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Investigations on user perspective evaluation of some reliability aspects of web services
Published in Debatosh Guha, Badal Chakraborty, Himadri Sekhar Dutta, Computer, Communication and Electrical Technology, 2017
Subhash Medhi, Abhijit Bora, Tulshi Bezboruah
The software specifications at server side are: (a) IIS 7.5 as Web Server, (b) MS SQL version 2005 as database server, (c) Microsoft Visual Studio version 2012 as Integrated Development Environment (IDE), (d) Internet Explorer as web browser and (e) Windows Server 2008 as Operating System (OS). The software tools such as Microsoft SDK version 7.1 and EasyFit, version 6.5 are used. The hardware configuration includes Intel(R) Xenon(R) CPU E5620 processor with 2.4 GHz speed, 8 GB RAM and 600 GB hard drive. The load generator machine contains the software testing tool such as Mercury LoadRunner. We have created the service script by using the testing tool. The load was given on the WS from a remote desktop PC whose OS is windows XP. The hardware configurations for the remote desktop PC are: (i) Intel(R) Pentium (R) Dual CPUE2200, (ii) Processor speed: 2.2 GHz, (iii) RAM: 1GB and (iv) Hard drive: 150 GB.
Optimizing Join in HIVE Star Schema Using Key/Facts Indexing
Published in IETE Technical Review, 2018
Hussien SH. Abdel Azez, Mohamed H. Khafagy, Fatma A. Omara
The proposed system is developed under Linux Ubuntu LTS 12.4×64 Server with Hadoop cluster of 10 virtual machines over 3 PowerEdge T320 tower server Intel® Xeon® processor E5-2400 and E5-2400 v2 2.5MB cache per core with 32 GB RAM. 1G Ethernet network 500 GB Hard Disk over host OS Windows Server 2008 R2 by using Java SE7 with JDBC connection over HIVE 11.0.0/Hadoop 2.2.0 thrift server. Hadoop master processes (Map-Reduce Job Tracker, HDFS Name Node, and HIVE thrift server). The following configuration parameters are overridden in order to boost performance, JVM's were reused, the speculative execution was turned off, and a maximum of 1GB JVM heap space was used per task. By repeating each experiment, three times and reporting the average of the results.
Trade-off between efficiency and fairness in timetabling on a single urban rail transit line under time-dependent demand condition
Published in Transportmetrica B: Transport Dynamics, 2019
Dewei Li, Tianyu Zhang, Xinlei Dong, Yonghao Yin, Jinming Cao
This study selects Beijing Metro Changping Line as the case study. The model is solved by the approach mentioned in Section 4. The algorithm was written in C programming language. The solution process of all models was compiled using the Visual Studio IDE software. The running environment was Windows server 2008 R2 Enterprise 64-bit operating system. The computer performance was Intel Core i3, 2.20 GHz, 64 GB installed memory (RAM). The average optimization time used in the case is 5s.