April - June 2007 - Kasetsart University

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398 among computers in the OpenUCI system. 3. Execution Management Service (EMS) is used to start and controls processes. Furthermore, EMS also supports the invocation of web and grid service jobs. The core services that address the resources provisioning problem consist of user management service, virtual computer management service and job management service. 1. User Management service (UMS) handles authentication, authorization, accounting and users profiles. 2. Virtual Computer Management Service (VCMS) is used for managing and controlling the virtual computing environment created by users. 3. Job Management Service (JMS) is used for creating jobs and supporting job submission from users. JMS also provides job queuing and scheduling mechanisms. 2.4 Applications and tools layer Applications and tools layer is the layer of user applications developed for using facilities of OpenUCI system. OpenUCI system also provides basic command-line tools and web application interfaces for login, logout, virtual computer creation, resources discovering, job submission and etc. There are three main components in OpenUCI system as shown in Figure 4. Users Applications <strong>Kasetsart</strong> J. (Nat. Sci.) 41(2) Manager Core Services Figure 4 The interaction of manager, worker and user. 1. Manager is a computer that provides core services used for managing shared resources and supporting incoming requests of users. 2. Workers are computers that share its’ resources such as computing power, files, storage and utility services. There are two worker types in the OpenUCI system, dedicated and non-dedicated workers. Dedicated workers are always online and cannot reject jobs assigned by managers. For nondedicated workers, they can be online or offline all the time and they will request for a job and execute it when they are not busy. 3. Users are the people who need to access resources. They can discover resources, create job, submit job, download and upload files and any services provided by managers. RESULTS AND DISCUSSION 1. Proof of concept application Currently, the high performance computing is widely needed and not limited to the computer research field anymore. The financial engineering (FE) is a field that requires the high computing power because it has to handle and analyze a large amount of data in order to reduce or keep turn around time constantly as number of users increased. We evaluated the performance of OpenUCI system by applying the existing financial engineering application named Value-at- CPU Workers Agent Storage Services
Risk (VaR) calculation which was implemented in .NET web services. The VaR measures the maximum loss money which may be occurred in portfolio at a given time horizon (time of holding portfolio) and at a given level of confidence. The formula for calculating VaR has high complexity. Then, we will show the general form of formula. VaR = –Vp* (µp – Q*σp) The V p is the portfolio value, and the µp and the σp are the expected return and the standard deviation, respectively. The Q is the quantile value of %confidence level. For example, the 99% confidence level gives ~2.326 quantile value and the 95% confidence level gives ~1.645 quantile value. In this test, we used the VaR calculation web service as a utility service of OpenUCI system which was installed to all worker machines and then we developed VaR client program with Microsoft Excel. The VaR Excel program uses the OpenUCI API to connect to manager, discover VaR web services and then invoke them. 2. Test configuration The topology of test system is shown in Figure 2. The software that was installed on each machine is shown in Table 1. 3. Test assumptions • Each worker executes only one job at a time. Since the test application is a compute intensive application, the execution of more than <strong>Kasetsart</strong> J. (Nat. Sci.) 41(2) 399 one job on each worker will not give a better performance due to the overhead of task switching. • The input data is already in the workers. This can be done by preloading fixed data and table to worker prior to the execution. Thus, the communication can be minimized which yield a better performance for the system. 4. OpenUCI throughput test We evaluated the throughput of OpenUCI by submitting jobs to OpenUCI system that has 1, 2, 4, 8, 16, and 32 workers, and the run times used for testing are changed from 10, 30, 60, 90, 120, 180, 240, and 300 seconds. Figure 5 shows the procedure of this testing. 1. The client application discover URLs of web service located on the worker nodes from the manager. 2. The manager runs the resource selection algorithm and returns the URLs of the chosen worker node to requested client application. 3. The client application uses the returned URLs for connecting and invoking web service on worker nodes. After that, the client application will wait until there are some available workers. 4. The worker node executes the service and then it returns a result to client application. 5. The client program invokes web service on an available worker Table 1 Hardware and software configuration for testing OpenUCI system. Machines Hardware Operating system Software 1 Manager AMD Athlon 2.0GHz, 512 Windows server 2003 OpenUCI Broker, MS MB RAM SQL 2005 for OpenUCI database 32 Workers Intel Celeron 2.53GHz, 512 Windows XP OpenUCI Worker, MB RAM Professional MS SQL 2005 Express for VaR database 1 User Intel Pentium M 1.5GHz, Windows server 2003 VaR client application 768 MB RAM
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April - June 2007 Volume 41 Number
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KASETSART JOURNAL NATURAL SCIENCE T
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Kasetsart J. (Nat. Sci.) 41 : 205 -
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harvesting time which started from
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y Chen and Paull (2000). Figure 1 a
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pineapple fruit allowed the accumul
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Kasetsart J. (Nat. Sci.) 41(2) 215
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Cluster analysis Cluster analysis u
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Table 3 (Continued) 1 2 3 4 5 6 7 8
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CONCLUSION AFLP markers classified
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difference of soil texture used in
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under wet soil condition planting.
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tropics. Following the expansion, w
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sealed in a plastic box with 1 cm w
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moisture content increment after so
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Frequency Frequency 30 20 10 0 20.0
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School, Kasetsart University. The a
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for combining ability of lines (Lam
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selective mass sibbing within indiv
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Although most of S 2-interfamily hy
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composite sets as used in this stud
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days. The numbers of calli producin
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the report of Ching (1982) showing
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clearly amplified bands generated b
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caused by either the recombination
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Kuhlmann, V. and B. Foroughi-Wehr.
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Xanthomonas fuscans subsp. aurantif
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was collected and washed with 70% e
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expected size was amplified with 35
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eaction technique has been used for
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Schaad, N.W., D. Opgenorth and P. G
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MATERIALS AND METHODS Model descrip
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calculated TF value calculated TF v
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sincere gratitude and deep apprecia
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1989). In monogastrics, the inclusi
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of all LLS samples in this study we
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Melbourne, Parkville, Victoria. Goe
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considered responsible for low prod
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mineral supplementation suggested b
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Body weight chane (kg/head) 32 30 2
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CONCLUSION AND RECOMMENDATION With
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SAS. (Statistical Analysis System).
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genes covering a variety of desirab
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mg/l NAA and 0.5 mg/l zeatin) incub
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5. Purification by various sucrose
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as the culture period increased. So
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culture, pp. 1-20. In L.C. Fowke an
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GC. Analytical methods Total carboh
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ash. The crude hot water polysaccha
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spirulan (H-SP), and a desulfated c
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cells often displayed an increased
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LITERATURE CITED Bell, T.A. and D.V
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for alternative sources of supply.
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BR2.1.2 formed the same clade with
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supplement with glutamate (Iida et
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optimal for S. limacinum BR2.1.2 fo
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fluorescent lamp at 1.5 kLux develo
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Wisconsin, USA). Total extracted RN
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RESULTS Cloning and sequencing of m
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Expression of rmIL-2 and purificati
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other strains of mice have differen
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dose interleukin-2 therapy promotes
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Page 153 and 154: Relative activity (%) 100 80 60 40
Page 155 and 156: Uchida, K. Tateishi, O. Shida and K
Page 157 and 158: MATERIALS AND METHODS 1. Materials
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Page 161 and 162: in Table 5-7. It was found that the
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Page 165 and 166: the cultures at 10,200 × g for 15
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Page 175 and 176: As the 25 companies were divided in
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Page 179 and 180: as piece “A” or straight as pie
Page 181 and 182: et al. (1998); Zeng (2000); and Tal
Page 183 and 184: p 1, i * Kasetsart J. (Nat. Sci.) 4
Page 185 and 186: c ≤ c2 jq2 j n q p k p * 2, j , ,
Page 187 and 188: algorithm would consider RM 5, prio
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Page 191 and 192: the maximum deviation of about 10%.
Page 193 and 194: Stock Quantities Using Heuristic Op
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Page 201 and 202: Throughput (job/sec) 7 6 5 4 3 2 1
Page 203 and 204: Speed up 40 35 30 25 20 15 10 5 0 F
Page 205 and 206: and discovery, resource selection a
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April - June 2007 - Kasetsart University

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