An Adaptive Transfer Algorithm in GDSS

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1 An Adaptive Transfer Algorithm in GDSS Hai Jin, Xiangshan Guan, Chao Xie and Qingchun Wang Key Laboratory for Cluster and Grid Computing, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, , Hubei, PRChina Abstract The efficiency of transferring a pack of small-sized files or a large-sized file is very low in FTP based file transfer system To solve this problem, we have studied the optimized method of different types of file transfer and presented an Adaptive Transfer Algorithm Based on this algorithm, we design and implement the file transfer subsystem of Global Distributed Storage System [5] 1 Introduction Data-intensive, high-performance computing [3] applications require the efficient management and transfer of teras or petas of data in wide-area, distributed computing environments [6] As a promising approach, data grids [1] [2] [9] [10] are becoming increasingly important for sharing large data collections, archiving and disseminating As we know, data grids involve frequent data transfer in wide-area network, one of the problems facing which is that transferring small files and large files is very boring and time-consuming It is necessary to devise a mechanism to achieve effective and efficient data transferring world-wide However, although some grid data transfer protocols have been studied extensively, such as GridFTP [3] [7] and GASS (Global Access to Secondary Storage) [4] [13], none of them address this problem In this paper, we present an adaptive file transfer algorithm to achieve efficient transfer of both large files and a batch of small files, which we have implemented in our GDSS prototype of data grid In this algorithm, we adopt the socket multiplexing algorithm to optimize transfer of small files, as well as we use the slicing mechanism to optimize transfer of large files, all of which will be discussed below in details The rest of the paper is organized as follows In the section that follows, we discuss the existing FTP (File Transfer Protocol) [11] [12] method of file transfer In Section 3, the adaptive file transfer algorithm is described in detail Section 4 describes how the GDSS prototype implements the adaptive file transfer algorithm Section 5 presents performance evaluation of our implementation Finally, Section 6 gives conclusions and presents directions for future work 2 Related Work The file transfer mechanisms adopted in GFTP and GASS are similar to those adopted in traditional FTP, which we will discuss here As we know, two TCP ions are used during file transferring in FTP First, a control ion is established to transfer control command and response; then according to the information provided by control ion, a new data ion is established to transfer each file between clients and servers T T In this paper, we use to denote the time consumed to establish and close a ion, T to denote the time consumed to transfer data Under normal network conditions, is a transfer

2 2 constant whatever the size of the file is We define T as the practical time consumed to transfer 1 and f as the file transfer rate Let T indicate the time of transferring n files of the size of m s through standard FTP, then: T = n ( T + Ttransfer ) = n ( T + m T ) f = n m = n m = m T n ( T + m T ) T + m T T ( ) If m is very large, then might be relatively trivial and could be neglected, f 1 T resulting T If m is small, then might be relatively large and could not be neglected, f << 1 T resulting From what we have discussed above, we can found out that the low data transfer rate during FTP transferring of small files is because of the greater proportion of establishing and closing the ions, which would explain why the time consumed to transfer 1000 files of the size of 1KB may much longer than the time consumed to transfer only one file of the size of 1MB A common policy used to address this problem is to pack many small files into a data package and transfer the data package as a whole Although this policy can bring some extent of performance of file transfer, it requires users to select the files to be packed, which is inconvenient and even troublesome It is not applicable when transfer a lot of files intermixed with several large files 3 Adaptive Transfer Algorithm In our practice, we have proposed the adaptive file transfer algorithm, especially, to transfer data in grid environment 31 Socket Multiplexing Algorithm and performance analysis In order to ensure the performance of transferring a batch of small files, we present a new transferring algorithm called Socket Multiplexing Algorithm Using this algorithm, all these small files share a single data channel Both file information and data were transferred through this channel Only during establishing and closing the ion, servers and clients need to communicate with each other By this means, the overhead brings by establishing and closing multiple data ions will be avoided Definition 1We use S to describe the server information, which is presented by a 5-tuple (IP, Port, Type, State, Socket) IP, Port, Type, State and Socket are defined as follows: IP, the IP address of the storage server; Port, the port used by the ion; Type, the type of the storage server, such as HTTP and FTP; State, the current status of the storage server, 1 stands for available, 0 stands for failed; Socket, the socket established to transfer data between clients and servers Definition 2We define Connection pool D as the set of all existing S, that D = S, S, S,, S n is, Definition 3We use ST to describe the subtask information, which is presented by a 6-tuple (URL, Size, Start, End, Mode) URL, Size, Start, End, and Mode are defined as follows: URL, the URL of the file which expected to be transferred; Size, the size of subtask; Start, the start position of the file; End, the end position of the file; Mode, the mode of file transferring

3 3 Definition 4We define T as the set of subtask which belongs to Transfer Task, that is, T = ST1, ST2, ST3,, STn Where n represent the count of subtask that Transfer Task contains CT = T,,,, Definition 5 We define CT as the set of Transfer Task, that is 1 T2 T3 Tn Algorithm 1 Proc CR(CT) Proc CR(CT) while (CT is not null) do Select one task Ti and remove it from CT; Get the storage server information fromt i ; if Si D Si ( socket) = Connect( Si ( IP), Si ( Port) ) ; /* to storage server and return the socket*/ Add(D, S i ); /*Add storage server information to the ion pool*/ else Get S i from D; Transfer( T Let T indicate the time of transferring n files of the size of m through Socket Multiplexing Algorithm, then: T = T + n T = T + n m T transfer and the transfer rate f = n m n m = T ( T + n m T ) T << n m While n is extraordinarily large, f 1 T T Because is approximately ten times of thus f 10 f 32 Adaptive Transfer Algorithm S, ); /* transfer files Using ( socket) i i T, then: m T S i */ when transferring small files, As we know, files we transferred usually consist of small files and big files For example, it is a typical request to transfer a directory which contains hundreds of small files intermixing with several big files Beside the algorithm discussed in Section 31 to enhance performance of transferring small files, we slice big files and transfer those slices parallelized The benefit of slicing big files is not only enhancing

4 4 the transferring speed but also balancing the load of various storage resources Base on this analysis, we presented the Adaptive Transfer Algorithm The main function of the algorithm is to sort the files, which users expected to transfer, according to the size and put small files to queues which correspond to data channels While we slice big files in order to transfer those files parallelized Depending on the distribution and the size of slices, we determine the degree of parallel transfer Usually, a thread is allocated to transfer a slice When a slice is big enough, then this slice will be sliced again The method of slicing is halving a slice into two smaller parts and each part is the half of the origin slice in size After this, we reorder the slices by their size and re-judge the need of further slicing If it needs, then repeat the steps that described above At the same time, the count of threads for each task must below the maximum degree of parallel The goal of this mechanism is to protect severs and clients from overloading When the count of subtasks exceed the maximum degree of parallel, we transfer slices equal to the degree of parallel and remain slices be left to wait for the next scheduling Definition 6 MAX_PARALLEL stands for the maximum degree of parallel; MAX_FILE stands for the threshold of big files We consider the file whose size is larger than this value as a big file and this file will be sliced; MIN_FILE stands for the threshold of small files We consider the file whose size is less than this value as a small file Algorithm 2 Proc D(T) Proc D(T) while(the count of subtasks <MAX_PARALLEL) do /*while the count of slices less than the maximum degree of parallel, then sorting subtasks*/ ST=Max(T); /*Load the maximum subtask*/ if(st(size)< MAX_FILE) break; /*if the maximum slice is not the defined large slice, then break*/ else Div( ST, ST a, ST b );/*halve ST into STa and STb */ Del(T,ST); /*Delete the maximum task from T*/ Merge(T, ST a, ST b ) /*Add the new tasks to T */ if(the count of subtasks in T > MAX_PARALLEL) Transfer(T,MAX_PARALLEL); /*if the count of slices is larger than the maximum degree of parallel, then select subtasks to transfer The count of selected subtasks equals to MAX_PARALLEL The subtasks that remained will enter the scheduling next time*/ else Transfer(T) /*if the count of slices equals to or below the maximum degree of parallel, then transfer this task*/ Figure 1 shows the schematic plan of the Adaptive Transfer Algorithm

5 5 4 Prototype Implementation Figure 1 Schematic plan of the Adaptive Transfer Algorithm 41 GDSS Architecture As discussed above, our access model is based on the GDSS framework The framework is shown in Figure 2 Figure 2 GDSS architecture The GDSS introduces a new component, called Storage Service Point (SSP), to play as an interface for users to access the storage system There are many decentralized SSPs in GDSS, independent of each of the domains SSPs can join in system dynamically to meet the requirement of expansibility GNS (Global Name Server) work as a metadata server, with responsibility for the management of metadata User information and access control information of data are kept in GNS RM (Resource Manager) administrate requires for resources and resource scheduling Additionally, it implements transparent duplicating strategies, which helps to achieve high reliability and load-balance CA (Certification and Authentication Server) take charge of certificate management and digital signature, ensuring the security of the system SA (Storage Agent) screens heterogeneity of underlying storage resources and provides the uniform data access interfaces to users Moreover, SA can select data transmission mode automatically, in order to achieve efficient data transmission 42 Implementation of transfer subsystem of GDSS In the transfer subsystem of GDSS, we have used algorithms described in Section 3 First creating the subtask using Adaptive Algorithm, then encapsulated each transfer task to TTDO(Transfer Task Description Object) After this, these TTDOs will be putted to the task queue On the other hand, data transferring layer obtain these objects and executing data transferring In client machine, each small transfer task queue corresponding to a data channel In server, server process read file information from this data channel, obtain file name, file size etc Then receive file data from data channel according to this information

6 6 Figure3 shows the style of data channel Figure 3 The style of data channel Figure 4 shows the operation flow of transfer subsystem in GDSS First, user commit an operation request, then according to the operation type and metadata, transfer system create transfer tasks and put these tasks into task queue Transfer controller fetch transfer tasks from task queue and submit it to lower data transfer layer to complete data transfer During the transfer process, the status of transfer will be feedback to the transfer task We use this information to monitoring the transfer process When transfer completed, transfer controller confirm the usage of resource then modify metadata 5 Performance Evaluations 51 Testing Platform Figure 4 Operation flow of transfer subsystem in GDSS This system is tested on an environment which has a cluster of 16 nodes The configuration of each node is Xeno 1G CPU, 512M memory, 40G disk and all nodes ed with 100M Ethernet Each node runs Red Hat Linux 73(kernel version 249) Testing servers and clients of GDSS are all these cluster nodes 51 Results and Analysis Figure 5 shows comparisons between the traditional FTP server and Socket Multiplexing when transferring a batch of small files In the experiment, 100 files of same size are transferred, and the average transferring time is obtained Figure 5 Comparisons between Socket Multiplexing Algorithm and traditional FTP when transferring a batch of small files

7 7 Figure 6 shows comparisons between FTP and GDSS when transferring large files Because of the network bandwidth and the processing limitation of clients, the transferring bandwidths of servers were limited to 1500KB/s To simplify the implementation of prototype system, we slice files according to its size That is, no slicing if the file size is between 50~100M, 2 slices if the file size between 100~200M and etc Figure 6 Comparisons between GDSS and Traditional FTP when transferring large files Figure 5 shows that the time of transferring a batch of small files (whose size is less that 500K) changed slightly using standard FTP Frequent ions between servers and clients can explain this phenomenon When the size of files increasing from 500K, transfer rate rise gradually The transfer rate of Socket Multiplexing Algorithm is 1~8 times as fast as that of standard FTP Figure 5 also shows that the average transfer time of transferring files whose size is less than 500B is larger than that of transferring files whose size is between 1K~100K Reasons of this phenomenon are redundant file information and frequent file operations (such as open and close) It can be predicted that performance improvement is more remarkable as network latency increasing Figure 6 shows that as file size increasing, the transfer rate increases and the transfer rate using GDSS is much faster than that of standard FTP 5 Conclusions and Future Work The paper presents the Socket Multiplexing Algorithm and an Adaptive Transfer Algorithm which tries to solve the slow transfer problem of a batch of small files and large files on the Internet The implementation of prototype is also described in this paper The test results show significant improvement in transfer rate for a batch of small files and large files when Adaptive Transfer Algorithm is used as compared to traditional FTP, however much work remains to be done Several issues such as the selection of degree of parallel, slicing policy, assignment of slices and prototype improvement of algorithm are some of the intended future work The impact of different approaches used to slice files is to be studied in detail in future 6 References A Chervenak, I Foster, C Kesselman, C Salisbury, and S Tuecke, "The Data Grid: Towards an Architecture for the Distributed Management and Analysis of Large Scientific Data Sets," J Network and Computer Applications, pp , 2001 Arcot Rajasekar and Arun Jagatheesan "Data grid management systems" Proceedings of the 2003 ACM SIGMOD international conference on on Management of data, 2003 A L Chervenak, C Kesselman, D Quesnel, I Foster, J Bester, J Bresnahan, S Meder, S Tuecke, VNefedova and W Allcock "Secure, Efficient Data Transport and Replica Management for High-Performance Data-Intensive Computing," presented at Mass Storage Conference, 2001

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