Sun Zhiguo

Xia Hui

Wang Wensheng

An Architecture for the Agricultural Machinery Intelligent Scheduling in Cross-regional Work based on Cloud Computing and Internet of Things

Keywords: Agricultural machinery, Cross-regional work, Intelligent scheduling, Cloud computing, Internet of Things, GPS, Bei-dou

The paper introduces the problems in china's agricultural machinery information. We provide an architecture for the agricultural machinery intelligent scheduling in cross-regional work. We put forward constructing the private cloud of agricultural machinery with the aid of cloud computing technology, and forward agricultural machinery will link together through Internet of Things technology. We provide an information platform and simplified it to three components including information service system, communication line and monitoring front-end equipment machine carrying. We also describes two modes to realize the intelligent scheduling function of agricultural machinery cross-regional working.

Introduction

The agricultural mechanization is a key measure to improve efficiency of agriculture production. The agricultural mechanization of china has grown increasingly, presented a good situation of fast and sound development since 2000. Today, the agricultural machinery with high performance and big power and compound working keeps high speed growth, the structure of agricultural machinery equipment has made a remarkable improvement, and the level of farmland working mechanization has risen considerably.

In recent years, the informationization construction of agricultural machinery has developed to some extent in our country, and information network services are further provided too. But because the informationization construction of agricultural machinery started later in China, the whole level is still lower, there exists differences especially in the development and utilization of agricultural machinery information resource, comparing with developed countries and other domestic industries. The negative effects of unrestricted flows of agricultural machines begin to show gradually. The situation of gathering and loss of control may occur sometimes, and cause society instability incidents. The unrestricted flows of agricultural machines are as follows: 1. wasting energy; 2. lower working efficiency; 3. instability in service prices; 4. increasing traffic pressure; 5. the damage of fields water conservancy facilities resulted by repeat movement; 6. no farm machines and implements to hire in some remote areas.

We will construct an advanced intelligent scheduling platform for agricultural machinery cross-regional working with advanced communication and information technologies such as Internet, mobile telephone, fixed-line telephone, 3G, GPS, Bei-Dou satellite navigation system, cloud computing and Internet of Things and so on, to implement the guidance and service of administrative departments to agricultural mechanization production, promote the restricted and efficient flows of agricultural machinery, improve the utilization and benefit of agricultural machinery, provide alldirectional services for agricultural machinery users and farm households.

Functions

The platform can command and dispatch farm machines and implements to execute cross-regional working and accomplish the tasks of tillage and cultivation and harvest according to the factors such as crop mature time, weather, farm machines distribution in different areas of our country. It can realize various functions including inquiry of farm machines position, track review, information reception and release, state monitoring of farm machines, failure remote diagnosis of farm machines, inquiry of maintenance and oil supply sites, and measure of farmland area and estimation of crop yields.

3 Architecture design

Design of overall information network architecture

We will construct an advanced intelligent scheduling platform consisting of information system, database, users at all levels and farm machines for agricultural machinery cross-regional working. The information system and database will be constructed and monitored uniformly by central government with the aid of cloud computing technology and constructing the private cloud of agricultural machinery, namely agricultural machinery cloud that will control all computing resource of information system and storage resource of database. The system users consist of agricultural mechanization administrative departments from central government to local governments, service organizations of agricultural machinery and agricultural machine users. The agricultural mechanization administrative departments at all levels and service organizations of agricultural machinery can supervise the farm machinery and implements in their areas and realize the intelligent scheduling of cross-regional working by using the information system uniformly. The agricultural machine users can get related scheduling information and all kinds of service information with the system. The trends, static state and other information factors related to agricultural machinery production including crop mature time in different areas of our country, farm machines distribution and state, current crop planting and disasters such as drought or waterlog will be stored in the database of agricultural machinery cloud, and the database can get information related to agricultural machinery production by network connecting to the databases of national departments of atmosphere, earthquake monitoring and water conservancy in time. With Internet of Things technology, farm machines and implements in all areas can communicate with datacenter of information service platform in wireless mode by configuration of monitoring front-end equipments machine carrying, and upload all kinds of information such as running state and geographical position automatically.

Fig. 1. The information network architecture

The information platform can be simplified to three components including information service system, communication line and monitoring front-end equipment machine carrying.

Design of information service system

The customer level of information service system will consist of central system, local classification system, agricultural machinery service organization system and agricultural machine users system.

The central system has two main functional modules which are system maintenance and resource distribution module and intelligent scheduling module of agricultural machinery cross-regional working. The management and maintenance of whole system are taken in charge by central government with the system maintenance and resource distribution module. The intelligent scheduling module of agricultural machinery cross-regional working can command and control farm machines and implements to execute cross-regional working and accomplish the tasks of tillage and cultivation and harvest, realize the functions including inquiry of farm machines position and track review. The local classification system can be divided into multi-level such as province level, ground level, county level and other higher and lower system according to the detailed condition in different areas. The systems at all levels have the similar functions; the main function module is intelligent scheduling module of agricultural machinery cross-regional working that has similar function comparing with the scheduling module in central system.

The agricultural machinery service organization system that is grass-roots organizations of agricultural machinery management mainly comprises two functional modules which are intelligent scheduling module of agricultural machinery crossregional working and farm machines management module. The function of intelligent scheduling module of agricultural machinery cross-regional working is similar to that of central and local systems. The farm machines management module can provide data management for farm machines in the whole large-scale system. All farm machines registered in system should belong to a certain grass-roots agricultural machinery service organizations in principle, the service organizations should acknowledge and supervise the information validity of farm machines belonging to them.

The agricultural machine users system can mainly provide some scheduling services and additional service automatically. The agricultural machine users or farm households can receive scheduling instructions automatically and answer back, and get some additional service interactively such as location navigation service, farm machines state alarm, failure remote diagnosis of farm machines and inquiry of maintenance and oil supply sites.

Design of communication lines and monitoring front-end machine carrying

The users at all levels can connect to information platform through various Internet connection modes directly. They also can encrypt the data transmitting end-to-end with installation of SSL VPN considering the data confidentiality of the entire system. The devices used to connect to Internet can support all kinds of information terminals, and normal agricultural machine users can utilize the function such as failure remote diagnosis of farm machines by furnishing mobile video terminals such as cell phones.

The farm machines and implements can connect to information platform and upload data by the suggesting three patterns as follow considering different situations of all parts of our country: ② Binding RFID on the farm machines, the agricultural machinery service organizations install mobile RFID reader in the areas in which signals are acceptable, and read the running state data of farm machines terminals needing management in their area of jurisdiction intently, and upload data by mobile network uniformly. The pattern adapts to the group working of farm machines and implements, for example, various farm machines and implements are combined together to form a comprehensive service farm machine group, these farm machines and implements typically move and are supervised uniformly.

③ The farm machines and implements can upload data with handheld devices or netbooks after getting data manually. The pattern adapts to the farm machines and implements without automatic uploading conditions. It is a good supplement to the first pattern.

The monitoring front-end equipment farm machine carrying is a small scale integration instrument installing on the farm machine, can integrate all kinds of sensors and data collection devices, and upload data automatically. For example, it can integrate GPS or Bei-Dou positioning module to get position information and record the movement tracks of farm machines and implements for intelligent scheduling of cross-regional working; it can integrate video camera to record the working state of farm machine users; it can integrate oil circuit sensor to get oil supply information, and upload data to agricultural machinery cloud, and perform remote computing combining with its movement state to match the oil supply time and best supply sites, then transmit the data to farm machine users as reference; it can integrate key running position sensor to monitor the working state and help making failure diagnosis; it can integrate metered sensor to record farm machines working distance.

Fig. 3. The monitoring front-end equipment

The front-end equipment machine carrying can send position information and various data of running state to agricultural machinery cloud continuously with Internet of Things in working time of farm machine, and provide decision making references and service information. If data transmission fails and overruns the preset time, the information platform system will automatically alarm and send cell phone message to farm machine users by the system, or the operators of agricultural machinery service organizations will contact with users by telephone or other measures to deal with the farm machine.

Design of intelligent scheduling of agricultural machinery cross-regional working

There are two modes as follows to realize the intelligent scheduling function of agricultural machinery cross-regional working:

The manual deployment mode. The deployment modes of systems at all levels are abstracted to a unified mode that is deployment between higher and lower. The flow chart is as follow:

Fig. 4. The manual deployment mode

The meaning of flow chart is as follows: ① The lower system of district A requests higher system to deploy farm machines to district A.

② The administrative personnel of higher system finds the lower system of district B has spared farm machines after reviewing the system data collected from all districts, then gives deployment instructions to the lower system of district B, and deploys X farm machines to district A ③ The lower system of district B replies to higher system about the details of deployment, and dispatches spared farm machine starting off. ④ The higher system sends the detailed deployment program to lower system of district A, then the lower system of district A implements the program, the flow of cross-regional deployment comes to an end.

Intelligent study and automatic scheduling mode. Basing on prior consideration of manual deployment mode, the system has a scheduling mode of automatic matching. According to the scheduling requirements proposed by administrators of scheduling centers of system at all levels, the system automatically sends recommended program of automatic matching based on model algorithm to system scheduling centers at all ① ② ③ ④ A lower system B lower system

The higher system levels considering the factors including weather and mature time, and scheduling administrators of system at all levels make comprehensive judgment according to existent experience and issue scheduling instruction. The system also has automatic study function, can correct the scheduling model continuously according to the final scheduling program implemented by scheduling administrators of system at all levels.

To realize the intelligent scheduling model algorithm, the factors including weather and crop mature time should be considered; the smallest distance matrix of all deployment sites and the smallest path matrix relevant should be computed using Floyd algorithm; the tasks are assigned by sweep algorithm; the task routes are sorted by genetic algorithm; the existing research results related to multi-depot vehicle scheduling problem home and abroad need to be studied. The paper focus on architecture research, so do not analyze the detailed algorithm in depth hereon.

Fig. 2 .

 2 Fig. 2. Three patterns of connect to information platform and upload data ① Installing GPS tracker or Bei-Dou tracker on the farm machines, and machines transmit data to datacenter by mobile network including GPRS, TD, WCDMA. It is the main connection pattern of the platform.② Binding RFID on the farm machines, the agricultural machinery service organizations install mobile RFID reader in the areas in which signals are acceptable, and read the running state data of farm machines terminals needing management in their area of jurisdiction intently, and upload data by mobile network uniformly. The pattern adapts to the group working of farm machines and implements, for example, various farm machines and implements are combined together to form a comprehensive service farm machine group, these farm machines and implements typically move and are supervised uniformly.③ The farm machines and implements can upload data with handheld devices or netbooks after getting data manually. The pattern adapts to the farm machines and implements without automatic uploading conditions. It is a good supplement to the first pattern.The monitoring front-end equipment farm machine carrying is a small scale integration instrument installing on the farm machine, can integrate all kinds of sensors and data collection devices, and upload data automatically. For example, it can integrate GPS or Bei-Dou positioning module to get position information and record the movement tracks of farm machines and implements for intelligent scheduling of cross-regional working; it can integrate video camera to record the working state of farm machine users; it can integrate oil circuit sensor to get oil supply information, and upload data to agricultural machinery cloud, and perform remote computing combining with its movement state to match the oil supply time and best supply sites, then transmit the data to farm machine users as reference; it can integrate key running position sensor to monitor the working state and help making failure diagnosis; it can integrate metered sensor to record farm machines working distance.

Acknowledgements

. The work is supported by the Academy of Science and Technology for Development fund project "intelligent search-based Tibet science & technology information resource sharing technology", the National Science and Technology Major Project of the Ministry of Science and Technology of China (Grant No. 2009ZX03001-019-01), and the special fund project for Basic Science Research Business Fee, AII (No. 2010-J-07)


Zhang Yue

Yang Jing

Liu Lin

S U Yuan

X U Ling

Zhu Youyong

Chengyun Li
email: li.chengyun@gmail.com

Analysis of Secretary Proteins in the Genome of the Plant Pathogenic fungus Botrytis cinerea

Keywords: Botrytis cinerea, signal peptide, secretary protein, host-targeting-motif Botrytis

The signal peptides prediction algorithm SignalP v3.0, subcellular protein location prediction algorithm TargetP.v1.1, potential GPI-anchor sites prediction algorithm big-PI predictor, trans-membrane domains prediction algorithm TMHMM v2.0 and bioinformatics algorithm MEME were used to analyze 16446 protein sequences of Botrytis cinerea. The results showed that there were 579 deduced secretary proteins. Among these proteins, the minimum and maximum of open read frame were 102 bp and 4848 bps respectively and mean score was 1271 bps. The signal peptides' length was concentrated to 16~39 amino acids and the average length was 21. 122 of these proteins contain the highly conserved host-targeting-motif RxLx within 100 residues adjacent to the signal peptide cleavage site. According to PEDNAT and COG of GenBank database, this motif's functions include metabolism modification and cell secretion etc. We blast those putative secretary proteins with RxLx motif in GenBenk and found 47.54% of them have highly conserved homologues in other species, among them 74.1% have putative protein domains. This means these proteins may be more stable or earlier origin. We suppose these proteins are candidate participating in the pathogenesis of Botrytis cinerea but we still need more experimental evidence to confirm their definite functions.

helpful to reveal its pathogenesis mechanisms. The secreted proteins used to be synthesized by ribosome and need a transport process to secrete outside the cells.

There are two mechanisms for peptides transportation. The fist is cotranslational transfer. In this way, synthesized partial signal peptide combined to endoplasmic reticulum and the secreted proteins were synthesized meanwhile entered the endoplasmic reticulum, after moderated by endoplasmic reticulum and Golgi complex they were secreted outside. The second is posttranslational translocation.

By this mechanism the complete proteins were synthesized and then were transported for modifying with the help of leader peptide [START_REF] Meyer | The signal hypothesisa working model[END_REF]. In both mechanisms, the signal peptide has played the fundamental role. The signal peptide was usually composed by 10 to 60 amino acids. It contained a hydrophobic region (H-region) which was constituted by 6 to 15 amino-acid residues in the center and hydrophilic N terminal and C terminal at the both sides [START_REF] Martoglio | Signal sequences: more than just greasy peptides[END_REF]. According to Gunter Blobel's signal peptide hypothesis, secretary protein's destiny was decided by its signal peptide and this peptide will be cut off when the protein arrive its destination. So we can decide whether a protein is a secretary protein by analysis of its signal peptide of N terminal [START_REF] Cheng-Gang | Bioinfornation methods and practice[END_REF]. Several software had been developed to indentify the signal peptide in the protein. Lee used SignalP(v2.0) analyzed 47 secretary protein and 47 other proteins of Candida albicans, it shows that the putative results of this software is credible [START_REF] Lee | Ananalysis of the Candida albicans genome database for soluble secreted proteins using computer-based prediction algorithms[END_REF].

The interactions between pathogens and their hosts is a hot spot for scientific research recently. How the secretary proteins entered the plant cells and play their function is still not clear now. Bacteria's type III secretary system have been illustrated by many researchers, but the pathway of the eukaryotic pathogen's secretary proteins is still unclear [START_REF] Christie | architecture, and function of bacterial type IV secretion systems[END_REF][START_REF] Journet | Type III secretion: A secretory pathway serving both motility and virulence (review)[END_REF]. There is a report revealed that during the process of Plasmodium Falciparum infected erythrocyte, most of secretary proteins which will be injected into erythrocyte contain an RxLxE/D/Q motif at 60 amino-acid residues downstream the cutting site of the signal peptide [START_REF] Hiller | A host-targeting signal in virulence proteins reveals a secretome in malarial infection[END_REF]. Souvik Bhattacharjee also indicated that RxLx motif also existed in hundreds of pathogenic secretary proteins of Phytophthora infestans which play the same function as RxLxE/D/Q motifs of Plasmodium Falciparum. Plasmodium Falciparum and Phytophthora infestans are far related and infect animal and plant respectively; they should have different pathogenic process and mechanism, so RxLx motif may be a conserved signal recognition motif of eukaryotic pathogen [START_REF] Bhattacharjee | The malarial host-targeting signal is conserved in the Irish potato famine pathogen[END_REF]. In this study, we try to make use of the genome data to indicate how many secretary proteins contained by Botrytis cinerea and whether RxLx motif exist in this saprophytic fungi's secretary proteins and play pathogenic function.

Materials and methods

The sequence data of Botrytis cinerea was downloaded from the database of BROAD institute(Botrytis cinerea strain B05. [START_REF] Bhattacharjee | The malarial host-targeting signal is conserved in the Irish potato famine pathogen[END_REF] [START_REF] Lee | Ananalysis of the Candida albicans genome database for soluble secreted proteins using computer-based prediction algorithms[END_REF], secreted outside the cells not to other subcellular organelle, not anchoring protein and didn't contain transmembrane helix as secretary proteins. Then we use MEME(Multiple Expectation Maximization for Motif Elicitaion) [START_REF] Bailey | Fitting a mixture model by expectation maximization to discover motifs in biopolymers[END_REF] to test whether these putative secretary proteins contain RxLx motif and whether there exist conserved amino-acid residue at the two sides of this motif. Then the graph was produced by software LOGO [START_REF] Crooks | A sequence logo generator[END_REF] according to the result of MEME. We also compared the sequence of these putative secretary proteins with RxLx motif to PEDNAT database(http://pedant.gsf.de/index.jsp)and searched them in the COG database from GeBank (http://www.ncbi.nlm.nih.gov/COG/old/xognitor.html)to find and categorized the putative functions of these proteins. At last, we blast these sequence in the GeBank ( BLASTP 2.2.17 (Jun-24-2007) (http://www.ncbi.nlm.nih.gov/)try to find the homologues of these proteins and conjectured their functions.

Results and analysis

As figure 1 showed, we get 868 proteins contained the signal peptide in the Botrytis cinerea's genome, among them 579 proteins which account for 3.52% of the Botrytis cinerea's genes were predicted to be secretary proteins by the method have been mentioned. The average length of the predicted secretary proteins of Botrytis cinerea is 1271bp; the longest one and the shortest one are 4848bp and 102bp respectively.

From figure 2, we can find that the most of them are 500-2000bp, which account for 72.19% of all the genes. The average length of these secretary proteins' peptide is 21 amino-acid residues, the longest one is 39 and the shortest one is 16. The figure 3 showed the length distribution of these proteins' peptide. 540 of them are 17-25 amino-acid residues which account for 93.3% of all and proteins with 19 amino-acid residues come up to 109 proteins (18.8%). We analyzed the all these putative secretary proteins by MEME software and found that there are 122 proteins (21% of total) contain RxLx motif within the 100 amino-acid residues downstream of the cutting site of signal peptide. The report had showed that Plasmodium Falciparum's pathogenic secretary proteins had conserved E, D or Q fowled the RxLx motif while Phytophthora infestans didn't. But from the figure 4, we can found that there more A, G, L and S appeared at the downstream and upstream of the RxLx motif in Botrytis cinerea. A, G and L are all nonpolar amino acid and S is polar neutral amino acid. The most conserved amino-acid residue followed the RxLx motif is D. This is just the same as Plasmodium Falciparum. Then we blast all these proteins in the GenBank and found that 58 proteins have high conserved homologues (E-value〈1×10-20 and identities〉40%) in other species (accounted for 47.54% of all predicted proteins) and most of them have putative conserved protein domains (Table 2). Then we selected 7 the most conserved (have more than 50 homologues) proteins and marked out the location of their RxLx motif and protein domains (Figure 6). We can see that in 6 proteins, the motif appeared within the first 10 amino-acid residues of the C terminals of their protein domains.

Discussion

By application of software of bioinformatics' analysis, we found 579 putative secretary proteins and 122 (21%) of them contained Host-Targeting-motif RxLx within the 100 amino-acid residues downstream of their cutting sites in the genome of Botrytis cinerea. The length of most of these proteins and their signal peptide are moderate. RxLx motif spread in the secretary proteins of this kind of saprophytic fungi indicate they maybe play functions in this fungus's pathogenic secretary pathways. But we still need experiment evidence to conform this hypothesis.

By compared these proteins contain RxLx motif in the PEDANT database and COG of GenBank, we seldom found the definite description to them. For those proteins with description in COG, most of them are related to cell metabolism and cellular process. Interestingly, among these proteins, BC1G_06540 is described as an aspartic proteinase precursor. It is had already been revealed that Botrytis cinerea could secreted aspartic proteinase into its host cell during its pathogenic process [START_REF] Ten Have | An aspartic proteinase gene family in the filamentous fungus Botrytis cinerea contains members with novel features[END_REF]. Researchers also found that the a pathogens infected the plant, it must break through the plant cell's physical outline such as cell wall as well as changed the inner environment of host cells to fit themselves. In this process, their secretions must play the most important roles [START_REF] Haldar | Common infection strategies of pathogenic eukaryotes[END_REF]. Considered other 5 RxLx motif containing proteins with definite descriptions in PEDANT database are all enzymes involved in the cell metabolism and the categorization with most proteins by COG is related to amino-acid metabolism, we supposed that maybe Botrytis cinerea need secreted proteinase into the plant cells in its pathogenic process. Blast these RxLx motif containing proteins in the GenBank, we find that 47.54% of them have high conserved homologues in other species and most have the conserved protein domains. This indicates these proteins with RxLx motif are conserved during the evolution process or early originated in the history. Most of the homologues contained in fungi but still some appeared in higher eukaryotes. Among them, BC1G_14702, BC1G_14244, BC1G_05799 and BC1G_06035 contained huge number of homologues distributed in so many different kinds of plants and animals. This indicated that maybe these genes played the irreplaceable role in organisms as well as Botrytis cinerea. BC1G_05799 had been depicted as peptidylprolyl isomerase in both COG and PEDANT database. This kind of protein interconverts the cis and trans isomers of peptide bonds with the amino acid proline and involved in many cellular process. The 6 of 7 RxLx motifs in the highest conserved putative secretary proteins are all located at the N terminal of their protein domains. Interestingly, the description of protein domains contained by these 7 proteins indicated they all maybe involved in the secretion process even in the pathogenic process. Cerato-platanin had been reported as a kind of phytotoxic protein which was secreted by Ceratocystis fimbriata f.sp. platani [START_REF] Pazzagli | Characterization, and Amino acid sequence of cerato-platanin, a new phytotoxic protein. from Ceratocystis fimbriata f.sp. platani[END_REF].

FKBP_C is related to protein synthesis and locate of plant plasmids [START_REF] Li | Phylogenomic Analysis Identifies Red Algal Genes of Endosymbiotic Origin in the Chromalveolates[END_REF].

Glyco_hydro_7 is a kind of glycosyl hydrolase which was secreted to destroy cellulose in the plant cell [START_REF] Sulzenbacher | Structure of the Fusarium oxysporum endoglucanase I with a nonhydrolyzable substrate analogue: substrate distortion gives rise to the preferred axial orientation for the leaving group[END_REF]. SpeB have been reported as a kind of virulent effectors secreted by Group A Streptococcal [START_REF] Kansal | Inverse Relation between Disease Severity and Expression of the Streptococcal Cysteine Protease, SpeB, among Clonal M1T1 Isolates Recovered from Invasive Group A Streptococcal Infection Cases[END_REF]. GPI8 is related to the synthesis of phosphatidylinositol anchor protein which could anchor it to the cell surface [START_REF] Vidugiriene | The GPI anchor of cell-surface proteins is synthesized on the cytoplasmic face of the endoplasmic reticulum[END_REF]. So these proteins may play their functions in the Botrytis cinerea's secretion pathway and contributed to its pathogenic process.

Most of pathogenic proteins reported now are secretary proteins. In this article we predicted 579 secretary proteins and 122 of them contain Host-Targeting-motif RxLx which could be treated as candidate pathogenic proteins of Botrytis cinerea. Although we still need experiment to prove whether these proteins contributed to pathogeneses, find these candidate proteins will accelerate our understandings of pathogenic mechanism of Botrytis cinerea. Many software used to analyze the protein had been proved effective and it is conveniently for us to understand the information lied in the genome by the help of them.

Fig 1 .Fig 3 .

 13 Fig 1. Analysis flow chart of Botrytis cinerea secretary proteins

Fig 4 .

 4 Fig 4. Logo shows the conservation of the RxLx motif from predicted Botrytis cinerea secretary proteins The motifs are highlighted in board letters in the table. Abbreviations for amino acid residues: A, Ala; C, Cys; D, Asp; E, Glu;F, Phe; G, Gly; H, His; I, Ile; K, Lys; L, Leu; M, Met; N, Asn; P,Pro; Q, Gln; R, Arg; S, Ser; T, Thr; V, Val; W, Trp; Y, Tyr.

Fig 5 .

 5 Fig 5. Functional categorization of putative secretary proteins containing RxLx motif which have function descriptions in COG database

Fig 6 .

 6 Fig 6. The location of RxLx motif and putative protein domain of 7 proteins which contain over 50 homologs in other species

Table

  ). The most of them are Tab 1. The functional description of secretary proteins containing the RxLx motif in Botrytis cinerea

	Gene name	start	end	Functional description
	BC1G_15580 29028	29981	may act as a sorting receptor in the delivery of vacuolar hydrolases, partial
	BC1G_05799 27623	28429	peptidylprolyl isomerase
	BC1G_12776 79340	77335	tripeptidyl peptidase
	BC1G_04994 285638 283980	hypothetical protein similar to alpha-L-arabinofuranosidase
	BC1G_06540 150762 152263	hypothetical protein similar to aspartic proteinase precursor
	BC1G_03579 412126 410768	hypothetical protein similar to aspartyl protease
	related to cell metabolism and some of them also appeared in the Phytophthora
	infestans's pathogenic secretary proteins.

Table 2 .

 2 The blast results of putative secretary proteins include RxLx motif

	Gene name	number of homologs	No. of the containing species	No. of species Pathogenic	Conserved protein domain


Hao Zhang
email: zhanghaohnnd@126.com

Li Ding

Guang Zheng

Xin Xu

Lei Xi

Xinming Ma

Design and Implementation of Crop Potential Model System Based on GIS and Componentware Technology

Keywords: componentware, crop potential output, GIS, UML

Based on SuperMap IS.NET and the empirical models about crop potential output, the paper firstly designed the model system of crop potential output by using componentware method based on distributed computing architecture under network environment. Secondly, the paper implemented crop potential model components by using componentware technology. Finally, with the abstract mechanism of interface, the paper integrated crop potential output models and loosely coupled model components with SuperMap GIS. The results show that the model system as a component container about crop potential output model integrated empirical and mechanism models and provided a dynamic management for crop potential output models and dynamic methods call, which solved the issues of integration and expansion, and the system has the characteristics of wide applicability and good independence, which provides ADM and technical support for the construction of major grainproducing areas, crop production management and potential mining.

Introduction

Currently, digital model on crop production system is the foundation and the core of the digital agriculture, and is also the bridge linking the planting digitization, intelligentization and precision [START_REF] Gao | The Combination of Crop Simulation and Cultivation Optimization Theory-RCSODS[END_REF][START_REF] Zhao | Development Platform for Agricultural Intelligent System Based on Techno-Componentware Model[END_REF][START_REF] Cao | Crop Management Knowledge Model[END_REF][START_REF] Xi | Design and Implementation of Crop Growth Simulation System Based on Techno-Componentware Model[END_REF]. The significance of crop potential output model lies in the quantitative analysis and evaluation for the factors' role to the whole crop producing stage, and explains the factors' impact to crop potential output in detail. Crop potential model has advantages of strong explanatory power, wide application and easy to quantify and be controlled.

With the development of crop potential output evaluation model, componentware and GIS, GIS-driven software development methods and componentware technology have been widely used in various types of GIS application system. It is effective to reduce the coupling GIS with model system and improve the maintainability and independence by using componentware technology [START_REF] Zhang | Design and Realization of GIS-Based County Testing Soil for Wheat Formulated Fertilization System[END_REF][START_REF] Zhu | Application of Soft Component Technology to Design of Intelligent Decision-Making System for Crop Management[END_REF]. In this case, the paper adopted the componentware method to build a strong extension and low coupling crop potential model system based on SuperMap IS.NET 2008, accomplished crop production information management and potential analysis, and provided technical support and decision-making for managing crop production and mining crop potential.

2

Data Source and Research Method

Data Source

Research data includes meteorological data, soil data, socio-economic data and crop production data. Meteorological data includes daily average temperature, daily maximum temperature, daily minimum temperature, rainfall and sunshine hours, provided by the weather bureau of Henan Province. Soil information includes soil texture, soil type and soil nutrients, provided by the county soil station or compiled through Henan TuRang Dili [START_REF] Wei | Henan TuRang Dili[END_REF]. Socio-economic data includes producing condition, economic condition and producing level. Crop production data includes crop varieties, planting region, annual crop area, yield per hectare, annual total output and multiple crop index, collected from the statistical yearbook of Henan Province.

Research Method

Crop Potential Output Evaluation Process. First, based on the process and the method of crop potential output evaluation [START_REF] Zhang | Evaluation System of Wheat Natural Potential Productivity at County Scale Based on GIS[END_REF][START_REF] Zhou | Knowledge Model and GIS-based Crop Potential Productivity Evaluation[END_REF], the paper collected and collated attribute data, such as Meteorology, soil and social production, and spatial data at 30 counties in Henan Province, to integrate space and attributes database. Second, the paper built the model components at all levels based on crop potential output model about light, temperature, water and soil and realized the model system of crop potential output. Finally, the paper quantitatively calculated crop potential output through mechanism model. temperature, climate and soil was calculated according to reference [START_REF] Huang | Natural Conditions and Crop Production-Photosynthetic Potential[END_REF][START_REF]Agricultural Climate Resources in China[END_REF][START_REF] Mao | Estimation of Potential Productivities of Main Crops in the Coastal Area of Fujian Province[END_REF][START_REF] Shao | A Study on the Agricultural Potential Land Productivity in the Northwestern Shandong[END_REF][START_REF] Wang | A Study on the Calculating Method of the Soil Effective Coefficient in the Alluvial Plain[END_REF]. Similarly, Analysis model of crop potential output included natural and social resource analysis, and the analysis approaches included spatial analysis and time analysis.

Design of Model System

System Architecture

Crop potential output model system consisted of system tool, system data, potential calculation and potential evaluation. System tool was responsible for system data management and made up of data editing components and model components for generating meteorological data of previous and current years. System data included the input database and the output database. The input database was made up of regional data, meteorological data, soil data, varieties data and crop cultivation and harvest data, which provided data-driven function for computing model components of potential output. The output database was made up of potential coefficient data, total potential data and thematic evaluation data, which provided data-driven function for model verification and potential distribution evaluation model component.

Crop potential output computing component was made up of the standard parameter library of crop species, regional parameter library of crop species, metadata and model file library.

Driven by the input database, system generated a set of crop potential output model views. Driven by model management, models were imported and exported, and model parameters were adjusted according to local conditions. Driven by the suitable potential model, crop potential output coefficients and total potential output were calculated and exported into the output database. Driven by crop potential evaluation components, crop potential output thematic analysis and the credibility of potential models were realized. Fig. 1. shows the architecture of crop potential output model system. In crop model components, the sub-model should be refined as far as possible to build an autocephalous atom component. In addition, model components' integration and expansion should be considered with other agricultural production system, so the interface technology was used to highly abstract model components and build a unified data interface. Crop potential output model system was made up of various types of crop model components, such as crop species interface, potential computing interface, and potential evaluation interfaces. Design of Crop Potential Model Interface. According to the empirical models of crop potential output, system model components consisted of the interface and class of photosynthetic potential, temperature potential, climate potential, soil potential, and socio-economic potential. In view of photosynthesis potential associated with solar radiation, solar radiation object was designed as a attribute in photosynthetic potential class, and solar radiation class was also associated with photosynthetic potential class. Crop potential output interface was provided from crop potential output model system, and was responsible for calculating photosynthesis, temperature, climate, soil and socio-economic potential coefficient and total potential. Photosynthesis, temperature, climate, soil and society sub-interfaces and classes were derived from crop potential output interface. Five classes at all levels of the potential output, potential coefficient and the total amount were derived from the corresponding sub-interface.

Taking into account the relationship among models and the integration of different models at all levels, interface technology was used to design coupled models loosely in crop potential output model system. The container component of crop potential model system provided the potential model interface for different crop production management and dynamic function calls, imported all kinds of crop potential models, and retained the further capacity for expansion interfaces to integrate other agriculture information system. Taking wheat and maize production in Henan Province as example, the meteorological data, soil data and crop cultivation and harvest data of all cities in Henan Province were inputted into the model system, the potential yield of wheat and maize was quantitatively estimated through the calculating model subsystem, and the potential distribution of wheat and maize at all cities was qualitatively analyzed through the evaluation model subsystem, which provided the decision-making and technical support for building the core area of crop in Henan Province.

System Application

The system has been applied to crop production management in Henan Province. The system is running well, and effectively estimates and predicts crop potential output.

Conclusion

Crop potential output model system was built by using componentware technology based on GIS, and crop potential was calculated and quantitatively analyzed. The results show that the system has the characteristics of wide applicability and good independence, which provides ADM and technical support for construction of major grain-producing areas, crop production management and potential mining. The model system could be extended to other crop potential calculation and also combined with other software system, such as soil productivity evaluation system, agriculture fertilization expert system and crop production early warning system. It is necessary to strengthen the function of crop potential model management, improve the component management and enrich the model types of crop potential practically to increase productivity in the model system, such as crop growth simulation models, nutrient dynamics and balanced fertilization models and crop output benefit models.

Fig. 1 .

 1 Fig. 1. System architecture

Fig. 2 . 5 Fig. 2 .

 252 Fig. 2. Model component interface of crop potential output

Fig. 3 .

 3 Fig. 3. The integrated potential model components

Fig. 4 .

 4 shows the interface of crop potential calculation. Fig.5. shows the interface of crop potential analysis.

Fig. 4 .Fig. 5 .

 45 Fig. 4. The interface of crop potential calculation

Crop Potential Output Mechanism Models. The frequent methods of crop potential

  output included mechanism model and empirical model[START_REF] Bai | Status and Evaluations on Research of Crop Production Potential in China and Abroad[END_REF][START_REF] Xu | Crop Productivity Potential Model Applied in China[END_REF][START_REF] Gu | Reviews on Crop Productivity Potential Researches[END_REF] at present. The latter was complicated and had the disadvantage of more inputted parameters leading to be difficult to spread, but the former was simple and easy to popularize. So, the paper used mechanism model of light, temperature, water, soil and social-economic factors, designed crop potential output model components, and constructed a crop production potential model system. Crop potential output model included computing and analysis models of crop potential output. Computing model included natural and social resource calculation. Computing model of natural resource included solar radiation model, photosynthetic potential model, temperature potential model, climate potential model and soil potential model. Computing model of social resource was used to quantify social factors' contribution, such as producing condition, economic condition and producing level. The daily and total solar radiations were calculated during crop growth according to reference[START_REF] Zheng | MAIZESIM--A Model to Simulate Maize Growth and Development[END_REF]. The potential output of photosynthesis, Design and Implementation of Crop Potential Model System Based on GIS and Componentware Technology 3

*Corresponding author

Acknowledgments. This study is supported by "Eleventh Five-Year" national scientific and technological support and major project plan: "High-yield Crop Science and Technology Engineering" (Contract Number: 2006BAD02A07-4), specific industry research sponsored by ministry of agriculture (Contract Number: 20083028) and "863" plan(Contract Number: 2006AA10Z271). Sincerely thanks are also due to the National Engineering Research Center of Wheat for providing the data for the study.


Min Zhang
email: zhangm@shou.edu.cn

Huizhong Zhao

Zhiyou Zhong

Jianhua Chen

Zhenhua Che

Jiahua Lu

Le Yang

Determination of Thermal Conductivity of Aloe in the Cooling and Thawing Process

Keywords: Probe measurement system, Thermal conductivity, Temperature, Aloe

In this study, thermal conductivity of aloe was determined in the cooling process from 20℃ to -11℃, and in the thawing process from -11℃ to 20 ℃ using the tiny thermal probe method. The tiny thermal probe measurement system had the advantages of high accuracy, short test time, low temperature rise and little water removal and was found to give accurate and consistent experimental results. The results showed that the thermal conductivities increased with temperature over the freezing point. The thermal conductivities rapidly increased below the freezing point and it increased with the temperature decreasing. The thermal conductivities decreased with temperature below the thaw point. The thermal conductivities rapidly decreased over the thaw point and it decreased with temperature. The thermal conductivity of aloe in the cooling process was greater than that in the thawing process at the same temperature.

Introduction

Aloe belongs to perennial evergreen succulent herb. It has been paid more and more attentions for its many functions such as medical, facial beauty, food, ornamental [1]. Quality of the aloe products depends on its storage and processing. In freezing and thawing processes, one of the most important properties to estimate the time is the thermal conductivity [2]. Some investigators has reported that the experimental values of thermal conductivity of fruits and vegetables over years [START_REF] Sweat | Engineering properties of foods[END_REF], [START_REF] Rahman | Food properties handbook[END_REF], [START_REF] Liang | [END_REF], [START_REF] Saravacos | Transport properties of foods[END_REF], [START_REF] Ali | [END_REF], [8]. However, the thermal conductivity of aloe in the cooling and thawing process has never been reported in the literature.

In general, an extensive review of existing methods of measuring of thermal conductivity of food has been carried out by some researchers [9], [10], [11]. The thermal conductivity measurement methods can be classified into steady-state and unsteady-state methods [12]. The steady-state methods have the shortcoming of requiring a long time to equilibrate. Among various unsteady state methods, the line heat source thermal conductivity probe is recommended for most food applications because it is simple, fast, convenient, cheaper and suitable for small sample size [START_REF] Sweat | Engineering properties of foods[END_REF], [START_REF] Carson | Predicting the effective thermal conductivity of unfrozen, porous foods[END_REF]. In this work, an improved thermal conductivity probe system was used to determine the thermal conductivity of aloe in the cooling and thawing process. Advantages of this system are the short duration of the experiments, simplicity, high accuracy and relatively small sample requirement.

The objective of this work was to determine the thermal conductivity of aloe by a proposed probe measurement system in the cooling and thawing process.

MATERIALS AND METHODS

Materials

The aloe leaves grown better were obtained from local market at Yangpu district of Shanghai, China. The homogeneity tissue in the middle of aloe leaves were chosen to determine because the tissue of aloe was different at various place. Three groups of aloe leaves bodies were placed in a constant temperature cabinet (Shanghai Precision Instrument CO., LHS-100CL, ±0.5℃ precision) of temperature from -11℃ to 20 ℃ to ensure the initial temperature of sample was constant and uniform to facilitate the determination of thermal conductivity at different temperature conditions. Three replicates of each sample were used for each run and each run was repeated at least three times. The thermal conductivity values obtained were the average values of nine measurements under the same conditions.

Method

The probe measurement system which was based on the idealized non-steady state transient heat conduction model was proposed to determine the thermal conductivity of aloe tissue in the cooling process from 20℃ to -11℃, and in the thawing process from -11℃ to 20℃. The apparatus was shown schematically in Figure 1. The measurement system consisted of a designed thermal conductivity probe, DC power supply, multimeter (Keithley data acquisition), a constant temperature cabinet and controlled circuit system. A copper wire coated with a thin electrical insulation layer put inside the probe was used as heating element and measuring element in this experiment [14]. The probe was inserted into the measured aloe tissue which had been put into the constant temperature cabinet for four hours to keep a desired uniform temperature. An invariableness voltage was applied across the circuit system to cause the temperature rise of the copper wire inside the probe. This resulted in the temperature rise of probe tube and around aloe tissue in turn. The heat would transfer from the copper wire to the aloe tissue. As a result, the temperature of copper wire would change according to the correlation of copper resistance and its temperature. The weak output voltage signal from the electric bridge would be amplified and input the computer to be handled. And then the slope of output voltage and time of natural logarithm was obtained to calculate the thermal conductivity of aloe tissue according the Equation ( 1)

) d(ln ) d( 2 3 t V C R U b m ∆ - = λ (1) Where L R C π α 64 0 0
= was instrument constant of the probe (Ω/mKs), which only related with the probe material, length and had nothing with the heating power and testing temperature of the testing system [15]. 0 α was the copper electric resistance's temperature coefficient at 0℃(K -1 ).

was the electric resistance value of the copper electric resistance at 0℃(Ω). L was the effective length of the probe (m). △V was the output electric voltage of the electric circuit (V). t was heating time(s). The thermal conductivity probe apparatus had the merits of rapidity, accuracy, online measurement, and small sample requirement, which had been described by Zhang [16]. The apparatus was firstly calibrated with a sample of known thermal conductivity before testing thermal conductivity of aloe at different temperature. Here glycerin was chosen as the calibration sample for its higher viscosity. The thermal probe was inserted into the glycerin sample that filled the sample holder. The output voltage signal ∆V tested by the measurement system and was fed into a computer. And the curve of ∆V-lnt could be drawn. According to the least-squares method, the slope of d (∆V)/d (lnt) could be obtained. Then the instrument constant of the probe could be obtained by Equation (1). The measurement accuracy of the apparatus was checked by determining thermal conductivities of ultra pure water (resistivity 18.4 MΩ• cm) at temperatures ranging from -15℃ to 20℃. After the accuracy of the apparatus was tested, the thermal conductivities of aloe tissue in the cooling process were determined at desired temperatures from 20, 10, 5, 3, 1, -1, -3, -5, -7, -9℃ to -11℃. Then the thermal conductivities of aloe tissue in the thawing process were determined at desired temperatures from -9, -7, -5, -3, -1, 1, 3, 5, 10℃ to 20℃, respectively.

Results and discussions

Validation of measurement apparatus

In order to validate the proposed method and the experimental design, the standard samples of water and ice were tested from -15℃ to 20℃. Each sample was measured three times under the same conditions and the average value was calculated as thermal conductivity of the sample. An example of such profiles of output voltage was shown in Figure 2. The best fitting of the linear part of the output voltage-Ln (time) history was chosen optimizing the R 2 correlation coefficient, disregarding the initial and the final points. During the experiments, the maximum temperature rise of the heated probe was controlled within 2℃. The experimental results were shown in Figure 3 with comparison to the recommended values [START_REF] Chen | Thermophysical Property Measurement[END_REF]. The results showed the agreement between the measured and the recommended values. The maximum of relative error with reference values was 2.93% and the mean relative error was 1.51%. The accuracy of the measuring apparatus was satisfactorily accepted in this experiment.

Measured thermal conductivities of aloe in the cooling and thawing process

Using the calibrated probe measurement apparatus, the thermal conductivities of aloe tissue were determined in the cooling process from 20℃ to -11℃ and in the thawing process from -11℃ to 20℃. The tested thermal conductivities of aloe were showed in Figure 4. The thermal conductivity of normal aloe issue which was not subjected to the frozen injury was bigger than that of denatured aloe issue which had been subjected to frozen injury at the same temperature. The developing tendency of thermal conductivity of aloe in the cooling process and in the thawing process was shown as Figure 5 and Figure 6. In the cooling process, it showed that the thermal conductivities decreased with temperature dropping over the freezing point, and rapidly increased at the freezing point. The thermal conductivities increased with the temperature dropping below the freezing point. Because the water is the main component of aloe tissue, the thermal properties of aloe were similar to that of water. The thermal conductivity of water decreased with temperature dropping over the freezing. The thermal conductivity of water increased at the freezing point because of appearance of phase transition. Thermal conductivity of ice decreased with the temperature increasing. Variation of thermal conductivity of water within aloe tissue influenced the variation of the thermal conductivity of aloe. In the thawing process, it showed that the thermal conductivities decreased with the temperature increasing below the thaw point, which was similar to that of in the cooling process. But over the thaw point, the thermal conductivities decreased with the temperature increasing over the thaw point, which was different with that of in the cooling process. It was probably for the extreme dehydration of cytoplasm of aloe tissue. The protein structure and the membrane system in the aloe cell were damaged. It resulted in water loss of aloe tissue in the thawing process.

Conclusion

The proposed tiny heat probe test system allowed accurate and reliable measurement of thermal conductivity in a given range to be obtained, which was used to determine the thermal conductivities of aloe at different temperatures from -11℃ to 20℃. The results showed that the thermal conductivities increased with temperature over the freezing point. The thermal conductivities rapidly increased below the freezing point and it increased with the temperature decreasing. The thermal conductivities decreased with temperature below the thaw point. The thermal conductivities rapidly decreased over the thaw point and it decreased with temperature. The thermal conductivity of aloe in the cooling process was greater than that in the thawing process at the same temperature and gave an explanation from the changes in tissue microstructure. These studies could give help to the storage and processing, the engineering calculations in the course of freezing of aloe, and give foundations for the future study of the impact of frost on the aloe.

  conductivity of sample(W/mK). U was the electric voltage of power supply (V). R b was the initial electric resistance value of probe (Ω).

Fig. 1 .

 1 Fig. 1. Thermal conductivity measurement system.

Fig. 2 .

 2 Fig. 2. Output voltage and temperature rise profiles in the measurement.

Fig. 3 .

 3 Fig. 3. Comparison of measured and recommended values of pure water.

Fig. 4 .

 4 Fig. 4. Determined thermal conductivities of aloe.

Fig. 5 .

 5 Fig. 5. Determined thermal conductivities of aloe in the cooling process.

Fig. 6 .

 6 Fig.6. Determined thermal conductivities of aloe in the thawing process.

Acknowledgments. Funding for this research was provided by the Natural Science Foundation of China, Grant No 30771245 and Leading Academic Discipline Project of Shanghai Municipal Education Commission, Project No J50704.


Jingyu Liu

Yu-An Tan

Yuanzhang Li

Xuelan Zhang

Zexiang Zhou

A Method of Deduplication for Data Remote Backup

Keywords: Disaster Recovery, Deduplication, Hash, Duplicate Data

With the ever-growing amount of data, the network bandwidth requirement has increased greatly in Remote Backup System. The network bandwidth became the bottleneck of the whole system. The paper describes the Remote Backup System using Hash to identify and avoid sending duplicate data blocks between the Primary Node and the Secondary Node, thereby, to reduce the data replication network bandwidth, decrease overhead and improve network efficiency.

On both nodes, some extra storage spaces (the Hash Repositories) besides data disks are used to record the Hash for each data block on data disks. We extend the data replication protocol between the Primary Node and the Secondary Node. When the data, whose Hash exists in the Hash Repository, is duplication, the block address is transferred instead of the data, and that reduces network bandwidth requirement, saves synchronization time, and improves network efficiency.

INTRODUCTION

Today, the ever-growing volume and value of digital information have raised a critical and mounting demand for long-term data protection through large-scale and high-performance backup and archiving systems. The amount of data requiring protection continues to grow at approximately 60% per year [START_REF] Yang | DEBAR: A Scalable High-Performance De-duplication Storage System for Backup and Archiving[END_REF] . The massive storage requirement for data protection has presented a serious problem for data centers. Typically, data centers perform weekly full backups for weeks to months. Local hardware replication techniques can mask a significant number of failures and increase data availability. For example, RAID can protect against single disk-failure. Furthermore, certain raid levels even survive multiple simultaneous failure [START_REF] Garcia-Molina | Management of a remote backup copy for disaster recovery[END_REF], [START_REF] Polyzois | Evaluation of remote backup algorithms for transaction-processing systems[END_REF], [START_REF] Ellenberg | DRBD 8.0.x and beyond Shared-Disk semantics on a Shared-Nothing Cluster[END_REF] . However, local hardware replication techniques are inadequate for extensive failures or disasters, which may be caused by environmental hazards (power outage, earthquake, and fire), malicious acts or operator errors. To ensure continuous operation even in the presence of such failures, the secondary node (a backup copy of the primary node) is often maintained up-to-date at a remote geographical location and administered separately. When disaster strikes at the primary node, the secondary node takes over transaction processing. The geographic separation of the two copies reduces the likelihood of the backup also being affected by the disaster. Disaster Recovery is such technique.

Data Disaster Recovery is an important measurement to ensure the integrity and availability of computer systems. With the remote replication technology, an offsite independent backup for the local data is stored via the network [START_REF] Li | Data Deduplication Techniques[END_REF] . When the local node is damaged, the data can be recovered from a remote system immediately [START_REF] Ellenberg | DRBD 8.0.x and beyond Shared-Disk semantics on a Shared-Nothing Cluster[END_REF], [START_REF] Reisner | DRBD-Distributed Replicated Block Device[END_REF] .

At first, all data blocks on the disk of the local source server (the Primary Node) are duplicated to the remote target server (the Secondary Node) to complete the initial data synchronization. From then on, the data in the Primary Node changed is duplicated to the Secondary Node synchronously or asynchronously via the network [START_REF] Patterson | SnapMirror: file-system-based asynchronous mirroring for disaster recovery[END_REF] . If the data is transferred offsite over a wide area network, the network bandwidth requirement can be enormous [START_REF] Zhu | Avoiding the disk bottleneck in the Data Domain deduplication file system[END_REF] .

The Primary Node and the Secondary Node are usually deployed separately in two buildings that one is very far apart from the other, or even two cities. There are two ways to transfer the data packet between the nodes, one is then common IP network, the other is the Fibre Channel [START_REF] Tan | A high-throughput fibre channel data communication service[END_REF] . Because the private network is expensive, data replication between the Primary Node and the Secondary Node usually uses the common IP network [START_REF] Reisner | Drbd v8-replicated storage with shared disk semantics[END_REF] . When the updates are frequent, and the amount of data gets massive, the performance degrades and the backup data maybe lost because of the low network bandwidth and the latency. Some data blocks on the disk are duplication, for example, a file on disk may have multiple copies or different versions, and the great majority is same [START_REF] Bobbarjung | Improving duplicate elimination in storage systems[END_REF], [START_REF] Barreto | Efficient locally trackable deduplication in replicated systems[END_REF] . In the data disaster recovery system, all the data blocks of the file need to be transferred to the Secondary Node when the Primary Node creates a duplicate of the file or updates it. However, the Secondary Node already contains most sections of the data blocks, and the data blocks transferred through the network is duplication of the section of the Secondary Node [START_REF] Aref | Hashing by proximity to process duplicates in spatial databases[END_REF], [START_REF] Eltabakh | Duplicate Elimination in Space-partitioning Tree Indexes[END_REF], [START_REF] You | Deep Store: An Archival Storage System Architecture[END_REF] .

The paper describes the Remote Data Disaster Recovery System using Hash to identify and reduce the amount of data transmitted over the network, and at the same time, the technique assures the reliability and availability of the data, and reduces network bandwidth requirement.

The rest of this paper is organized as follows. Section 2 describes the architecture of Disaster Recovery System we used, highlighting the features important for performance. Section 3 discusses implementation of the deduplication method. Section 4 discussed the design and performance evaluation of the solution. Section 5 summarizes and describes future work.

ARCHITECTURE

The existing Disaster Recovery System duplicates the data between the Primary Node and the Secondary Node to maintain data consistency of the two nodes through the IP network. An extra storage space is used as a Hash Repository to record the Hash of each data block on the disk. The Hash Repositories of the Primary Node and the Secondary Node are keep consistency, and both of them update synchronously with the data blocks on the disks, as shown in Fig. 1. Generally, when the Primary Node receives the write request, it writes through the local disk immediately and sends the data packets to the Secondary Node. The Secondary node receives the data packets and writes through the disk. After it completes the event, it sends back ACK to the Primary Node, then the event is completed, as shown in Fig. 2 [START_REF] Reisner | Drbd v8-replicated storage with shared disk semantics[END_REF] .

Typically, the size of the data block is 4KB (4096 Bytes), and the Hash is 16 Bytes (128 bits) that is calculated according to MD5. The Hash Repository storages the Hash of each data block in sequence. Each data block takes 16 Bytes, 16/4096=1/256, so the storage space that Hash Repository takes is 1/256 of the storage space that data blocks take.

The architecture of the Hash Repository is shown in Fig. 3.

After the Primary Node receives write request to a data block (the Destination Block), it writes the data to the Destination Block, and calculates the Hash of data block to match with the Hash Repository.

If they do not match, the Primary Node transfer the data block to the Secondary Node and the Secondary Node writes it to the disk.

On the other hand, if they match, it means that the disk of the Primary Node has the same data. This block (the Source Block) is duplication data. It has been delivered to the Secondary Node during the previous initialization or data replication, and it also means that the Secondary Node's disk already contains data of the source block. In this case, the Primary Node needs to transfer the Source Block Address and the Destination Block Address to the Secondary Node Only. Then, the Secondary Node reads the data from the Source Block Address of its local disk and writes it to the Source Block Address.

When the transmission succeeds, both the Primary Node and the Secondary Node update their Hash Repositories.

IMPLEMENTATION

The Primary Node receives a write request to write the data A to the destination block PD_B. Correspondingly, the data A should also be written to the destination block SD_B(PD_B=SD_B) of the Secondary Node, as shown in Fig. Hash of the data A; 3) matches the Hash Repository; if it matches with the value in SH_A in the Hash Repository where the Hash of the data block PD_A is placed, it means that the date in PD_A is the same as data A. it is the duplication data. Similarly, the duplication data exists in the Secondary Node also. We suppose that the address is SD_A(SD_A=PD_A). Therefore, when the two nodes synchronize, the data A need not to be transferred. 4) only transfers the source address (PD_A) and the destination address (PD_B) to the Secondary Node; 5) the Secondary Node gets the network package and extracts the addresses from it; 6) reads the data from the source address; 7) writes the data to the destination address; lastly, 8) updates the Hash Repository. For example: in the existing Disaster Recovery Systems, the file F which size is 8MB (8192KB) is replicated from the A to B. In the 64-bit addressing file system, each data block size is 4KB. The address of each block consists of 8 Bytes (64bit) component, so the file F contains a total of 8192KB/4KB = 2048 data blocks. A total amount of data of synchronization between the Primary Node and the Secondary Node is all the data blocks and their destination addresses, 2048×(4KB+8B) = 8208KB. With the method this paper provides, only the source address, the destination address and the identification information (the size is 1B/block) are transferred because the file F already exists in the Secondary Node. A total amount of data to be transferred is 2048×(8B×2+1B)=34KB, so the data to be transferred is 34KB/8208KB≈1/241≈0.415% of the former, and that significantly reduces the network bandwidth requirement for data transmission overhead.

When the Primary Node is malfunction, the Secondary Node can start the remote service system to take over the Primary Node service. Before the Primary Node restored, the Secondary Node's change is written to the disk and the Hash Repository updates at the same time, but this update cannot be synchronized to the Primary Node until it restores. Similarly, When the Secondary Node is malfunction, the Primary Node's update cannot be synchronized to the Secondary Node. Date resynchronize-tion must be performed after the Secondary Node is restored.

Comparing Hash Repositories of the two nodes, we can get the collection of the changed data. The normal node sends these data blocks to the node which used to be malfunction to maintain the consistency between two nodes. During the transfer process, we can use the deduplication technology also.

Each data block size is 4KB, and the Hash size is 16 Bytes, so the Hash Repository size is 1/256 of the data disk size. The Address of data block's Hash in the Hash Repository can be calculated with the following formula: Hash Address=Block Address×16 Similarly, find the Address of Hash in the Hash Repository, the block address can be calculated with the following formula:

Block Address= Hash Address/16 When the Primary Node receives write request, it does as follows:

A. For the Primary Node 1) Write the data to the disk.

2) For all data block needed to be written, perform the following steps 3) to 5).

3) Calculate the Hash of each data block. 4) Match the Hash in the Hash Repository.

• If it does not match, the Primary Node constructs the network packet, the structure is shown in Fig. 5(a), and transfers it to the Secondary Node. The "ID" in the package is "0" which means the package includes data and the destination address. • If it matches, the Primary Node calculates the source address with the formula above, constructs the network package, the structure shown as Fig. 5(b), and transfers it to the Secondary Node. The "ID" in the package is "1" which means the package includes the source address and the destination address. 2) According to the ID in the network, implement as follows:

• if ID=0, extract the destination block address and the data block from the network package, and write them to the data disk. • if ID=1, extract both the destination block address and the source block address from the network package. Read the source data block from the source block address, and write them to the destination block address. 3) Calculate the Hash of data blocks. 4) Update the Hash of the destination data block in the Hash Repository.

EVALUATION

To evaluate the performance of our solution, we build an experimental system under Linux and compare it with the existing Disaster Recovery System of our lab. To compare fairly, we try our best to create similar experiment environment. In the experiment, there are two machines with the same configurations. We divided the four computers into two groups. One Group is for the existing Disaster Recovery system, the other is for the system with our solution. Each group has two computers. One is the Primary Node and the other is the Secondary Node. The machine's CPU is Pentium(R) Dual-Core. We deployed 2.0GB RAM in the machine. The disk of the machine is ST3500418AS (500GB) and NIC is Atheros AR8132. The OS we used is Fedora 10 (Linux Kernel 2.6.27). The two nodes are connected with Fast Ethernet.

In the experiment, we designed two projects: one is that the transmitted data is divided into blocks which sizes are 4KB, the other is that the transmitted data is divide into blocks which sizes are 2KB. In these experiments, we employ a software packet to simulate to normal operation and record the amount of transmitted data.

The results of the experiment are shown in Fig 6 . The figure shows that amount of transmitted in the new system is only 22.32% maximum and 12.31% minimum of the former system while divided the date into 4K blocks and it is reach approximately 19% over a period of time (about 20 days). It is only 20.15% maximum and 12.30% minimum of the former system while divided the date into 2K blocks, and it is reach approximately 15% over a period of time. That shows the smaller size of the block can get the higher performance of deduplication. However, there is a question: the smaller size of the block means the greater workload of CPU. It lowered system's performance.

CONCLUSION

According to the data replication protocol which is extended between the Primary Node and the Secondary Node, When the Primary Node receives a write request to the Destination Block, the Primary Node identifies if it is the duplicate data block according to the Hash. While the data block to be written is the duplication data block, it will not be the data block to be transferred to the Secondary Node but the block addresses which includes the Source Block Address and the Destination Block Address. The Secondary Node reads the data from the Source Block address of it's local disk and writes to the Destination Block Address. Therefore, when the data is duplication, the Block address is transferred To judge the duplication data makes the CPU's workload increased and this may make the CPU the bottleneck of the system. Future work includes designing a new method to reduce the CPU's workload to improve the system's performance.

  Figure 2. Write Simultaneously

Figure 4 .

 4 Figure 4. Example of Replication

Figure 6 .

 6 Figure 6. the Rate of Deduplication

  DBA: the Destination Block Address SBA: the Source Block Address

Figure 5 .

 5 Figure 5. Network Package Architecture