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Local community detection, only considering the regional information of the large network, can be used to identify a densely connected community containing the seed node in a network, aiming to address the efficiency problem faced by global community detection. However, most existing studies in local community detection did not account for the higher-order structures crucial to the network, but rather have simply focused single nodes or edges. Moreover, existing higher-order solutions are not purely local methods, as they still use global search to find the best local community, which leads to a global search problem. Furthermore, the quality of the detected community depends on the location of the seed node, which leads to a seed-dependent problem. Thus, in this paper, we proposed a fuzzy agglomerative algorithm (FuzLhocd) for local higher-order community detection based on different fuzzy membership functions. To solve the global search problem, we introduce a novel, purely localized metric called local motif modularity. Based on this local metric, FuzLhocd only needs to visit a limited number of neighborhoods around the seed node. To solve the seed-dependent problem, we systematically studied the formation of the local community, divided the process of local community detection into three stages and employed various fuzzy membership functions at different stages. Our extensive experiments based on both real-world and synthetic networks demonstrated that FuzLhocd not only runs efficiently locally but also effectively solves the seed-dependent problem and achieves a high accuracy as well. We concluded that our local motif modularity metric and FuzLhocd algorithm is highly effective for local higher-order community detection.
Tao Meng; Lijun Cai; Tingqin He; Lei Chen; Ziyun Deng. Local Higher-Order Community Detection Based on Fuzzy Membership Functions. IEEE Access 2019, 7, 128510 -128525.
AMA StyleTao Meng, Lijun Cai, Tingqin He, Lei Chen, Ziyun Deng. Local Higher-Order Community Detection Based on Fuzzy Membership Functions. IEEE Access. 2019; 7 (99):128510-128525.
Chicago/Turabian StyleTao Meng; Lijun Cai; Tingqin He; Lei Chen; Ziyun Deng. 2019. "Local Higher-Order Community Detection Based on Fuzzy Membership Functions." IEEE Access 7, no. 99: 128510-128525.
Wenhong Ma; Lijun Cai; Tingqin He; Lei Chen; Zehong Cao; Renfa Li. Local Expansion and Optimization for Higher-Order Graph Clustering. IEEE Internet of Things Journal 2019, 6, 8702 -8713.
AMA StyleWenhong Ma, Lijun Cai, Tingqin He, Lei Chen, Zehong Cao, Renfa Li. Local Expansion and Optimization for Higher-Order Graph Clustering. IEEE Internet of Things Journal. 2019; 6 (5):8702-8713.
Chicago/Turabian StyleWenhong Ma; Lijun Cai; Tingqin He; Lei Chen; Zehong Cao; Renfa Li. 2019. "Local Expansion and Optimization for Higher-Order Graph Clustering." IEEE Internet of Things Journal 6, no. 5: 8702-8713.
To obtain the target webpages from many webpages, we proposed a Method for Filtering Pages by Similarity Degree based on Dynamic Programming (MFPSDDP). The method needs to use one of three same relationships proposed between two nodes, so we give the definition of the three same relationships. The biggest innovation of MFPSDDP is that it does not need to know the structures of webpages in advance. First, we address the design ideas with queue and double threads. Then, a dynamic programming algorithm for calculating the length of the longest common subsequence and a formula for calculating similarity are proposed. Further, for obtaining detailed information webpages from 200,000 webpages downloaded from the famous website “www.jd.com”, we choose the same relationship Completely Same Relationship (CSR) and set the similarity threshold to 0.2. The Recall Ratio (RR) of MFPSDDP is in the middle in the four filtering methods compared. When the number of webpages filtered is nearly 200,000, the PR of MFPSDDP is highest in the four filtering methods compared, which can reach 85.1%. The PR of MFPSDDP is 13.3 percentage points higher than the PR of a Method for Filtering Pages by Containing Strings (MFPCS).
Ziyun Deng; Tingqin He. A Method for Filtering Pages by Similarity Degree based on Dynamic Programming. Future Internet 2018, 10, 124 .
AMA StyleZiyun Deng, Tingqin He. A Method for Filtering Pages by Similarity Degree based on Dynamic Programming. Future Internet. 2018; 10 (12):124.
Chicago/Turabian StyleZiyun Deng; Tingqin He. 2018. "A Method for Filtering Pages by Similarity Degree based on Dynamic Programming." Future Internet 10, no. 12: 124.
Fusing multiple existing models for filtering webpages can mitigate the shortcomings of individual filtering models. To provide an engine for such fusion, we propose a multimodel fusion engine for filtering webpages (MMFEFWP) for the extraction of target webpages. This engine can handle large datasets of webpages crawled from websites and supports five individual filtering models and the fusion of any two of them. There are two possible fusion methods: one is to simultaneously satisfy the conditions of both individual models, and the other is to satisfy the conditions of one of the two individual models. We present the functions, architecture, and software design of the proposed engine. We use recall ratio (RR) and precision ratio (PR) as the evaluation indices of the filtering models and propose rules describing how PR and RR change when individual models are fused. We use 200,000 webpages collected by crawling the popular online shopping website "www.jd.com" as the experimental dataset to verify these rules. The experimental results show that two-model fusion can improve either PR or RR. Thus, the proposed engine has good practical value for engineering applications.
Ziyun Deng; Tingqin He; Weiping Ding; Zehong Cao. A Multimodel Fusion Engine for Filtering Webpages. IEEE Access 2018, 6, 66062 -66071.
AMA StyleZiyun Deng, Tingqin He, Weiping Ding, Zehong Cao. A Multimodel Fusion Engine for Filtering Webpages. IEEE Access. 2018; 6 (99):66062-66071.
Chicago/Turabian StyleZiyun Deng; Tingqin He; Weiping Ding; Zehong Cao. 2018. "A Multimodel Fusion Engine for Filtering Webpages." IEEE Access 6, no. 99: 66062-66071.
Community detection is a key technique for identifying the intrinsic community structures of complex networks. The distance dynamics model has been proven effective in finding communities with arbitrary size and shape and identifying outliers. However, to simulate distance dynamics, the model requires manual parameter specification and is sensitive to the cohesion threshold parameter, which is difficult to determine. Furthermore, it has difficulty handling rough outliers and ignores hubs (nodes that bridge communities). In this paper, we propose a robust distance dynamics model, namely, Attractor++, which uses a dynamic membership degree. In Attractor++, the dynamic membership degree is used to determine the influence of exclusive neighbors on the distance instead of setting the cohesion threshold. Additionally, considering its inefficiency and low accuracy in handling outliers and identifying hubs, we design an outlier optimization model that is based on triangle adjacency. By using optimization rules, a postprocessing method further judges whether a singleton node should be merged into the same community as its triangles or regarded as a hub or an outlier. Extensive experiments on both real-world and synthetic networks demonstrate that our algorithm more accurately identifies nodes that have special roles (hubs and outliers) and more effectively identifies community structures.
Tao Meng; Lijun Cai; Tingqin He; Lei Chen; Ziyun Deng; Weiping Ding; Zehong Cao. A Modified Distance Dynamics Model for Improvement of Community Detection. IEEE Access 2018, 6, 63934 -63947.
AMA StyleTao Meng, Lijun Cai, Tingqin He, Lei Chen, Ziyun Deng, Weiping Ding, Zehong Cao. A Modified Distance Dynamics Model for Improvement of Community Detection. IEEE Access. 2018; 6 (99):63934-63947.
Chicago/Turabian StyleTao Meng; Lijun Cai; Tingqin He; Lei Chen; Ziyun Deng; Weiping Ding; Zehong Cao. 2018. "A Modified Distance Dynamics Model for Improvement of Community Detection." IEEE Access 6, no. 99: 63934-63947.
Network representation learning is a method of expressing vertexes in a graph in the form of vectors, which facilitates further clustering and classification. Attributed graph is a kind of graph. In the attributed graph, each vertex has some text attribute. Through the network structure of these text attribute and attributed graph itself, it can be well clustered and classified. With the development of deep learning, the network representation learning field combined with deep learning has also produced a series of excellent algorithms, such as DeepWalk and Node2vec, etc. However, the randomly generated sequence will lose the text attribute of the vertex. This paper proposes a new algorithm PROD which enable the deepwalk algorithm to combine the probability to generate the corresponding sequence. Later experiments show that in the case of a large number of edges, this algorithm generates low-dimensional representations of vertices that are better for clustering with the kmeans algorithm than for deepwalk.
Lijun Cai; Yongbao Xu; Tingqin He; Tao Meng; Huimin Liu. PROD: A New Algorithm of DeepWalk Based On Probability. Journal of Physics: Conference Series 2018, 1069, 012130 .
AMA StyleLijun Cai, Yongbao Xu, Tingqin He, Tao Meng, Huimin Liu. PROD: A New Algorithm of DeepWalk Based On Probability. Journal of Physics: Conference Series. 2018; 1069 (1):012130.
Chicago/Turabian StyleLijun Cai; Yongbao Xu; Tingqin He; Tao Meng; Huimin Liu. 2018. "PROD: A New Algorithm of DeepWalk Based On Probability." Journal of Physics: Conference Series 1069, no. 1: 012130.
Link prediction has attracted more and more attention due to its wide application in social network analysis, bioinformatics, and personalized recommendation. One of the methods for judging whether two nodes have connections in the network is to calculate the similarity. This method not only has low computational complexity, but also can achieve better prediction results, and it's more suitable for large-scale networks. There are many similarity indexes proposed until now, but most of them only consider degree of the node and its common neighbors. With the proposal of Deepwalk, many people applied it to link prediction. However,the method of judging the similarity between two nodes simply by their distance is also one-sided. In this paper, we propose a new similarity index, called Deep Affinity (DA) index, through combining the traditional similarity index with the distance index obtained by Deepwalk and introducing the idea of clustering at the same time. After conducting experiments on different network datasets, the results show that DA-based link prediction algorithm has greatly improved the prediction accuracy, especially for large-scale network datasets.
Lijun Cai; Jibin Wang; Tingqin He; Tao Meng; Qi Li. A Novel Link Prediction Algorithm Based on Deepwalk and Clustering Method. Journal of Physics: Conference Series 2018, 1069, 012131 .
AMA StyleLijun Cai, Jibin Wang, Tingqin He, Tao Meng, Qi Li. A Novel Link Prediction Algorithm Based on Deepwalk and Clustering Method. Journal of Physics: Conference Series. 2018; 1069 (1):012131.
Chicago/Turabian StyleLijun Cai; Jibin Wang; Tingqin He; Tao Meng; Qi Li. 2018. "A Novel Link Prediction Algorithm Based on Deepwalk and Clustering Method." Journal of Physics: Conference Series 1069, no. 1: 012131.
Tao Meng; Lijun Cai; Tingqin He; Lei Chen; Ziyun Deng. K-Hop Community Search Based On Local Distance Dynamics. KSII Transactions on Internet and Information Systems 2018, 12, 3041 -3063.
AMA StyleTao Meng, Lijun Cai, Tingqin He, Lei Chen, Ziyun Deng. K-Hop Community Search Based On Local Distance Dynamics. KSII Transactions on Internet and Information Systems. 2018; 12 (7):3041-3063.
Chicago/Turabian StyleTao Meng; Lijun Cai; Tingqin He; Lei Chen; Ziyun Deng. 2018. "K-Hop Community Search Based On Local Distance Dynamics." KSII Transactions on Internet and Information Systems 12, no. 7: 3041-3063.
Data mining task is a challenge on finding a high-quality community structure from largescale networks. The distance dynamics model was proved to be active on regular-size network community, but it is difficult to discover the community structure effectively from the large-scale network (0.1-1 billion edges), due to the limit of machine hardware and high time complexity. In this paper, we proposed a parallel community detection algorithm based on the distance dynamics model called P-Attractor, which is capable of handling the detection problem of large networks community. Our algorithm first developed a graph partitioning method to divide large network into lots of sub-networks, yet maintaining the complete neighbor structure of the original network. Then, the traditional distance dynamics model was improved by the dynamic interaction process to simulate the distance evolution of each sub-network. Finally, we discovered the real community structure by removing all external edges after evolution process. In our extensive experiments on multiple synthetic networks and real-world networks, the results showed the effectiveness and efficiency of P-Attractor, and the execution time on 4 threads and 32 threads are around 10 and 2 h, respectively. Our proposed algorithm is potential to discover community from a billion-scale network, such as Uk-2007.
Tingqin He; Lijun Cai; Tao Meng; Lei Chen; Ziyun Deng; Zehong Cao. Parallel Community Detection Based on Distance Dynamics for Large-Scale Network. IEEE Access 2018, 6, 42775 -42789.
AMA StyleTingqin He, Lijun Cai, Tao Meng, Lei Chen, Ziyun Deng, Zehong Cao. Parallel Community Detection Based on Distance Dynamics for Large-Scale Network. IEEE Access. 2018; 6 ():42775-42789.
Chicago/Turabian StyleTingqin He; Lijun Cai; Tao Meng; Lei Chen; Ziyun Deng; Zehong Cao. 2018. "Parallel Community Detection Based on Distance Dynamics for Large-Scale Network." IEEE Access 6, no. : 42775-42789.
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.
Lijun Cai; Tao Meng; Tingqin He; Lei Chen; Ziyun Deng. K-Hop Community Search Based on Local Distance Dynamics. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 24 -34.
AMA StyleLijun Cai, Tao Meng, Tingqin He, Lei Chen, Ziyun Deng. K-Hop Community Search Based on Local Distance Dynamics. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; ():24-34.
Chicago/Turabian StyleLijun Cai; Tao Meng; Tingqin He; Lei Chen; Ziyun Deng. 2017. "K-Hop Community Search Based on Local Distance Dynamics." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 24-34.
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.
Ziyun Deng; Jing Zhang; Tingqin He. Automatic Combination Technology of Fuzzy CPN for OWL-S Web Services in Supercomputing Cloud Platform. International Journal of Pattern Recognition and Artificial Intelligence 2017, 31, 1 .
AMA StyleZiyun Deng, Jing Zhang, Tingqin He. Automatic Combination Technology of Fuzzy CPN for OWL-S Web Services in Supercomputing Cloud Platform. International Journal of Pattern Recognition and Artificial Intelligence. 2017; 31 (7):1.
Chicago/Turabian StyleZiyun Deng; Jing Zhang; Tingqin He. 2017. "Automatic Combination Technology of Fuzzy CPN for OWL-S Web Services in Supercomputing Cloud Platform." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 7: 1.
Synchronization-inspired clustering algorithm is recently proposed and performs many excellent characteristics on data clustering, e.g., detecting arbitrarily shaped clusters, outlier separation, and strong robustness. However, high time complexity and inefficient are two drawbacks for large scale data environment. A parallel synchronization-inspired partitioning clustering algorithm (PSync for brevity) is proposed in this paper. The PSync algorithm consists of three parts, including data partition, local parallel clustering, and global clustering optimization. In first part, a grid partition method is designed to split the large data set into several grid subsets while still retaining the continuity of data distribution. In the process of data partition, k-neighborhood region and k-core point are defined to maintain the integrity of neighbor of each object in all grid subsets. In second part, parallel execute dynamic clustering method on all grid subsets. For each grid subset, each data object is considered as an oscillator and interplayed with his neighbors over time in the dynamic clustering process. As time evolves, similar objects naturally synchronize together and form distinct clusters. To balance the relationship between neighbor distance and clustering degree of objects, a new k order parameter is applied. After the parallel dynamic clustering, each grid subset would get a grid clustering result, then a local merging method is developed to combine all grid clustering results to local clustering result. In the final part, in order to get global ideal clustering result, a global optimization method is developed to adjust the neighbor radius and repeatedly execute the local parallel cluttering to get the global optimization clustering result by using the Davies-Bouldin criterion. Extensive experiments on synthetic and real world data demonstrate the effectiveness and efficiency of PSync from the running time and three evaluation criterions.
Lei Chen; Jing Zhang; Li-Jun Cai; Ting-Qin He; Tao Meng. Parallel Synchronization-Inspired Partitioning Clustering. Journal of Computational and Theoretical Nanoscience 2016, 13, 8709 -8729.
AMA StyleLei Chen, Jing Zhang, Li-Jun Cai, Ting-Qin He, Tao Meng. Parallel Synchronization-Inspired Partitioning Clustering. Journal of Computational and Theoretical Nanoscience. 2016; 13 (11):8709-8729.
Chicago/Turabian StyleLei Chen; Jing Zhang; Li-Jun Cai; Ting-Qin He; Tao Meng. 2016. "Parallel Synchronization-Inspired Partitioning Clustering." Journal of Computational and Theoretical Nanoscience 13, no. 11: 8709-8729.
Micro-blog is a sort of open social network platform. The massive real-time messages that Twitter users publish through the micro-blog platform every day are called tweets, which are characterized with instantaneity and subjectivity and imply some topic information that contain the users' daily activities and hot news. Because of the short length and non-prominent topics of tweets, the traditional mining and clustering methods are not very effective. In order to solve the difficulty of topics mining, we try to find the relationship structure by the dependent of tweets, and propose reLDA (the abbreviation of relationship LDA) model based on the relationship structure. In particular, we build the tree structure for tweets, then deduce and improve the process of Gibbs Sampling by this model, at last we carry out the topic mining of tweets that captured by Twitter API, the result of experiment proves that our model is effective.
Lijun Cai; Wenjian Tao; Lei Chen; Tingqin He. ReLDA for micro-blog topic mining based on relationship structure. 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) 2016, 1557 -1563.
AMA StyleLijun Cai, Wenjian Tao, Lei Chen, Tingqin He. ReLDA for micro-blog topic mining based on relationship structure. 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). 2016; ():1557-1563.
Chicago/Turabian StyleLijun Cai; Wenjian Tao; Lei Chen; Tingqin He. 2016. "ReLDA for micro-blog topic mining based on relationship structure." 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) , no. : 1557-1563.
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.
Ting-Qin He; Li-Jun Cai; Zi-Yun Deng; Tao Meng; Xuan Wang. Queuing-Oriented Job Optimizing Scheduling In Cloud Mapreduce. Advances on P2P, Parallel, Grid, Cloud and Internet Computing 2016, 435 -446.
AMA StyleTing-Qin He, Li-Jun Cai, Zi-Yun Deng, Tao Meng, Xuan Wang. Queuing-Oriented Job Optimizing Scheduling In Cloud Mapreduce. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 2016; ():435-446.
Chicago/Turabian StyleTing-Qin He; Li-Jun Cai; Zi-Yun Deng; Tao Meng; Xuan Wang. 2016. "Queuing-Oriented Job Optimizing Scheduling In Cloud Mapreduce." Advances on P2P, Parallel, Grid, Cloud and Internet Computing , no. : 435-446.
Ziyun Deng; Jing Zhang; Lijun Cai; Lei Chen; Tingqin He. Matching Algorithm Based on Semantic Similarity of Service Requirement Ontology for CAE Simulation in Cloud Platform. International Journal of u- and e- Service, Science and Technology 2016, 9, 221 -242.
AMA StyleZiyun Deng, Jing Zhang, Lijun Cai, Lei Chen, Tingqin He. Matching Algorithm Based on Semantic Similarity of Service Requirement Ontology for CAE Simulation in Cloud Platform. International Journal of u- and e- Service, Science and Technology. 2016; 9 (9):221-242.
Chicago/Turabian StyleZiyun Deng; Jing Zhang; Lijun Cai; Lei Chen; Tingqin He. 2016. "Matching Algorithm Based on Semantic Similarity of Service Requirement Ontology for CAE Simulation in Cloud Platform." International Journal of u- and e- Service, Science and Technology 9, no. 9: 221-242.
In order to develop a Supercomputing Cloud Platform (SCP) prototype system using Service-Oriented Architecture (SOA) and Petri nets, we researched some technologies for Web service composition. Specifically, in this paper, we propose a reliability calculation method for Web service compositions, which uses Fuzzy Reasoning Colored Petri Net (FRCPN) to verify the Web service compositions. We put forward a definition of semantic threshold similarity for Web services and a formal definition of FRCPN. We analyzed five kinds of production rules in FRCPN, and applied our method to the SCP prototype. We obtained the reliability value of the end Web service as an indicator of the overall reliability of the FRCPN. The method can test the activity of FRCPN. Experimental results show that the reliability of the Web service composition has a correlation with the number of Web services and the range of reliability transition values.
Ziyun Deng; Lei Chen; Tingqing He; Tao Meng. A Reliability Calculation Method for Web Service Composition Using Fuzzy Reasoning Colored Petri Nets and Its Application on Supercomputing Cloud Platform. Future Internet 2016, 8, 47 .
AMA StyleZiyun Deng, Lei Chen, Tingqing He, Tao Meng. A Reliability Calculation Method for Web Service Composition Using Fuzzy Reasoning Colored Petri Nets and Its Application on Supercomputing Cloud Platform. Future Internet. 2016; 8 (4):47.
Chicago/Turabian StyleZiyun Deng; Lei Chen; Tingqing He; Tao Meng. 2016. "A Reliability Calculation Method for Web Service Composition Using Fuzzy Reasoning Colored Petri Nets and Its Application on Supercomputing Cloud Platform." Future Internet 8, no. 4: 47.
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-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.
Lei Chen; Jing Zhang; Lijun Cai; Rui Li; Tingqin He; Tao Meng. MTAD: A Multitarget Heuristic Algorithm for Virtual Machine Placement. International Journal of Distributed Sensor Networks 2015, 2015, 1 -14.
AMA StyleLei Chen, Jing Zhang, Lijun Cai, Rui Li, Tingqin He, Tao Meng. MTAD: A Multitarget Heuristic Algorithm for Virtual Machine Placement. International Journal of Distributed Sensor Networks. 2015; 2015 ():1-14.
Chicago/Turabian StyleLei Chen; Jing Zhang; Lijun Cai; Rui Li; Tingqin He; Tao Meng. 2015. "MTAD: A Multitarget Heuristic Algorithm for Virtual Machine Placement." International Journal of Distributed Sensor Networks 2015, no. : 1-14.