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In Figure 2(b), the power consumption order of the scheduling algorithms from highest to lowest is: (1) Max- Min, (2) Qos-Min-min,(3)Min-Min,(4)Sufferage, (5)EDF, (6)MCT, and (7)PFM. PFM has the lowest power consumption because it takes into account the real time power consumption when scheduling tasks. V. CONCLUSIONS In this paper we propose a power aware job scheduling algorithm based on feedback control theory and characterize workload through data gathered from trace data to capture possible application behavior. The job scheduling model is independent of implementation platforms and therefore feasible for future applications on multi-processor systems. By exploring the nature of dependence of server performance on time-varying load and operating conditions, the proposed general framework is possibly applicable to a diverse spectrum of server-based applications. In the future, several extensions can be made further under different dimensions. For example, the scheduled tasks will be extended to be real tasks whose actual execution time is not known until its completion. And the scheduler can be implemented as a kernel module in an OS to provide power aware job scheduling. ACKNOWLEDGMENT The funding support of this work by Natural Science Fund of China (Grant No. 60873023), Science and Technology Research and Development Program of Zhejiang Province, China (Grant No. 2009C31033), Key Program of Science and Technology Planning of Hydraulic Bureau of Zhejiang Province, China (Grant No. RB0927), and Hangzhou Dianzi University Startup Fund (Grant No. KYS 055608058) are greatly appreciated. REFERENCES [1] C. Lefurgy, K. Rajamani, F. Rawson, W. Felter, M. Kistler, and T.W. Keller, “Energy management for commercial servers,”Computer, Vol.36, pp.39-48,2003. [2] X. Wang, M. Chen, C. Lefurgy,and T.W. Keller, “SHIP: Scalable hierarchical power control for large-scale data centers,”in Proceedings of 2009 18th International Conference on Parallel Architectures and Compilation Techniques(PACT’09),pp.91-100,2009. [3] X. Wang,and M. Chen , “Cluster-level feedback power control for performance optimization,” in Proceedings of IEEE 14th International Symposium on High Performance Computer Architecture(HPCA 2008), pp.101-110, Feb. 2008. [4] Report to Congress on Server and Data Center Energy Efficiency. U.S. Environmental Protection Agency, ENERGY STAR Program, August 2, 2007.Available at:http://www.energystar.gov/ia/partners/prod_developmen t/downloads/EPA_Datacenter_Report_Congress_Final1.pd f [5] Y. Wang,K. Ma, and X. Wang, “Temperature-constrained power control for chip multiprocessors with online model estimation,” in Proceedings of the 36th annual international symposium on Computer architecture (ISCA’09),pp.314-324,June 2009. [6] C. Franke, J. Lepping, and U. Schwiegelshohn, “Greedy scheduling with custom-made objectives,”Annals of Operations Research,in press. [7] T.D. Braun, H.J. Siegel, N. Beck,L. L. Bölöni,M. Maheswaran,A.I. Reuther,J.P. Robertson, et al., “A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems, ” Journal of Parallel and Distributed Computing, Vol. 61,pp.810-837,2001. [8] X. He, X. Sun, and G.V. Laszewski, “QoS guided Min- Min heuristic for grid task scheduling, ”Journal of Computer Science and Technology, Vol.18,pp. 442- 451,2003. [9] M. Maheswaran, S. Ali, H.J. Siegel, D. Hensgen, and R.F. Freund, “Dynamic mapping of a class of independent tasks onto heterogeneous computing systems,” Journal of Parallel and Distributed Computing, Vol. 59, pp.107- 131,1999. [10] L.D. Briceño , M. Oltikar, H.J. Siegel, and A.A. Maciejewski, (). “Study of an iterative technique to minimize completion times of non-makespan machines,” in Proceedings of 2007 IEEE International Parallel and Distributed Processing Symposium (IPDPS 2007),pp.1- 14,2007 200
ISBN 978-952-5726-09-1 (Print) Proceedings of the Second International Symposium on Networking and Network Security (ISNNS ’10) Jinggangshan, P. R. China, 2-4, April. 2010, pp. 201-205 Networked Manufacturing Resources Optimization Deployment for Complicated Parts Wenli Peng 1* , Zhongbao Qin 2 , and Wenni Zhang 3 1 College of Electro-mechanics Engineering, Jiaxing University, Jiaxing, China Email: peng_wenli@163.com 2 Xi’an Institute of high-technology, Xi’an, China Email: zhongb_qin@163.com 3 College of Foreign Languages, Jiaxing University, Jiaxing, China Email: zhangwenni.pc@163.com Abstract—Agile of physical manufacturing unit is the competitive advantage in the networked manufacturing environment. It is believed that the agile can be reached by optimization deployment of networked manufacturing resources. To solve this problem, logical manufacturing unit (LMU) and logical manufacturing process (LMP) are put forward and defined to decompose and model networked manufacturing task according to the process of complex part. When selecting manufacturing resources for these manufacturing tasks, many factors should be taken into account. However, manufacturing cost, time to market and manufacturing quality are the most important factors. In this paper, networked manufacturing resources pre-allocate is carried out to find candidate manufacturing resources based on abilities of manufacturing resources, such as part family, geometric feature, material type, rough type, dimension range, machining method, precision grade and production type. Then, taking transportation time and cost besides manufacturing time, cost and quality into consideration, the target and constraint of manufacturing resources optimization deployment are analyzed, and manufacturing resources optimization deployment problem is considered as a multi-objectives optimization problem. Index Terms—manufacturing resources optimization deployment, physical manufacturing unit, logical manufacturing unit, logical manufacturing process, executive manufacturing process I. INTRODUCTION With the development of network and information technologies, the manufacturing of complex products can be achieved based on agile manufacturing through collaborating of enterprises. In this paper, the model of networked manufacturing resources optimization deployment was built in considering the processing cost, time, manufacturing quality [1]. But in the networked manufacturing environment, the physical manufacturing units are geographically distributed, so transportation cost and time between different physical manufacturing units are significant enough not to be ignored, and should be taken into consideration[2]. Thus, the model of networked manufacturing resources optimization deployment was much more complicated [3]. This resembles the partner selection in virtual enterprise, but *Corresponding author 86-573-83647635; Project Number: 70508003 there were many differences. In our views, before those factors were considered, manufacturing resources abilities such as part family, geometric feature, material type, rough type, dimension range, machining method, precision grade and production type, which can be realized by physical manufacturing units, should be taken into consideration. We call this process as the networked manufacturing resources pre-allocate[4]. II. NETWORKED MANUFACTURING TASK DECOMPOSITION AND DESCRIPTION Definition 1: Physical manufacturing unit (PMU) is composed of all physical equipments located at the same place, including machining equipments, fixture accessories, and computers and so on. The basic information of PMU includes location information, workshop name, enterprise name, typical products, personnel information, contact method and other main information. A. Designing logical manufacturing unit Definition 2: Logical manufacturing unit (LMU) expresses the manufacturing sub-process, and is defined as the aggregation of work procedures. The information model of LMU is described by eight elements: LMU(Pf,F,Ma,R,D;Mach,Pre,Pro). Where: Pf expresses part family; F expresses geometric feature; Ma expresses material type;R expresses rough type; D expresses dimension range; Mach expresses machining method; Pre expresses precision grade; Pro expresses production type. From the definition of LMU and the characteristics of these eight elements, a LMU has the following characteristics: All elements inside a LMU have close relationship each other, but the relationship between every two LMU-s is loose; each work procedure is included in a LMU, but a LMU can include several work procedures [5]. B. Designing logical manufacturing process Definition 3: Logical manufacturing process (LMP) is defined as the abstract description of the whole production procedure of a complex part. It is composed of a sequence of some LMU-s and the dynamic information of each LMU, namely the restrictions of © 2010 ACADEMY PUBLISHER AP-PROC-CS-10CN006 201
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Proceedings The Second Internationa
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Table of Contents Message from the
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Zuming Xiao, Zhan Guo, Bin Tan, and
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Message from the Symposium Chairs T
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Second International Symposium on N
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model that can deal with time serie
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training for the Wushu competition
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information, called weak uncertain
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Student side Student side Student s
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If we define element of student as
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QoS, each frame data is divided int
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for Imaging Two and Three Phase Flo
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A. Profiling&following control algo
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Research and Realization about Conv
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PDF document structure is a tree st
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support 10 Mb / s. But ENC28J60 onl
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condition the first byte output of
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According to maximum membership deg
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ubber according to the mass ratio o
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Ⅲ. AN IMPROVED DNA ALGORITHM FOR
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Ⅴ.CONCLUSION REMARKS DNA computer
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its first child q 1 on the left, th
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chains can greatly improve efficien
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II. RELATED WORK A. Mobile Service
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special services. SOAP is used to b
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detection methods of DDoS attacks m
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Step4 calculate the new subordinate
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Figure 1. An analysis of the partit
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The preceding three formulas can be
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And the decay speed of buffer seque
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distance of view point. Given that
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Ⅳ. EVALUATION OF BLENDED LEARNING
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symmetric with respect to the origi
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each sample belongs to each categor
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A. Data Preparing To generate our t
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oth α and β . determination of Qu
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Strong earthquake 0.1< M L
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indirect causes, and the logical re
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egression. Granger causality test i
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B. Evaluation We have evaluated thi
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used as a source and neighboring ce
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Figure 3. Drainage networks generat
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Combinational logic unit failures i
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Tabal.1 Combinational fault logic t
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under the endorsement of both the m
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TABLE I. DESCRIPTION OF PROPOSITION
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Figure 2. The process of the invers
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Figure 9. (a)the original image.(b)
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In order to reduce to the number of
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that studying being going to be to
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Reference[10] analyzed the evolutio
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module, communication module, apper
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After the comprehensive performance
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system testing can be seen that the
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III. AUTONOMIC RESOURCE ALLOCATION
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The QoE i (T i ) is the ith user’
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(2) In a process (tokens from outsi
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The reduction process consists of t
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all eight normal vectors are identi
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is B object , and the number of emp
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xi, j y' = εα ( (1 + tanh( )) −
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Error 8000 7000 6000 5000 4000 3000
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Architecture (AMBA) a new bus archi
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In order to ensure the smooth proce
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In this paper, we adopt two Sobel o
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four layers.The far right of the gr
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TABLE II. OPERATIONAL EMPLOYEE TABL
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A. Object-relational Type To audit
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to execution time. Also, DBA would
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Liping Chen .......................
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