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It is the goal of private cloud platforms to optimize the resource allocation process and minimize the expense to process tasks. Essentially, resource allocation in clouds involves two phases: virtual machine selection (VMS) and virtual machine placement (VMP), and they can be jointly considered. However, existing solutions separate VMS and VMP, therefore, they can only get local optimal resource utilization. In this paper, we explore how to optimize the resource allocation globally through considering VMS and VMP jointly. Firstly, we formulate the joint virtual machine selection and placement (JVMSP) problem, and prove its NP hardness. Then, we propose the Resource-Decoupling algorithm that converts the JVMSP problem into two independent sub-problems: Max-Capability and Min-Cost. We prove that the optimal solutions of the two sub-problems guarantees the optimal solution of the JVMSP problem. Furthermore, we design the efficient Max-Balanced-Utility and Extent-Greedy heuristic algorithms to solve Max-Capability and Min-Cost, respectively. We evaluate our proposed algorithms on datasets with different distributions of resources, and the results demonstrate that our algorithms significantly improve the resource utilization efficiency compared with traditional solutions and existing algorithms.

Opinion mining focuses on analyzing opinions in documents. Existing most algorithms for mining opinion either are machine-only, leaving plenty of confused puzzles due to lacking human background knowledge, or using opinion dictionary from domain experts. The latter is expensive and hard to scale. In this paper, we propose a novel approach RULING (conveRging hUman knowLedge opInion miNinG) for opinion mining, where human include both the crowd and the experts. Firstly, we propose a method for combining expert knowledge with the machine learning method. Then we use the prediction result to find out the hard item, and classify them using crowdsourcing. This method can scale better than the previous methods and get a better result. Experimental results demonstrate our RULING approach outperforms related proposals in terms of classification performance.

Most existing recognition methods of multi-category words merely focus on the area of traditional Chinese words rather than the area of electronic commerce. In this paper, we propose an effective method on how to recognize Chinese multi-category words in electronic commerce using Conditional Random Field. Experimental results show that our method remarkably enhances the accuracy of the recognition, reduces the misunderstanding and improves the user experience of electronic commerce retrieval.

Most previous researches on out-of-vocabulary words recognition are concentrating on traditional areas, while in electronic commerce area this recognition is rarely involved. In this paper, we focus on the out-of-vocabulary words recognition in the field of electronic commerce. Based on the unique characteristics of electronic commerce data, we introduce the Conditional Random Fields (CRFs) into the out-of-vocabulary words recognition, and use the electronic commerce text corpus for the experimental verification. The experimental results show that CRFs in the recognition of out-of-vocabulary words in the field of electronic commerce is effective.