Abstract:
For the transfer of bulk data between a client computer system and at least one server computer system, throughput-relevant information is gathered from system components involved in the transfer. The information gathered is passed to a knowledge base that holds algorithms and data on relations and combinations of throughput-relevant information. By means of the knowledge base a set of performance parameters is generated that are effective to achieve a maximum data throughput. The determined set of performance parameters is used for a setup and configuration of a data transfer controller that controls the system complex for the transfer a maximum of data within a minimum amount of time and to reduce the expenditure for the system setup and configuration before the transfer is started. The data transfer may be part of backup and restore operations of bulk data.

Description:
BACKGROUND OF THE INVENTION  
         [0001]    1. Field of the Invention  
           [0002]    The invention relates to the performance optimizing of bulk data transfer between computer systems each having storage devices able to store large amounts of data.  
           [0003]    2. Decsription of the Related Art  
           [0004]    Complex computer systems store and process large amounts of data which often are of vital importance to an enterprise. In such environment a regular data backup is an important concept to ensure the system integrity and to allow fast system regeneration in case of a system dropout. A data backup operation requires movement of huge amounts of data from a source location, which may be a set of system disk storage devices, to a data storage pool, which may be a set of tape storage devices or again disk storage devices. The amount of data to be transferred extends from some kilobytes up to many terabytes. The transfer of such bulk data may involve a data transport through local or remote network connections. It is controlled by a backup control tool operable on various software platforms including AIX, Solaris, HP-UX, Windows NT, Linux and others. The backup control tool is responsible for transferring the data from a source location to a target storage pool.  
           [0005]    Bulk data transfer operations require a considerable share of system workload, which only in some cases can be shifted to hours of a day where the regular system workload is low. If network connections are involved, the data transfer also takes a large amount network bandwidth and connect time. It is therefore a demand of bulk data transfer operations to move a maximum of amount of data within a minimum amount of time. Furthermore, there is also an interest to reduce the expenditure required to adjust the system parameters for an effective bulk data transfer operation.  
           [0006]    U.S. Pat. No. 5,778,395 discloses a system for backing up files from disk volumes on multiple nodes of a computer network. In this system duplicate files or portions thereof are identified across the nodes to make sure that only a single copy of the contents of duplicate files is stored in the backup storage. The backup operations are restricted to those files which have been changed since the last backup operation. In addition, differences between a file and its version that was the subject of the previous backup operation are determined and only the changes are written on the backup storage means. These measures aim to reduce the amount of data which are the subject of the backup storing in order to reduce the amount of backup storage and the amount of network bandwidth required for performing the backup. This system requires a considerable processing expenditure in advance of the actual data transfer and storing operation.  
         SUMMARY OF THE INVENTION  
         [0007]    It is an object of the invention to increase the data throughput in performing bulk data transfer operations.  
           [0008]    Furthermore, it is an object of the invention to reduce the period of time during which a complex computer system has to perform bulk data transfer operations including backup and restore operations.  
           [0009]    It is also an object of the invention to free the administrator of a computer system complex from an empirical setup of system parameters in order to increase the performance of data backup and restore operations.  
           [0010]    According to the invention, as defined in the claims, for the transfer of bulk data between computer systems, for example, between a client computer system and a server computer system, throughput-relevant information is gathered from system components involved in the transfer. The throughput-relevant information is passed to a knowledge base which holds algorithms and data on relations and combinations of throughput-relevant information. By means of the knowledge base a set of performance parameters is generated which are effective to achieve a maximum data throughput. The determined set of performance parameters may be used to set up and configure the system complex for performing the data transfer, which may be part of a backup or restore operation for bulk data.  
           [0011]    It is one aspect of the invention to extend the gathering of throughput-relevant information to a network which is used to connect the client to the server computer system and to all servers used. On the basis of the information gathered the performance of the client and its subsystems, the performance of the network and the performance of the servers are evaluated. The set of performance parameters determined preferably includes the usage of data compression, the multiplex factor to be applied and the number of sessions to be used.  
           [0012]    The invention allows transfer of a maximum of data within a minimum amount of time and reduction of the expenditure for the system setup and configuration before the transfer is started. 
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0013]    [0013]FIG. 1 is a general block diagram of a computer system comprising a data transfer performance optimizer according to the invention;  
         [0014]    [0014]FIG. 2 is a block diagram the system components involved in the operation of the data transfer performance optimizer of FIG. 1;  
         [0015]    [0015]FIG. 3 is block diagram of an implementation of the backup/restore performance optimizer according to the invention;  
         [0016]    [0016]FIG. 4 is schematic flow diagram of a method according to the invention; and  
         [0017]    [0017]FIG. 5 is modified implementation of the invention. 
     
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS  
       [0018]    [0018]FIG. 1 shows a schematic block diagram of a computer system in which an embodiment of the invention is implemented. The computer system consists of a client computer  10  and a storage server  12  which are connected with each other through a network  13 . The storage server  12  receives bulk data from the client  10  to store it in a mass storage  14 . The storage server  12  also transfers bulk data from storage  14  to the client  10 . The data transfers take place under the control of a transfer controller  15 .  
         [0019]    A performance optimizer  16  is connected to the transfer controller  15  to increase the throughput in the transfer of huge amounts of data from the storage of the client  10  to mass storage  14  and vice versa. The controller  16  comprises an information gatherer  17  and a knowledge base  18 . The information gatherer  17  serves for collecting system parameters of both the client  10  and the storage server  12  in advance of a data transfer operation. The knowledge base  17  evaluates the system parameters and generates performance parameters which are used to tune the controller  16  for a high-performance transfer of data from the client  10  to the mass storage  14  or from the mass storage  14  to the client  10 . The controller  15 , the information gatherer  17  and the knowledge base  18  are software components which run on the client system  10  and in part also on the storage server  12 .  
         [0020]    [0020]FIG. 2 shows the system components which contribute to the information gathering. It shows further the relationship among those components, the type of parameters gathered and the type of performance parameters generated by knowledge base  18 . First, the information gatherer  17  has to determine type and version of the operating system  20  which is used by the client computers  10 . Through the operating system parameters of processors  21  are gathered including the number of processors contained in the client  10 , their processing power and the number of threads they are able to run in parallel. Furthermore, an input/output subsystem  22  provides throughput parameters including the maximal read rate and the maximal write rate. Connected therewith are input/output controllers  23  which provide parameters on their maximal data throughput and on the number of supported input/output devices such as magnetic disk storage devices. A network subsystem  24  provides parameters on its overall bandwidth and also provides parameters of network connectors  25  including their bandwidth and the network type used. A bus subsystem  26  which supports the input/output subsystem  22  and the network subsystem  24  provides parameters on the maximal throughput of the buses and the number of devices attached to the buses such as local network connectors, intranet or Internet connectors etc. Devices of this type may consume a substantial amount of processor power which then is not available for performing the transfer operations. Further system components which provide throughput parameters are the data sources  27 , i.e. the storage volumes of the client  10  such as magnetic disk drives, and the data files  28  stored in the data sources  27 . The parameters provided by the data sources  27  include the maximal read rate and the write rate, and the sources parameters provided by the data sources  27  include the number, size and distribution of the files to be transferred.  
         [0021]    Gathering of Throughput-relevant Information  
         [0022]    The information gathering extends to system information including parameters which are relevant for the data throughput of a data transfer operation. Relevant are parameters of the input/output subsystem comprising data buses, input/output controllers and the disk storage devices. Relevant are also parameters of the files which are subject of the transfer and parameters of the client and server computer and of the network connecting both.  
         [0023]    In particular, for the input/output subsystem the information gathered includes the number of I/O buses available, the throughput per bus, the number of controllers per bus, the number of I/O controllers available, the throughput per controller, the number of disk storage devices per controller and the throughput per disk. The information gathered on the file system includes information on the distribution of the files over the disk storage devices, the size of the files, the kind of the files such as binary, ASCII. The information gathered on the computer systems includes the number of processors, the computing power of the processors, and the processes or tasks or threads the system is able to run in parallel. In addition, for the computer systems operating as storage servers the information gathered includes the number of processors involved, the kind of storage devices installed such as tape storage devices, disk storage devices, optical storage devices etc., the number of tape drives, the throughput per tape drive, the number of tape drives per controller and the number of controllers per bus. The information gathered on the network includes the number of network connectors, the kind of the connectors used such as Fibre Channel, Token Ring, Ethernet, Sequential Area Network, the throughput per connector and which storage server is reachable through which connector.  
         [0024]    Information Generated by Means of the Knowledge Base  
         [0025]    The information gathered allows the knowledge base to generate information which is used to setup and configure the system complex for a maximal data throughput during a transfer operation. The object of the process is to generate parameters which are suitable to optimize the overall data throughput in GB/h (Gigabyte per hour) of end-to-end transfer operations.  
         [0026]    Among the information to be generated is the maximum number of processes or threads to be used by the transfer operation. It depends on the number of processors involved, on the processing power of the system same question and on other processes running on the system. A system slow down is not acceptable if some performance relevant application are running on the same system.  
         [0027]    Other information to be generated includes the use of compression, the type of compression to be used, and the phase of operations when compression is applied, i.e. before or after the multiplexing. The information depends on the overall network throughput, on the processing power of the system and on other processes running on the system.  
         [0028]    Furthermore, by means of the knowledge base it has to be decided whether a sort file operation is required in advance of a data transfer operation which depends on the gathered information about the disk storage subsystem and the distribution of the files over the disk storage devices. By a file sort to different disks the performance of the subsequent transfer of the files may be substantially increased. Furthermore, it has to be decided whether multiplexing has to be used. This depends on a response to the questions whether there are network connections which are faster than the read rate of single disk storage devices, and how many storage servers are available, how many tape drives are available and what is the throughput of the tape drives.  
         [0029]    By means of the knowledge base it has also to be determined whether parallel sessions have to be used and how many of them. This depends on the throughput of the network connections and further on a response to the questions of how many storage servers are available, how many tape drives per storage server are available, what is the throughput of the tape drives, how many disk storage devices are attached to which disk controller and how are the files distributed over the disk storage devices.  
         [0030]    [0030]FIG. 3 shows a block diagram of an implementation of the invention for performing data backup and restore operations. The computer complex shown in FIG. 3 comprises a host computer system  30  which operates as a client and a host computer system  31  which operates as a storage server. Both host computer systems are connected with each other through a network  32 . The host  30  contains at least one processor  33  and a memory  34 . The host  30  further contains a first data bus  35  to which a number of input/output controllers  36  are connected. In FIG. 3 these I/O controllers are SCSI controllers. Other types of I/O controllers may be used instead. Each of the I/O controllers  36  support a number of disks  37  which may be well known magnetic disk storage devices. The host  30  further contains a second data bus  38  to which a number of controllers  39  are connected. At least one of the controllers  39  supports a network connector to the network  32 .  
         [0031]    The host  31  contains at least one processor  41  and a memory  42 . The host  31  further contains a first data bus  43  to which a number of controllers  45  are connected. At least one of the controllers  45  supports a network connector to the network  32 . The host  31  further contains a second data bus  44  to which a number of input/output controllers  46  are connected. In FIG. 3 these controllers are SCSI controller but other types of controllers I/O controllers may be used instead. At least some of the input/output controllers  46  support a number of high volume storage devices  47  which may be well known magnetic tape drives, disks of optical storage devices.  
         [0032]    A backup-restore controller  51  serves to perform a backup of data from the disks  37  to the tapes  47  and to transfer data from the tapes  47  to the disks  37  during a restore operation. The backup-restore controller  51  runs on the host  30  as application program. A first information gatherer  52  is used to collect throughput-relevant parameter information from the components of the host  30 . These components include the processors  33 , the memory  34 , the buses  35  and  38 , SCSI controllers  36 , the disks  37  and the controllers  39 . The first information gatherer  52  runs on the host  30  as application program.  
         [0033]    A second information gatherer  53  is used to collect throughput-relevant parameter data from the components of the host  31  and from the network  33 . These components include the processors  41 , the memory  42 , buses  43  and  44 , the controllers  45 , the network connector attached to one of the controllers  45 , the SCSI controllers  46  and the tape drives  47 . The second information gatherer  53  runs on the host  31  as an application program.  
         [0034]    The information gatherers  52  and  53  supply the collected data to a knowledge base  54  which generates performance parameters as will be described subsequently. The performance parameters will be used by the backup-restore controller  51  to increase the data throughput during the backup of data from the disks  37  to the tapes  47  and during the restore operations by a transfer of data from the tapes  47  to the disks  37 .  
         [0035]    [0035]FIG. 4 shows an example of the method according to the invention comprising the steps of information gathering, evaluating the gathered information, calculating the overall performance and determination of performance parameters. In step  61  throughput-relevant information is gathered from the client which may be the host  30  of FIG. 3. Step  62  collects throughput-relevant information of the network which may be the network  32  of FIG. 3. In step  63  throughput-relevant information is gathered from the storage server which may be the host  31  of FIG. 3. Step  64  passes the gathered information to the knowledge base for being used in the subsequent evaluations. Step  65  performs an evaluation of the I/O performance by using the information data gathered in step  61 . Step  66  performs an evaluation of the network performance by using the information data gathered in step  62 . Step  67  performs an evaluation of the storage server performance by using the information data gathered in step  63 . In step  68  the overall performance of the client is calculated to determine a maximum of data throughput. Step  69  determines the performance parameters of the system. This step includes the determination whether a data compression has to be used for the data subject of the backup operation. Step  69  also determines the multiplex factor which indicates the number of files that can be written in parallel to the tape drives attached to the storage server. Furthermore, step  69  determines the number of sessions required for a backup or restore operation which is the number data streams to be written to one tape drive. In step  70  the performance parameters determined in step  69  are represented to allow an optimized setup and configuration of the system complex by adjusting the corresponding parameters in the backup-restore controller, for example, the controller  51 . Instead, the settings can be performed automatically by using the results of the step  69  directly as input data of the backup-restore controller.  
         [0036]    The method steps described by reference to FIG. 4 may be implemented in a program product which is stored and distributed on a machine readable carrier or transmitted through a digital data network such as the Internet.  
         [0037]    [0037]FIG. 5 relates to a system configuration which deviates from that one shown in FIG. 3. This modified configuration is a database-oriented configuration in which the performance optimization does not extend to the input and output subsystem. Instead, the data subject of the backup operation come directly from a database of a client host  76  which apart from the I/O subsystem corresponds to the host  30  in FIG. 3. The host  76  is connected via data channel  77  to a storage server  78  which corresponds to the host  31  in FIG. 3. The host  76  comprises a database  80  which resides on a number of disk storage devices and which is administrated and operated by a database management system (DBMS)  82 . A backup-restore controller  84  which corresponds to the controller  51  in FIG. 3 is installed as a shared library or as a Dynamic Link Library (DLL) in the database  82 . Under the control of the backup-restore controller  84  the data subject of the backup operation are transferred from the database  80  via the DBMS  82  and data channel  77  to the storage server  78 . In this configuration the information gathering on the client site is mainly directed to the components  21 ,  24 ,  25 ,  26  and  27  in FIG. 2 which provide information on the processors involved, the network subsystem, the buses used and the data source.  
         [0038]    System Optimization For Data Backup and Restore Operations  
         [0039]    The parameters which are to be optimized are related to the overall data throughput in GB/h (Gigabyte per hour). The following parameters are relevant for optimization of overall throughput in GB/h.  
         [0040]    Definitions of Terms Used in the Description  
         [0041]    Some definitions of terms used herein are described subsequently.  
         [0042]    File: A file represents one backup object. At least one multiplexing file goes into one session.  
         [0043]    Multiplexing. With multiplexing more than one file can be written in parallel into one session.  
         [0044]    Session. A session represents a data stream to be written on one storage device i.e. a tape drive.  
         [0045]    Compression. The implementation supports different kind of compression:  
         [0046]    RL compression which will consume less CPU time but only compresses with an average compression ratio of 3:2; and  
         [0047]    client API compression which consumes about half more CPU time than RL compression but has an better average compression ration of 5:2.  
         [0048]    It is also possible to use both compressions but this will not sum the two ratios but an average compression ratio of 3:1 can be reached.  
         [0049]    Variable definitions and definition parts of the system:  
         [0050]    IO performance (io_max_read/io_max_write). The maximum read rate from disks for backup/write rate to disks for restore; for the following examples we assume that there is no difference between read and write rate, thus only (io_max) is used; if other applications are doing IO operations during backup/restore, an average IO performance (io_avg) is used.  
         [0051]    bus[n] represents the maximum throughput of the bus n  
         [0052]    ioc[n] represents the maximum throughput of IO controller n  
         [0053]    disk[n]_read represents the average read rate of disk n  
         [0054]    disk[n]_write represents the average write rate of disk n  
         [0055]    Network throughput (tp). The maximum tp is (net_max); the average tp is (net_avg).  
         [0056]    Machine internal data throughput over the buses. The maximum tp over the buses is (bus_max); the average tp over the buses is (net_avg).  
         [0057]    Maximum read/write rate at the server side. The tape_max is the sum of all servers if more than one server is used. The tape_max[i] is the maximum rate on one server.  
         [0058]    Available processing power of client system. The maximal CPU load is cpu_max; to simplify the calculations described herein the maximum CPU load is set to 100%.  
         [0059]    Required processing power of the backup program. The required processing power of the backup operation is dp_cpu; in the implementation described it will be given in %.  
         [0060]    Throughput Basics  
         [0061]    The parameters which are to be optimized are related to the overall data throughput in GB/h (Gigabyte per hour). The following parameters are relevant for optimization of overall throughput in GB/h. To simplify the description of the gathering, processing and generation of parameters some assumptions are made:  
         [0062]    1a) A bus controller can reach its maximum throughput if one device/adapter is attached to it and reaches a higher or equal throughput.  
         [0063]    1b) A bus or controller has no controlling overhead if more than one device is attached to it.  
         [0064]    1c) If n devices are attached to a controller or bus with a throughput (tp_max), the devices can reach an average performance of tp_max/n.  
         [0065]    2) The network connectors used are dedicated for backup and restore operations. No other applications are using on the same network. This means that the network throughput is constant over time.  
         [0066]    Rules Applied  
         [0067]    The following rules should be maintained to ensure a maximum of data throughput:  
         [0068]    a) The number of sessions should be limited by the number of tape drives available for backup.  
         [0069]    b) The sum of the data throughput of all storage devices attached to a controller can not exceed the data throughput of that controller.  
         [0070]    c) The sum of the data throughput of all adapters attached to a bus can not exceed the data throughput of that bus.  
         [0071]    d) If IO and network devices share the same bus, the sum of data throughput of both storage and network devices must be less or equal to the maximum data throughput of the bus.  
         [0072]    Performance of the IO Subsystem  
         [0073]    The maximum throughput per IO controller (tp_io[n]):  
         [0074]    tp_io[n]=min(ioc[n], sum(disk[i]))  
         [0075]    where sum(disk[i]) is the sum disk[i] of all disks at controller n which contain relevant files for backup. If tp_io[n]&gt;ioc[n], then tp_io[n]=ioc[n].  
         [0076]    The maximum throughput per bus (tp_bus[n]):  
         [0077]    tp_bus[n]=max(bus[n], sum(tp_io[i]))  
         [0078]    where tp_io[i] is the throughput of the attached IO controllers and, if tp_bus[n]&gt;bus[n], then tp_bus[n]=bus[n].  
         [0079]    The overall IO throughput (io_max):  
         [0080]    io_max=sum(tp_bus[i])  
         [0081]    Determination of the Network Performance  
         [0082]    net_max=sum tp_bus[n] 
         [0083]    tp_bus[n]=min(bus[n], sum(net[i]))  
         [0084]    where net[i] are all attached network adapters which can be used to transfer data to the server.  
         [0085]    Determination of Storage Server Performance  
         [0086]    server[n]=min (sum(net[i], sum(tape[i]))  
         [0087]    where tape[i] is the performance of one tape and net[i] is the performance of one network adapter attached to the storage server that has connection to the client system.  
         [0088]    server=sum(server[i])  
         [0089]    which is the sum of throughput of all configured servers.  
         [0090]    Determination of Overall Maximum Data Throughput on Client  
                                                                                                                                                                                                                                                                         client_max       if (IO uses other buses than NET)       {                client_max = io_max + net_max            }       else       {                /** IO and Network adapter are on the same bus.            * At least some network controllers share buses            * with some IO controllers.            **/           /** But it makes a difference if we use compression           or not */           compress_ratio = 1           if use_compression then compress_ratio = 1/2;           bus[i] = min (           client_max =                sum(bus[i]/2)   /** buses with IO and                network controllers **/                + sum(net[j])   /** network adapter which don&#39;t                share their bus with a IO controller */                + sum(ioc[k])   /** IO adapter which don&#39;t share                their bus with a network adapter */                /** This maximum through put can only be reached if                the data read from one controller is not written to a controller on the same           bus if the sum of the controllers throughput exceeds the throughput of the           bus. */            }       if (client_max &lt; (sum(ioc[i]) + sum(net[i]))       {                /** the throughput of the controllers must be limited to avoid overhead when           switching bus access */           for (over all buses used by at least one IO controller and at least one network           adapter){           if (sum(ioc[k]) &gt; bus[i] /(2 + 1/compr_ratio + 1))           {                if (sum(net[k]) &gt; bus[i] / (2 + 1/compr_ratio +           1)){                if sum(net[k]) = bus[i] /                (2 + 1/compr_ratio + 1){                sum_ioc[k]) = bus[i] /                (2 + 1/compr_ratio + 1)                sum(ioc[k]) = bus[i] − sum(net[k])           } else {                sum(ioc[k]) = bus[i] − sum(net[k])                }           }           else {           }                }            }                  
 
         [0091]    Determination Whether Compression on Client has to be Used  
         [0092]    if (net_max&lt;io_max) and (net_max+io_max&lt;client_max) then use compression  
         [0093]    if (net_max&lt;io_max) or (net_max+io_max&gt;client_max) then use compression  
         [0094]    Backward Calculation if the Controllers have a Higher Data Throughput than the Buses Allow  
                                                                                                                                                 if(bus[i] &lt;                (sum(server[k]) * 1/compress_ratio ) +            sum(server[k])))                {                /** 132 MB/s &lt; 120 + 60 MB/s */           sum(server[k]) = bus[i] / (2 + 1/compr_ratio −1)                server[x]   = sum(server[k]) / number_of_servers           sum(ioc[k])   = sum(server[k]) / compress_ratio                ioc[x] = sum(ioc[k]) * tp_io[x]/sum(tp_io[k])                /**   note that sum(ioc[k]) is a little smaller than sum(server[k]); this will give room for                some additional bus traffic for memory access etc */                }                      
 
         [0095]    Determination of Multiplexin Factor (mux)  
         [0096]    mux=sum(ioc[i])/(sum(disk[k])/number_of_disks)/sessions /**here we will round up because we don&#39;t reach the average read rate per disk for all disks at the same time*/  
         [0097]    Determination of the number of sessions to be used  
         [0098]    sessions=min(round_up(server[i]/tp_tape), number_of_tapes)  
         [0099]    Numeric Example  
         [0100]    To simplify the description of the example the assumption is made that an off-line backup is performed in which the system performance and memory consumption can be disregarded. Furthermore, it is assumed that the files to backup are equally distributed over the disks.  
         [0101]    System Setup  
         [0102]    System Bus:  
         [0103]    1×PCI Bus 132 MB/s  
         [0104]    Network:  
         [0105]    1×Gigabit Ethernet (total 1000 MBit/s)  
         [0106]    net without compression 80 MB/s  
         [0107]    net with compression 160 MB/s  
         [0108]    Disk subsystem:  
         [0109]    2×ultrawide SCSI Controller with 80 MB/s  
         [0110]    Disks on controller  1 :  
         [0111]    4×13 MB/s  
         [0112]    2×12 MB/s  
         [0113]    Disks on controller  2 :  
         [0114]    3×11 MB/s  
         [0115]    4×13 MB/s  
         [0116]    Storage Server:  
         [0117]    3×Tape drive 20 MB/s  
               Calculation   :                                          tp_io        [   1   ]                  =     min        (       ioc        [   1   ]       ,     sum        (     disk        [   i   ]       )         )                                  =     min        (       80                 M                 B        /        s     ,       2   *   12     +     4   *   13                 M                 B        /        s         )                                  =     76                 M                 B        /        s                     tp_io        [   n   ]                  =     min        (       ioc        [   n   ]       ,     sum        (     disk        [   i   ]       )         )                     tp_io        [   2   ]                  =     max        (       ioc        [   2   ]       ,     sum        (     disk        [   i   ]       )         )                                =     max   (       80                 MB        /        s     ,       3   *   11     +     4   *   13                 MB        /        s         )                                =     max        (       80                 MB        /        s     ,     85                 MB        /        s       )                                    if                   tp_io        [   2   ]         &gt;     ioc        [   2   ]                                    then                   tp_io        [   2   ]         =         ioc        [   2   ]            tp_io        [   2   ]         =     80                 MB        /        s                     io_max              =     76   +     80                 MB        /        s                                =     156                 MB        /        s                   net_max              =     min        (       bus        [   1   ]       ,     net        [   1   ]         )                                =     min   (       132                 MB        /        s     ,     80                 MB        /        s       )                                80                 MB        /        s                                raw                 data                 rate                   (     compressed                 or                 not     )       :                   server        [   1   ]                  =     min        (       net        [   1   ]       ,     sum        (     tape        [   i   ]       )         )                                =     min   (       80                 MB        /        s     ,     3   *   20                 MB        /        s       )                                =     60                 MB        /        s                                         
 
         [0118]    Because without compression only as much data as written from IO can be sent over the network and received by the storage server. The application would read 60 MB/s from a disk. Thus, without compression a maximum of 60 MB/s can be written to the tapes; this is equal to 180 GB/h.  
         [0119]    With compression:  
                                                                                                                                                                                                                       client_max = sum(bus[i]/2) + sum(net[j]) + sum(ioc[k])                = 132 MB/s                if (net_max &lt; io_max) or (net_max + io_max &gt; client_max)                then use compression                80MB/s &lt; 156 MB/s                so the application should use compression                compr_ratio = 1/2           The system meets the following requirement:           bus[i] &lt; (sum( server[k]) * 1/compress_ratio) +           sum(server[k]))) so that the following backward           calculation has to be performed:           {           /** 132 MB/s &lt;120 + 60 MB/s */           sum(server[k]) = bus[i] / (2 + 1/compr_ratio −1)                = 132 MB/s/3           = 44 MB/s                server[x]   = sum(server[k]) / number_of_servers           server[1]   = 44 [MB/s] / 1               = 44 MB/s                sum(ioc[k])   = sum(server[k]) / compress_ratio           sum{ioc[k])   = 44 [MB/s] / 1/2                = 88 MB/s                ioc[x]   = sum(ioc[k]) * tp_io[x]/sum(tp_io[k])                ioc[1]   = 88 MB/s * 76 MB/s / 156 MB/s           ioc[1]   = 42 MB/s           ioc[1]   = 88 MB/s * 80 MB/s / 156 MB/s           ioc[1]   = 45 MB/s           }           sessions   = min(round_up(server[i] / tp_tape),                number_of_tapes)           sessions   = min(round_up(44 / 20), number_of_tapes)               = min(3, 3)           sessions   = 3           mux   = sum(ioc[i]) / (sum(disk[k]) / number_of_disks)                / sessions           mux   = 88[MB/s  / 156[MB/s]/13                mux   = 88 /12 / 3                mux   = 7.33 / 3           mux   = 4                      
 
         [0120]    While the invention is described with reference to preferred embodiments, modifications or other implementations are within the scope of the invention as defined in the claims.