Ziyun Deng

Fast Community Detection Based on Distance Dynamics

The distance dynamics model is excellent tool for uncovering the community structure of a complex network. However, one issue that must be addressed by this model is its very long computation time in large-scale networks. To identify the community structure of a large-scale network with high speed and high quality, in this paper, we propose a fast community detection algorithm, the F-Attractor, which is based on the distance dynamics model. The main contributions of the F-Attractor are as follows. First, we propose the use of two prejudgment rules from two different perspectives: node and edge. Based on these two rules, we develop a strategy of internal edge prejudgment for predicting the internal edges of the network. Internal edge prejudgment can reduce the number of edges and their neighbors that participate in the distance dynamics model. Second, we introduce a triangle distance to further enhance the speed of the interaction process in the distance dynamics model. This triangle distance uses two known distances to measure a third distance without any extra computation. We combine the above techniques to improve the distance dynamics model and then describe the community detection process of the F-Attractor. The results of an extensive series of experiments demonstrate that the F-Attractor offers high-speed community detection and high partition quality.

An Improved Community Detection Algorithm Based on the Distance Dynamics

To solve the slow convergence or non-convergence problem of traditional distance dynamics method, an improved community detection algorithm based on distance dynamics is proposed in this paper for speeding up the time efficient and accuracy of algorithm. Our improved algorithm firstly analyzes the node cohesiveness and slow convergence problems. Secondly, the neighbor cohesion is defined and two new interaction patterns are designed to enhance the accuracy of community. Finally, the convergence coefficient is defined to judge whether the dynamic interaction process is in the slow convergence or non-convergence status. In the dynamic interaction process, when the percentage of non-converged distance is less than convergence coefficient, the final value of the distances are pre-judged and the whole algorithm is over. The experimental results show our improved algorithm has a fast convergent speed and accurate detection results.

Locality-Aware and Energy-Aware Job Pre-Assignment for Mapreduce

Cloud Map-Reduce (CMR) is an advantage Map-Reduce platform and has been aroused more and more attention. To further balance the performance of job secluding among job cost, execution time and energy consumption, a locality-aware and energy-aware job pre-assignment algorithm is proposed for Map-Reduce of CMR in this paper. Firstly, the importance of rack in data locality and energy saving is analyzed. Secondly, a capacity pre-judged method is developed to measure the idea capacity of one rack for different jobs where the energy-efficient is defined to measure the balance statues of rack usage among job cost, execution time and energy consumption in job scheduling. Finally, based on the pre-judged idea capacity of racks, job pre-assignment method is proposed to centrally assign one job to virtual machines of several booked racks for saving energy and reducing communication. By comparing with other three algorithms, the extensive experimental results show our algorithm has good performance on job execution time, cross rack traffic, 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.