April - June 2007 - Kasetsart University

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Kasetsart J. (Nat. Sci.) 41 : 394 - 405 (2007) Design and Implementation of a Framework for .NET-based Utility Computing Infrastructure Thanapol Rojanapanpat * and Putchong Uthayopas ABSTRACT Future organizations must handle a very large and complex IT infrastructure that consists of very diverge and highly heterogeneous computing systems. Moreover, the future generation applications must access services and resources regardless of the geographical location, access methods, and domain of authorization. In order to meet these challenging requirements, a very high degree of virtualization has to be implemented using a smart middleware. This is a very challenging problem for both theory and practice. This paper presents a new framework called OpenUCI (Open Utility Computing Infrastructure). The OpenUCI project aims to explore the innovative design of scalable and flexible software infrastructure that manages large scale heterogeneous distributed system ranging from large Server, PC, and Mobile Devices. OpenUCI exploits a well established technology such as Grid, Web services and .NET technology to build a virtualized and unify access to resources. Basic services that need to be presented will be discussed. The prototype system has been implemented along with the prototype financial engineering application. The results are presented along with the discussion of the experiences learned. With OpenUCI, users can easily harness computing and storage of large distributed system. Key words: utility computing, .NET technology, web services INTRODUCTION The competition in business causes organizations to be ready to handle a large amount of demand of users, which need more high performance computing system. It is a risk for the small and medium organizations to invest in the high performance computing system, because they have to pay for the system maintenance cost. There are two solutions. Firstly they can outsource the computing power. The other solution is to create the supercomputing system by utilizing the already existing personal computers (PC) in their company. Building a supercomputing system from personal computers or desktop PCs now is not an imagination, because the speed and performance of PCs has been increasing as well as the speed and bandwidth of network. From this advantage, it emerges many new computing systems; one of them is the utility computing system. Utility computing (Eilam et al., 2004) is a computing model that involves the use of many diverge technology such as grid computing (Foster et al., 2002) and autonomic computing (Ganek and Corbi, 2003). Utility computing system focuses on the creating of virtual computing environment High Performance Computing and Networking Center, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand, * Corresponding author, e-mail: thanapolr@hpcnc.cpe.ku.ac.th, pu@ku.ac.th Received date : 03/10/06 Accepted date : 25/12/06
which dynamically and automatically virtualizes, provisions and manages resources and services on users’ demand. The major benefits of utility computing are • better utilization – the resources in utility computing system can be shared and used in efficient ways, • more flexibility – utility computing system provides flexibility in the creation of the dynamic computing environment which can automatically increase or decrease the computing resources corresponding to users’ demand, and • lower total cost of ownership – utility computing can provide IT and business process outsourcing which help reducing cost of investing in resources such as hardware assets, maintenance cost, training cost, etc. The design and building of utility computing infrastructure is still a complex and challenging task. Figure 1 shows the concept of resources virtualization and resources provisioning in a modern IT infrastructure. Utility computing system must consist of a way to provide both features. Firstly, resources virtualization is a feature of system that can make resources transparent to application. Since the resources that we use for build a utility computing infrastructure are PCs, these systems have a high dynamism e.g. resources can be available and unavailable from time to time. The utility computing system must have mechanisms for collecting resources and monitoring its status. Furthermore, resource virtualization should have mechanisms for discovering and accessing resources. Secondly, resources provisioning is a feature of a system to perform an on-demand resources allocation to application. A good utility computing system must have mechanism for creating the automatic adjustable virtual computing environments which consist of hardware resources and utility services in order to keep responsiveness when users’ demand increases. Moreover, the utility computing system must provide friendly-used interfaces to Kasetsart J. (Nat. Sci.) 41(2) 395 user, which may be business manager for accessing and managing this virtual computing environment. These interfaces can be Windows applications, Web applications, command line, and API. Most utility computing systems are based on current distributed system technology. Thierry (2006) provides a good survey of platform technology that is available. There are three commonly used distributed systems and technologies. The first one is distributed information system that focuses on sharing knowledge such as the web. Secondly, a distributed storage system for sharing data such as peer-topeer files sharing. Finally, distributed computing or metacomputing (Smarr and Catlett, 1992) frameworks for sharing computing power. The systems that are classified in this area and related to OpenUCI framework are the followings. Business Processes/ Appications Virtual Computing Environment Resources/ Services Resources Provisioning Virtualized Resource Business Processes/ Appications Virtual Computing Environment Resources Virtualization Resources/ Services Resources/ Services Figure 1 The relationship of resource viortualization and resource provisioning.
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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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Page 157 and 158: MATERIALS AND METHODS 1. Materials
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Page 163 and 164: Kasetsart J. (Nat. Sci.) 41 : 363 -
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 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%.
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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
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April - June 2007 - Kasetsart University

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