Tingqin He

MTAD: a multitarget heuristic algorithm for virtual machine placement

Cloud data centers are facing increasingly virtual machine (VM) placement problems, such as high energy consumption, imbalanced utilization of multidimension resource, and high resource wastage rate. In order to solve the virtual machine placement problems in large scale, three algorithms are proposed. Firstly, we propose a physical machine (PM) classification algorithm by analyzing pseudotime complexity and find out an important factor (the number of physical hosts) that affects the efficiency, which improves running efficiency through reduction number of physical hosts; secondly, we present a VM placement optimization model using multitarget heuristic algorithm and figure out the positive and negative vectors of three goals using matrix transformation so as to provide the mapping of VMs to hosts by comparing distance with positive and negative vectors such that the energy consumption is saved, resources wastage of occupied PM is lowered, multidimension resource utilization is optimized, and the running time is shortened. Finally, we consider the poor placement efficiency problem of large-scale virtual serial requests and design a concurrent VM classification algorithm using the K-means method. Simulation experiments validate the performance of the algorithm in four aspects, including placement efficiency, resources utilization balance rate, wastage rate, and energy consumption.

Queuing-Oriented Job Optimizing Scheduling In Cloud Mapreduce

Cloud MapReduce, as an implementation of MapReduce framework on Cloud for big data analysis, is facing the unknown job makespan and long wait time problem, which have seriously affected the service quality. The Inefficient virtual machine allocation is one critical causing factor. Based on the M/M/1 model, a new queuing equation is built to ensure the virtual machine with the high efficiency. By jointing queuing equation and objectives function, a two variables equation group is designed to compute the desired virtual machine number for different jobs. According to the desired virtual machine number of each job, we developed a queuing-oriented job optimizing scheduling algorithm, called QTJS, to optimal job scheduling and enhance the resource utilization in Cloud MapReduce. Extensive experiments show that our QTJS algorithm consumes less job execution time and performs better efficiency than other three algorithms.

K-Hop Community Search Based on Local Distance Dynamics

Community search aims at finding a meaningful community that contains the query node and also maximizes (minimizes) a goodness metric, which has attracted a lot of attention in recent years. However, most of existing metric-based algorithms either tend to include the irrelevant subgraphs in the identified community or have computational bottleneck. Contrary to the user-defined metric algorithm, how can we search the natural community that the query node belongs to? In this paper, we propose a novel community search algorithm based on the concept of k-hop and local distance dynamics model, which can natural capture a community that contains the query node. Extensive experiments on large real-world networks with ground-truth demonstrate the effectiveness and efficiency of our community search algorithm and has good performance compared to state-of-the-art algorithm.

Automatic Combination Technology of Fuzzy CPN for OWL-S Web Services in Supercomputing Cloud Platform

Supercomputing Cloud Platform (SCP) provides a simple online Web way for computer aided engineering (CAE) simulation on supercomputer “Tianhe No.1.” We develop SCP prototype by using service-oriented architecture (SOA). Fuzzy colored Petri nets (FCPN) is selected as the automatic combination technology for the Ontology Web Language for Services (OWL-S) in our SCP. To build the dependency relation graphs among Web services in our SCP, we put forward some definitions of semantic threshold similarity for Web services. Based on these definitions, we propose a generation algorithm to build the FCPN dependency relation graph based on semantic similarity of Web services, and analyze an example about this algorithm. Also, we design an algorithm to simplify the FCPN dependency relation graph for fast responding the user’s requirements. The research works of this paper (SCP prototype) have been applied in real world, and we show the engineering design and application at the end. We will further research the service verification, transaction model and exception recovery mechanism in the future.

Active Learning Method for Constraint-Based Clustering Algorithms

Semi-supervision clustering aims to improve clustering performance with the help of user-provided side information. The pairwise constraints have become one of the most studied types of side information. According to the previous studies, such constraints increase clustering performance, but the choice of constraints is critical. If the constraints are selected improperly, they may even degrade the clustering performance. In order to solve this problem, researchers proposed some learning methods to actively select most informative pairwise constraints. In this paper, we presents a new active learning method for selecting informative data set, which significantly improves both the Explore phase and the Consolidate phase of the Min-Max algorithm. Experimental results on the data set of UCI Machine Learning Repository, using MPCK-means as the underlying constraint-based semi-supervised clustering algorithm, show that the proposed algorithm has better performance.