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In order to solve the problems of uneven resource utilization and long task completion time in complex task group scheduling, this paper constructed a complex task group resource scheduling model to minimize the mean square error of resource load and the task group completion time, and proposed a multi-objective optimization algorithm based on boundary range local search and NSGA-Ⅱ, called BRLSN. The algorithm used an effective coding method and cross mutation operator for iterative optimization, and constructed an elite retention strategy based on local search in boundary region to expand the search scope of the algorithm and preserved good individuals in the population. Experimental results show that compared with other multi-objective algorithms, the convergence and diversity of BRLSN are significantly improved. At the same time, the algorithm convergence speed is faster, the population quality is higher, and the result value of the final objective function is obviously optimized. [ABSTRACT FROM AUTHOR]

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In order to understand the development of outlier detection technique detailly, this paper overviewed the outlier detection techniques in recent years. Firstly, it introduced and summarized the definition of outliers, the causes of outlier and classification of the outlier mining algorithms. Next, it overviewed outlier detection methods based on neighbor, based on distributed framework and based on deep learning. Especially, it discussed the most representative methods in outlier detection, and pointed out the merits and drawbacks. In the end, this paper concluded and discussed the future direction of outlier detection techniques. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In cloud computing,mining anomaly pattern of nodes plays an important role in promptly diagnosing node states of resource pool. According to the data characters of computing resource,storage resource and network resource,it preprocessed the information of node status. And it used association rule algorithm to mine the rules among the nodes' parameter status of resource pool,e. g. "high level-high level" and"low level-high level". This paper proposed an improved Eclat algorithm .called i-Eclat, which based on vertical data format of restrictive attribute connection,i-Eclat at reduced the number of connections by transforming the format of state data and constructing a non-frequent 2-itemsets, as well as it constructed the storage structure to limit the redundant attribute connections. Extensive experiments have been conducted to demonstrate that this method can find the hiding rules among resource pool's nodes. And it also shows that i-Eclat at outperforms traditional methods on computing efficiency, especially on processing big data sets. [ABSTRACT FROM AUTHOR]

In the process of bit-stream unknown protocol identification，how to separate multi-protocol data frames into single protocol data frames is a challenging issue. To solve this problem，we propose an improved algorithm based on the idea of traditional AGNES algorithm. Combining the features of bitstream data frames, this algorithm can define the similarity between data frames and the similarity between clusters by two different ways. We perform clustering and extract clusters that meet the requirements of the class cluster simultaneously. Protocol data frames can be clustered quickly and effectively without inputting the number of clusters. And a similarity evaluation is included in the results o£ class clusters. Tests on the data set published by the Lincoln Laboratory show that the algorithm has a higher accuracy rate for clustering protocol data frames. [ABSTRACT FROM AUTHOR]