Computer and Information Science (Studies in Computational Intelligence, Volume 566)
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This edited book presents scientific results of the 13th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2014) which was held on June 4-6, 2014 in Taiyuan, China. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them.
The conference organizers selected the best papers from those papers accepted for presentation at the conference. The papers were chosen based on review scores submitted by members of the program committee, and underwent further rigorous rounds of review. This publication captures 14 of the conference’s most promising papers, and we impatiently await the important contributions that we know these authors will bring to the field of computer and information science.
Evaluation methodology provides empirical evidence on the choice of unsupervised learning algorithms, and could shed light on the future development of novel intrusion detection techniques for MANETs. In “Live Migration Performance Modelling for Virtual Machines with Resizable Memory,” Cho-Chin Lin, Zong-De Jian and Shyi-Tsong Wu present a general model for live migration. An effective strategy for optimizing the service downtime under this model is suggested. The performance of live migration is.
Alarm Rate (%) Detection Rate (%) 75 65 20m/s 5 False Alarm Rate (%) Detection Rate (%) PCA 75 65 10m/s 3 2 1 0 5m/s 6 PCA 3 2 1 5m/s 20m/s 10m/s 20m/s Velocity OCSVM False Alarm Rate (%) Detection Rate (%) Velocity 20m/s 4 OCSVM 10m/s 10m/s 5 Velocity 100 95 90 85 80 75 70 65 5m/s FW Velocity 85 55 5m/s 20m/s 4 Velocity 95 10m/s Velocity FW 10m/s C-means 4 2 5m/s 20m/s 85 55 5m/s 20m/s 5 Velocity 95 10m/s Velocity Velocity 65 5m/s K-means 20m/s.
And Eq. (3) seem to have intuitively captured the cost of the live migration for a virtual machine, more precise bounds should be found by classifying the memory space into various groups according to the usages. In the next section, we will refine the expressions based on our model. 5 Performance Measurement and Discussion In this section, we modify Eqs. (2) and (3) in Sect. 4 using the first parameter of our proposed model. The performance of live migration is studied by varying the sizes of.
Figure 1 shows examples of workflow template where ti represents a task with index i and w j represents a workflow template with index j. From Fig. 1, to finish a job with workflow template w1 , the tasks must be executed in the order of t1 , t2 , and t3 . In workflow template w2 , the tasks t5 and t6 can be executed in parallel after t4 is completed. In workflow template w3 , each task has no dependency on the others, in this case, the set of edges is empty. There are several attempts on.
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