the position with the highest supportiveness as path for extraction, which we use to extract other attribute value pairs with the same template. Finally, we conduct an extensive experimental study with large real data set to demonstrate the effectiveness of our extraction approach.

Efficient Approach for Repeated Patterns Mining Based on Indent Shape of HTML Documents

Mining of repeated patterns from HTML documents is the key step towards Web-based data mining and knowledge extraction. Many web crawling applications need efficient repeated patterns mining techniques to generate their wrapper automatically. Existing approaches such as tree matching and string matching can detect repeated patterns with high precision, but their performance is still a challenge for practical web crawling applications. In this paper, we propose an efficient approach for mining repeated patterns based on indent shape of HTML document. Indent shape is a novel and simple model of HTML document, in which tandem repeated waves have strong association with the repeated patterns to be detected. By scanning an indent shape with a horizontal indent-line from bottom to top, the tandem repeated waves are identified by filtering the wave segments with low self-similarities. After that the boundary of HTML code corresponding to repeated patterns can be identified, which could be transformed to regular expressions formal-defined easily. Extensive experiments on two practical data sets retrieved from Internet show that our approach achieves high efficiency significantly, and its precision performance is also generally better than the existing approaches.