Limin Xiao

Bio: Limin Xiao is an academic researcher from Beihang University. The author has contributed to research in topics: Cloud computing & File system. The author has an hindex of 13, co-authored 53 publications receiving 570 citations.

A Load Balancing Strategy of SDN Controller Based on Distributed Decision

Abstract: Software-Defined Networking (SDN), enabled by Open Flow, represents a paradigm shift from traditional network to the future Internet. Replicate or distributed controllers have been proposed to address the issues of availability and scalability that a centralized controller suffers from. However, it lacks a flexible mechanism to balance load among distributed controllers. To address this problem, this paper presents DALB, a dynamic and adaptive algorithm for controller load balancing totally based on distributed architecture, without any centralized component. This algorithm is running as a module of SDN controller. On one hand, it adopts an adjustable load collection threshold so as to reduce the overhead of exchanging messages for load collection, and on the other hand it can make policy and election locality in order to reduce the decision delay caused by network transmission. In this paper, we build the prototype system on floodlight to demonstrate our design and test the performance of our algorithm.

Task-dynamic dispatching method under distributed computation mode in cloud computing environment

Abstract: The invention provides a task-dynamic dispatching method under a distributed computation mode in a cloud computing environment, which comprises the following four steps: 1. a main node receives and analyzes heartbeat information of a subsidiary node; 2. the main node previously distributes the task according to a node state table and a task state table; 3. the subsidiary node demands the task from the main node; and 4. the main node distributes the task to the subsidiary node. The method firstly considers the resource demand of the task and the performance information of the nodes, and dynamically controls the distribution of the task under the condition that the requirement is met, so that the response speed of the work and the resource utilization of the nodes are improved. The method has wide practical value and application prospect in the technical field of the distributed computation in the cloud computing environment.

A statistical based resource allocation scheme in cloud

Abstract: Recently, cloud computing has emerged as a new computing paradigm on the Internet. With the development of cloud computing, enterprise data centers shift towards a utility computing model where many critical business applications share a common pool of infrastructure resources offering capacity on demand. The virtual machine with the features of strong isolation and flexible is usually assigned as the basic unit. However, as the demand of each type of VM can fluctuate independently at run time, it becomes a challenging problem to allocate data center resources to each VM to balance the workload in the cloud. In this paper, we introduce an approach (Statistic based Load Balance, SLB) that makes use of the statistical prediction and available resource evaluation mechanism to make online resource allocation decisions. Unlike the methods that balance load based on SLA (Service Level Agreement) of VMs, SLB achieves load balancing by predicting the VM's resource demand. The approach includes two parts:(1) A data analysis of on-line historical performance for forecasting the resource demand of each VM, and (2) An algorithm for choosing a proper host in the resource pool to run the VM. Experiments show that SLB can perform load balance in time, and also perform more balanced use of different resources.

An Efficient Density-Based Local Outlier Detection Approach for Scattered Data

Abstract: After the local outlier factor was first proposed, there is a large family of local outlier detection approaches derived from it. Since the existing approaches only focus on the extent of overall separation between an object and its neighbors, and ignore the degree of dispersion between them, the precision of these approaches will be affected by various degrees in the scattered datasets. In addition, the outlier data occupy a relatively small amount in the dataset, but the existing approaches need to perform local outlier factor calculation on all data during the outlier detection, which greatly reduces the efficiency of the algorithms. In this paper, we redefine a local outlier factor called local deviation coefficient (LDC) by taking full advantage of the distribution of the object and its neighbors. And then, we propose a safe non-outlier objects elimination approach named as rough clustering based on multi-level queries (RCMLQ) to preprocess the datasets to eliminate the non-outlier objects to the utmost. Finally, an efficient local outlier detection approach named as efficient density-based local outlier detection for scattered data (E2DLOS) is proposed based on the LDC and RCMLQ. The RCMLQ greatly reduces the amount of data that needs to be quantified for local outlier factor and the LDC is more sensitive to the degree of anomaly of the scattered datasets, and so the E2DLOS improves the existing local outlier detection approaches in time efficiency and detection accuracy. Experiments show that the LDC can better reflect the true abnormal situations of the data for the scattered datasets. And the RCMLQ can be used in parallel with the traditional methods of improving the efficiency of the nearest neighbor search, which can further improve the efficiency of the E2DLOS algorithm by about 16%.

Ranking outliers using symmetric neighborhood relationship

Abstract: Mining outliers in database is to find exceptional objects that deviate from the rest of the data set. Besides classical outlier analysis algorithms, recent studies have focused on mining local outliers, i.e., the outliers that have density distribution significantly different from their neighborhood. The estimation of density distribution at the location of an object has so far been based on the density distribution of its k-nearest neighbors [2,11]. However, when outliers are in the location where the density distributions in the neighborhood are significantly different, for example, in the case of objects from a sparse cluster close to a denser cluster, this may result in wrong estimation. To avoid this problem, here we propose a simple but effective measure on local outliers based on a symmetric neighborhood relationship. The proposed measure considers both neighbors and reverse neighbors of an object when estimating its density distribution. As a result, outliers so discovered are more meaningful. To compute such local outliers efficiently, several mining algorithms are developed that detects top-n outliers based on our definition. A comprehensive performance evaluation and analysis shows that our methods are not only efficient in the computation but also more effective in ranking outliers.

Progress in Outlier Detection Techniques: A Survey

Abstract: Detecting outliers is a significant problem that has been studied in various research and application areas. Researchers continue to design robust schemes to provide solutions to detect outliers efficiently. In this survey, we present a comprehensive and organized review of the progress of outlier detection methods from 2000 to 2019. First, we offer the fundamental concepts of outlier detection and then categorize them into different techniques from diverse outlier detection techniques, such as distance-, clustering-, density-, ensemble-, and learning-based methods. In each category, we introduce some state-of-the-art outlier detection methods and further discuss them in detail in terms of their performance. Second, we delineate their pros, cons, and challenges to provide researchers with a concise overview of each technique and recommend solutions and possible research directions. This paper gives current progress of outlier detection techniques and provides a better understanding of the different outlier detection methods. The open research issues and challenges at the end will provide researchers with a clear path for the future of outlier detection methods.

基於《知網》的辭彙語義相似度計算 (Word Similarity Computing Based on How-net).

Abstract: Word similarity is broadly used in many applications, such as information retrieval, information extraction, text classification, word sense disambiguation, example -based machine translation, etc. There are two different methods used to compute similarity: one is based on ontology or a semantic taxonomy; the other is based on collocations of words in a corpus. As a lexical knowledgebase with rich semantic information, How-net has been employed in various researches. Unlike other thesauri, such as WordNet and Tongyici Cilin, in which word similarity is defined based on the distance between words in a semantic taxonomy tree, How-net defines a word in a complicated multi-dimensional knowledge description language. As a result, a series of problems arise in the process of word similarity computation using How-net. The difficulties are outlined below: 1. The description of each word consists of a group of sememes. For example, the Chinese word “暗箱(camera obscura)” is described as: “part|部件, #TakePicture|拍攝, %tool|用具 , body|身”, and the Chinese word “寫信 (write a letter)” is described as: “write|寫, ContentProduct=letter|信件”; 2. The meaning of a word is not a simple combination of these sememes. Sememes are organized using a specific knowledge description language. To meet these challenges, our work includes: 1. A study on the How-net knowledge description language. We rewrite the How-net definition of a word in a more structural format, using the abstract data structure of set and feature structure. 2. A study on the algorithm used to compute word similarity based on How-net. The similarity between sememes, that between sets , and that between feature structures are given. To compute the similarity between two sememes, we

A Survey on Virtual Machine Migration: Challenges, Techniques, and Open Issues

Abstract: When users flood in cloud data centers, how to efficiently manage hardware resources and virtual machines (VMs) in a data center to both lower economical cost and ensure a high service quality becomes an inevitable work for cloud providers. VM migration is a cornerstone technology for the majority of cloud management tasks. It frees a VM from the underlying hardware. This feature brings a plenty of benefits to cloud providers and users. Many researchers are focusing on pushing its cutting edge. In this paper, we first give an overview of VM migration and discuss both its benefits and challenges. VM migration schemes are classified from three perspectives: 1) manner; 2) distance; and 3) granularity. The studies on non-live migration are simply reviewed, and then those on live migration are comprehensively surveyed based on the three main challenges it faces: 1) memory data migration; 2) storage data migration; and 3) network connection continuity. The works on quantitative analysis of VM migration performance are also elaborated. With the development and evolution of cloud computing, user mobility becomes an important motivation for live VM migration in some scenarios (e.g., fog computing). Thus, the studies regarding linking VM migration to user mobility are summarized as well. At last, we list the open issues which are waiting for solutions or further optimizations on live VM migration.