Patent Abstract:
Load information and a first processing time are provided in association with each of a plurality of first time segments that each have a fixed duration time and are included in a first time period, where the load information indicates a load condition of a target computer that executed a target job during the each first time segment, and the first processing time indicates a running time of the target job during the each first time segment. One or more first time segments each having a predetermined analogous relationship with a second time segment in a second time period during which the target job is expected to be executed by the target computer are selected to predict a second processing time indicating a running time of the target job during the second time period based on the first processing times associated with the selected one or more first time segments.

Full Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is based upon and claims the benefit of priority of the prior Japanese Patent Application No. 2011-122929, filed on May 31, 2011, the entire contents of which are incorporated herein by reference. 
     FIELD 
     The embodiments discussed herein are related to apparatus and method for predicting a processing time of a computer. 
     BACKGROUND 
     Some ordinary technologies for enabling steady and low cost operations of systems are to automatically power on or off a server, to automatically start or stop a service, to automatically schedule jobs, etc. Meanwhile, such a system predicts data processing time for carrying out jobs. If it is predicted that some of the jobs will continue over expected ending time, those jobs are manually avoided from continuing in order that daily work will finish in scheduled time. 
     A related art is, e.g., to calculate expected data processing time for a job scenario in a case where preregistered events are rescheduled and the job scenario is carried out while an effect of another job scenario expected for a period of time after being rescheduled is taken into account. Further, a related art is to change programs depending upon job running condition. Further, a related art is to divide achieved job running time into components and to pile such data up, and to predict job running time on the basis of the piled-up job processing history data so as to schedule the jobs. 
     Japanese Laid-open Patent Publications No. 2010-231694, No. 2005-208767 and No. H04-326434 discuss those related arts. 
     SUMMARY 
     According to an aspect of the invention, apparatus and method for predicting a processing time of a computer is provided. Load information and a first processing time are provided in association with each of a plurality of first time segments that each have a fixed duration time and are included in a first time period, where the load information indicates a load condition of a target computer that executed a target job during the each of the plurality of first time segments, and the first processing time indicates a running time of the target job during the each of the plurality of first time segments. One or more first time segments each having a predetermined analogous relationship with a second time segment included in a second time period during which the target job is expected to be executed by the target computer are selected from among the plurality of first time segments, based on the load information associated with the plurality of first time segments. Then a second processing time indicating a running time of the target job that is expected to be executed during the second time period is predicted based on the first processing times associated with the selected one or more first time segments. 
     The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims. 
     It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention, as claimed. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a diagram illustrating an example of practicing a prediction method, according to an embodiment; 
         FIG. 2  is a diagram illustrating a configuration example of a scheduling system, according to an embodiment; 
         FIG. 3  is a diagram illustrating an example of a hardware configuration of a job management device, according to an embodiment; 
         FIG. 4  is a diagram illustrating an example of what is stored in a schedule table, according to an embodiment; 
         FIG. 5  is a diagram illustrating an example of a functional configuration of a job management device, according to an embodiment; 
         FIG. 6  is a diagram illustrating an example of what is stored in a data processing time table, according to an embodiment; 
         FIG. 7  is a diagram illustrating an example of schedule information, according to an embodiment; 
         FIG. 8  is a diagram illustrating an example of warning information, according to an embodiment; 
         FIG. 9  is a diagram illustrating an example of transition in what is stored in a schedule table, according to an embodiment; 
         FIG. 10  is a diagram illustrating an example of a progress status screen, according to an embodiment; 
         FIG. 11  is a diagram illustrating an example of a setting screen, according to an embodiment; 
         FIG. 12  is a schematic diagram illustrating an example of a change in expected starting time of a job, according to an embodiment; 
         FIG. 13  is a schematic diagram illustrating an example of a change in expected starting time of a job, according to an embodiment; 
         FIG. 14  is a schematic diagram illustrating an example of a change in expected starting time of a job, according to an embodiment; 
         FIG. 15  is a diagram illustrating an example of an operational flowchart for managing jobs by means of a job management device, according to an embodiment; 
         FIG. 16  is a diagram illustrating an example of an operational flowchart for selecting a target computer to be loaded, according to an embodiment; 
         FIG. 17  is a diagram illustrating an example of an operational flowchart for calculating predicted data processing time, according to an embodiment; 
         FIG. 18  is a diagram illustrating an example of an operational flowchart for changing a starting time of a job, according to an embodiment; 
         FIG. 19  is a diagram illustrating an example of an operational flowchart for a sizing process, according to an embodiment; 
         FIG. 20  is a schematic diagram illustrating an example of an operation for machine replacement, according to an embodiment; and 
         FIGS. 21 and 22  are diagrams illustrating an example of operations in time of a shift to a cloud environment, according to an embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     There is a problem in the ordinary technologies in that it is difficult to predict data processing time desirable for carrying out jobs. As load condition of a computer to be loaded with the jobs, e.g., changes depending upon amounts of work and data size which change day by day, it is difficult to predict data processing time for the jobs to be carried out by the computer. 
     Embodiments of a prediction program, a prediction device and a prediction method will be explained in detail with reference to the drawings below. 
     First Embodiment 
       FIG. 1  is a diagram illustrating an example of practicing a prediction method, according to an embodiment.  FIG. 1  depicts a prediction device  100 , i.e., a computer which can access a load information database (DB)  110  and predicts data processing time for a job to be processed by a target computer. 
     The term job used here means a unit of work processed by the target computer. Specifically, e.g., a job is part or all of programs included in a series of programs which achieves batch or online work. 
     The load information DB  110  is a database in which load information indicating load condition of a target computer to be loaded with a job to be processed for every period of time of a certain segment length into which time is segmented is stored. The period of time of the certain segment length means each of periods of time into which the time axis is segmented by any time width having been set. The period of time may be, e.g., each of periods of time into which the time axis is divided on a daily or weekly basis. Hereinafter, a period of time of a certain segment length will be also expressed as “a time segment”. 
     The load information indicating load condition of a target computer indicates, e.g., an amount of work loaded onto the target computer which changes as time passes. Specifically, e.g., the load information indicates an amount of work loaded onto the target computer at regular intervals (e.g., every one minute, ten minutes, one hour, etc.) in each of the periods of time. The amount of work loaded onto the target computer may be represented by an amount of use of a central processing unit (CPU) of the target computer or an amount of use of an input/output (I/O) of the target computer. 
     The load condition of the target computer changes depending upon the number of programs being run or size of data being processed in each of the programs. Further, data processing time for a job changes depending upon the load condition of the target computer even if the same target computer carries out the job. If, e.g., a target computer to be loaded with a job is running a program which differs from the job to be processed, the data processing time for the job tends to be longer than that in a case where the target computer runs no other programs. 
     Thus, according to the first embodiment, calculate data processing time for a job to be carried out on a scheduled day on the basis of achieved data processing time for a job carried out on a day giving a sign that load condition of a target computer is similar to that on the scheduled day, so as to predict the data processing time for the job taking the load condition of the target computer which changes day by day into account. That is, the prediction device  100  calculates predicted data processing time for a job expected to be carried out in an expected period of time on the basis of achieved data processing time for a job carried out in the past specifying same content to be processed as content specified by the job expected to be carried out. 
     An exemplary prediction process to be run by the prediction device  100  will be explained below. Segment the time axis into periods of time (time segments) each having a certain segment length, e.g., on a daily basis, as explained here. Further, a job J to be carried out every Monday will be explained as an exemplary job to be processed. 
       FIG. 1  illustrates graphs  101 - 108  each indicating an amount of work loaded onto a target computer which changes as time passes in a rectangular coordinate system formed by a horizontal and vertical axes representing time and the amount of work, respectively. The target computer is to be loaded with the job J to be processed. 
     The graph  101  indicates change in the amount of work loaded onto the target computer from 9 to 12 o&#39;clock, Monday, April 11. The graph  102  indicates change in the amount of work loaded onto the target computer from 9 to 12 o&#39;clock, Monday, April 18. The graph  103  indicates change in the amount of work loaded onto the target computer from 9 to 12 o&#39;clock, Monday, April 25. The graph  101  indicates change in the amount of work loaded onto the target computer from 9 to 12 o&#39;clock, Monday, May 2. 
     The graph  105  indicates change in the amount of work loaded onto the target computer from 9 to 12 o&#39;clock, Monday, May 9. The graph  106  indicates change in the amount of work loaded onto the target computer from 9 to 12 o&#39;clock, Monday, May 16. The graph  107  indicates change in the amount of work loaded onto the target computer from 9 to 12 o&#39;clock, Monday, May 23. The graph  108  indicates change in the amount of work loaded onto the target computer from 9 to 12 o&#39;clock, Monday, May 30. 
     An exemplary case, in which data processing time for the job J starting to be carried out by the target computer to be loaded after 12 o&#39;clock, Monday, May 30 is calculated, will be explained here. 
     (1) The prediction device  100  obtains, from the load information DB  110 , load information of a target computer to be loaded with a job J to be processed in a particular time range in a scheduled day on which the job J will be carried out. The load information of the target computer is, e.g., an average of an amount of work loaded onto the target computer (called “average amount of work” hereafter). Suppose, here, that the particular time range is a “time range from 9 to 12 o&#39;clock”. In this case, the prediction device  100  obtains, from the load information DB  110 , load information indicating an average amount of work loaded onto the target computer, e.g., in the time range from 9 to 12 o&#39;clock, Monday, May 30. 
     (2) The prediction device  100  selects, from among past running days, one or more days on which the target computer carried out the job J giving a sign that the load condition of the target computer in the time range from 9 to 12 o&#39;clock is similar to the load condition of the target computer in the time range from 9 to 12 o&#39;clock on the scheduled day. 
     That is, the prediction device  100  selects one or more of periods of time in which the target computer carried out the job J in the past being particularly similarly related to the load condition in the period of time in which the job J is expected to be carried out. The particularly similarly related period of time is one of the periods time in which the target computer carried out the job J in the past giving a sign that the load condition of the target computer is similar to the load condition of the target computer in the period of time in which the job J is expected to be carried out. 
     For example, the particularly similarly related period of time may be a period of time in which a difference between an average amount of work loaded onto the target computer in the periods of time in which the target computer carried out the job J in the past and an average amount of work loaded onto the target computer in the period of time in which the job J is expected to be carried out is not greater than a threshold value. Further, as the particularly similarly related periods of time, a plurality of periods of time may be selected, from the periods of time in which the job J is carried out in the past, in the increasing order of the absolute value of a difference between an average amount of work loaded onto the target computer in the periods of time in which the job J is carried out in the past and an average amount of work loaded onto the target computer in the period of time in which the job J is expected to be carried out. 
     In  FIG. 1 , the running days on which the target computer carried out the job J in the past are Monday, April 11; Monday, April 18; Monday, April 25; Monday, May 2; Monday, May 9; Monday, May 16; and Monday, May 23. 
     For example, the prediction device  100  identifies at least one of the above past running days giving a sign that an absolute difference between an average amount of work loaded onto the target computer in the time range from 9 to 12 o&#39;clock on Monday, May 30 and an average amount of work loaded onto the target computer in the time range from 9 to 12 o&#39;clock in the past is not greater than a threshold. 
     Incidentally, the average amount of work loaded onto the target computer in the time range from 9 to 12 o&#39;clock on the scheduled day may be identified, e.g., from load information of the target computer in the time range from 9 to 12 o&#39;clock on the scheduled day. Further, the average amount of work loaded onto the target computer in the time range from 9 to 12 o&#39;clock on the past running days may be identified, e.g., from load information of the target computer in the time range from 9 to 12 o&#39;clock on the past running days. 
     The prediction device  100  may identify a past running day giving a sign that the load condition of the target computer in the “time range from 9 to 12 o&#39;clock” is similar to that on the scheduled day. In  FIG. 1 , e.g., the prediction device  100  identifies Monday, April 11; Monday, May 2; and Monday, May 16 as the running days giving a sign that the load condition of the target computer in the “time range from 9 to 12 o&#39;clock” is similar to that on the scheduled day “Monday, May 30”. 
     (3) The prediction device  100  calculates predicted data processing time for the job J to be carried out by the target computer to be loaded with the job J on the scheduled day, on the basis of the data processing time for the job J carried out on the identified running days. The data processing time mentioned here means, e.g., running time to be taken since the job J starts being carried out and until the job J finishes being carried out. 
     For example, the prediction device  100  calculates an average of the data processing time for the job J carried out on the identified running days so as to calculate predicted data processing time for the job J that is to be carried out, by the target computer to be loaded, after 12 o&#39;clock on the scheduled day. Incidentally, the data processing time for the job J carried out on the identified running days is obtained, e.g., from the target computer to be loaded with the job J. 
     Suppose, e.g., that the data processing time for the job J carried out on Monday, April 11; Monday, May 2; and Monday, May 16 is “20 minutes”, “15 minutes” and “25 minutes”, respectively. In this case, the predicted data processing time for the job J is “20 minutes”. 
     (4) The prediction device  100  outputs the predicted data processing time for the job J having been calculated. Here, the prediction device  100  outputs the predicted data processing time “20 minutes” in a case where the job J is carried out after 12 o&#39;clock on Monday, May 30. 
     The prediction device  100  of the first embodiment explained above may calculate predicted data processing time for the job J to be carried out on the scheduled day, on the basis of the data processing time for the job J carried out on the past running days each giving a sign that the load condition of the target computer in the same time range as the time range until the scheduled day and time is similar to the load condition on the scheduled day. The prediction device  100  may thereby calculate predicted data processing time for the job in accordance with the load condition of the target computer to be loaded with the job on the scheduled day. In this way, the prediction device  100  may predict data processing time for a job according to the first embodiment more precisely than according to simple calculation of an average of data processing time for the job J in the past, etc. 
     Second Embodiment 
     Next, a scheduling system  200  of a second embodiment will be explained. The second embodiment will be explained in a case where the prediction device  100  having been explained as to the first embodiment is applied to a job management device  201  included in the scheduling system  200 . Incidentally, explanation of a portion which is the same as the corresponding one explained as to the first embodiment will be omitted here. 
       FIG. 2  is a diagram illustrating a configuration example of a scheduling system, according to an embodiment. The scheduling system  200  depicted in  FIG. 2  may be configured to include the job management device  201 , an information collecting device  202 , machines M 1 -Mn (where n is a natural number larger than one), and a cloud environment  203 . The job management device  201 , the information collecting device  202 , the machines M 1 -Mn, and the cloud environment  203  are connected to one another via a wired or wireless network  210  in the scheduling system  200 . The network  210  is, e.g., the Internet, a local area network (LAN) or a wide area network (WAN). 
     The job management device  201  mentioned here is a computer which manages a job to be processed (hereinafter, also expressed as “a target job”). For example, the job management device  201  has functions to schedule a job, to watch how the job is run, and to power on and off the machines M 1 -Mn, etc. Further, the job management device  201  may access the load information DB  110  via the information collecting device  202 . 
     The information collecting device  202  may be a computer having the load information DB  110  and a function to collect load information of the respective machines M 1 -Mn. The load information characterizes load condition of the respective machines M 1 -Mn and represents amounts of work such as amounts of use of CPUs or I/Os of the respective machines M 1 -Mn. The amounts of use of the CPUs mean, e.g., usage rates of the CPUs of the respective machines M 1 -Mn. The amounts of use of the I/Os mean, e.g., a sum of the numbers of bytes read from and written into physical disks in the machines M 1 -Mn. 
     For example, the information collecting device  202  receives, from the respective machines M 1 -Mn, load information collected periodically (e.g., every one minute) by means of respective agents introduced into the respective machines M 1 -Mn. The information collecting device  202  may thereby periodically collect the load information of the respective machines M 1 -Mn. 
     The collected load information of the respective machines M 1 -Mn may be stored, e.g., in the load information DB  110 . Further, a lot of jobs to be processed each have a characteristic such that the job is repeatedly carried out on a particular day of every week or on a particular day of every month. Thus, the information collecting device  202  may calculate average amounts of work loaded onto the respective machines M 1 -Mn on a basis of particular days of the week or of the month and store the calculated average amounts of work in the load information DB  110 . 
     Further, upon being requested by the job management device  201  an amount of work loaded onto a particular target computer at particular time, the information collecting device  202  may calculate, e.g., an average amount of work loaded onto the particular target computer at the particular time and reply to the job management device  201 . 
     The machines M 1 -Mn are each a target computer which carries out a job to be processed (also expressed as “a target job”). The machines M 1 -Mn are each, e.g., a server in an on-premises environment, a personal computer (PC), a virtual computer which works on the server, etc. An agent which periodically collects the load information of the respective machines M 1 -Mn (by means of software) may be introduced into each of the machines M 1 -Mn. 
     Further, the machines M 1 -Mn each have a function to send the load information collected by the agent to the job management device  201 . Specifically, e.g., the machines M 1 -Mn each send the load information each time the agent collects the load information. As a result, the job management device  201  periodically collects the load information of the respective machines M 1 -Mn. 
     The cloud environment  203  is a cloud computing system including a group of servers which each carry out a job to be processed. Specifically, e.g., the group of servers includes a Web server in a data center, an application server, a database server, etc. The information collecting device  202  may collect load information of the group of servers included in the cloud environment  203 . 
     Incidentally, the job management device  201  and the information collecting device  202  are, although not limited to be, provided separately from each other according to the above explanation. For example, the job management device  201  may have the load information DB  110  and the function of the information collecting device  202 . 
     (Exemplary Hardware Constitution of the Job Management Device  201 ) 
       FIG. 3  is a diagram illustrating an example of a hardware configuration of a job management device, according to an embodiment. As depicted in  FIG. 3 , the job management device  201  may be configured to include a CPU  301 , a read-only memory (ROM)  302 , a random access memory (RAM)  303 , a magnetic disk drive  304 , a magnetic disk  305 , an optical disk drive  306 , an optical disk  307 , a display monitor  308 , an interface (I/F)  309 , a keyboard  310 , a mouse  311 , a scanner  312  and a printer  313 . Further, the above components are connected to one another via a bus  300 . 
     The CPU  301  entirely controls the job management device  201 . The ROM  302  stores therein a program such as a boot program. The RAM  303  may be used as a work area for the CPU  301 . The magnetic disk drive  304  controls operations to read and write data from and to the magnetic disk  305  as controlled by the CPU  301 . The magnetic disk  305  stores therein data written as controlled by the magnetic disk drive  304 . 
     The optical disk drive  306  controls operations to read and write data from and to the optical disk  307  as controlled by the CPU  301 . The optical disk  307  stores therein data written as controlled by the optical disk drive  306 , and allows a computer to read data stored in the optical disk  307 . 
     The display monitor  308  indicates thereon a cursor, an icon and a toolbox as well as data such as a document, an image, and functional data. Monitors such as a CRT, a TFT liquid crystal display, a plasma display and so on may be employed as the display monitor  308 . 
     The I/F  309  is connected to the network  210  via a communication line, and is connected to another device via the network  210 . Then, the I/F  309  controls an interface between the network  210  and the inside, and controls data input and output from and to an external device. Devices such as a modem, a LAN adaptor and so on may be employed as the I/F  309 . 
     The keyboard  310  has a key for operations to input a character, a numeral, various kinds of instructions, etc. and is used for data input. Further, the keyboard  310  may be replaced with a touchpad for data input or a ten key. The mouse  311  is used for operations to move the cursor, to choose an area, to move or resize a window on a screen displayed on the display monitor  308 . The mouse  311  may be replaced with a trackball or a joystick having a similar function of a pointing device. 
     The scanner  312  optically reads an image and introduces image data into the job management device  201 . Incidentally, the scanner  312  may have an optical character reader (OCR) function. Further, the printer  313  prints image or document data. A laser printer or an inkjet printer may be employed as the printer  313 . 
     Incidentally, the job management device  201  may lack some of the portions described above such as the optical disk drive  306 , the optical disk  307 , the scanner  312 , the printer  313 , etc. Further, the information collecting device  202 , the machines M 1 -Mn, and the group of servers included in the cloud environment  203  each may be implemented by a hardware configuration similar to that of the job management device  201  described above. 
     (What is Stored in a Schedule Table  400 ) 
     Next, what is stored in a schedule table  400  to be used by the job management device  201  will be explained. The schedule table  400  is implemented in a storage device such as the RAM  303 , the magnetic disk  305 , or the optical disk  307  as depicted in  FIG. 3 . 
       FIG. 4  is a diagram illustrating an example of what is stored in the schedule table  400 , according to an embodiment. As depicted in  FIG. 4 , the schedule table  400  has fields of a job name, a starting day, expected starting time, expected ending time, predicted data processing time, predicted ending time, a parameter, who&#39;s loaded, a user name, and a comment. Set data in each of the fields, so that schedule information  400 - 1  through  400 - m  for jobs J 1  through Jm, respectively, is stored as a record. 
     The job name represents an identifier of the job to be processed. The starting day represents a day on which the job starts to be carried out. The expected stating time is expected time (HH:MM) at which the job starts to be carried out. The expected ending time is expected time (HH:MM) at which the job finishes being carried out. The predicted data processing time represents predicted data processing time that the scheduling system  200  takes so as to carry out the job. 
     The predicted ending time represents predicted time (HH:MM) at which the job finishes being carried out. The parameter represents a file related to parameters that the scheduling system  200  uses so as to carry out the job. The field of who&#39;s loaded represents an identifier of a machine that the job is loaded with. The user name represents an identifier of a user who requested the job. The comment field represents a comment related to the job. 
     The schedule information  400 - 1 , e.g., says that, for a job J 1 , “Wednesday” is set to the starting day, “10:00” to the expected starting time, “10:15” to the expected ending time, “-” to the predicted data processing time, and “-” to the predicted ending time. Further, “file data 1.dat” is set to the parameter, “root” to the user name, and “customer management” to the comment. 
     The starting day “Wednesday” says that the job J 1  is carried out every Wednesday. The comment “customer management” says that the job J 1  is related to customer management work. Further, nothing is set for the job J 1  to the field of the predicted data processing time as indicated by “- (null)”, nor to the fields of the predicted ending time and who&#39;s loaded. 
     The schedule information  400 - 2 , e.g., says that, for a job J 2 , “25th” is set to the starting day, “9:00” to the expected starting time, “13:30” to the expected ending time, “-” to the predicted data processing time, and “-” to the predicted ending time. Further, “file data 2.dat” is set to the parameter, “root” to the user name, and “customer management” to the comment. 
     The starting day “25th” says that the job J 1  is carried out every 25th day of the month. Further, nothing is set for the job J 2  to the field of the predicted data processing time as indicated by “- (null)”, nor to the fields of the predicted ending time and who&#39;s loaded. Incidentally, set a “date (YY:MM:DD)” to the field of the starting day in order to specify a particular date for the starting day. 
     Further, the schedule information  400 - 1  through  400 - m  may each include related information representing another job related to the job to be processed. The related information includes, e.g., information representing a related group of jobs or order in which the jobs in the group are carried out. 
     (Exemplary Functional Constitution of the Job Management Device  201 ) 
       FIG. 5  is a diagram illustrating an example of a functional configuration of a job management device, according to an embodiment. As depicted in  FIG. 5 , the job management device  201  may be configured to include a choosing unit  501 , a selecting unit  502 , an obtaining unit  503 , an identifying unit  504 , a first calculating unit  505 , a deciding unit  506 , a changing unit  507 , a second calculating unit  508 , and an output unit  509 . These functions to form a controller (the choosing unit  501  through the output unit  509 ) may be implemented, for example, by means of a program stored in the storage device such as the ROM  302 , the RAM  303 , the magnetic disk  305 , or the optical disk  307  depicted in  FIG. 3  and run by the CPU  301  or the I/F  309 . Further, results of data processing run by the respective functional units are stored in the storage device such as the RAM  303 , the magnetic disk  305 , or the optical disk  307 . 
     The choosing unit  501  has a function to choose a job to be processed. For example, the choosing unit  501  may refer to the schedule table  400  depicted in  FIG. 4  so as to choose one of the jobs J 1 -Jm whose starting day is the present day. Further, the choosing unit  501  may choose a job specified by an input operation done by a user by the use of, e.g., the keyboard  310  or the mouse  311  depicted in  FIG. 3 . 
     The selecting unit  502  has a function to select a target computer to be loaded with a job to be processed. For example, the selecting unit  502  may select a machine to be loaded with the job to be processed, from the machines M 1 -Mn and the cloud environment  203  depicted in  FIG. 2 . A result of the selection is stored, e.g., in the schedule table  400 . 
     When, e.g., the machine M 1  is selected as the machine to be loaded with the job J 1  to be processed, “M 1 ” is set to the who&#39;s loaded field in the schedule information  400 - 1 . At this time, data as to where a group of programs for carrying out the job J 1  is filed (e.g., c:\) may be stored in the schedule information  400 - 1  together. Incidentally, what is specifically processed by the selecting unit  502  selecting the target computer to be loaded with the job will be described later with reference to  FIG. 11 . 
     A job to be processed (a target job) is written as a “job J” as explained below. Further, a target computer to be loaded with the job J will be written as a “machine M”. 
     The obtaining unit  503  has a function to obtain load information indicating load condition of the machine M in a specified period of time in an expected period of time. The term period of time means each of periods of time into which the time axis is segmented by a certain time width (a certain time duration) as described above. The expected period of time means a period of time in which the job J is expected to start to be carried out. The specified period of time means one of the respective periods of time. Any one of the respective periods of time may be set as the specified period of time. 
     Segment the time axis, e.g., on a daily basis. Then, the expected period of time is one of days on which the job J is expected to start to be carried out (e.g., May 18, 2011). Further, the specified period of time is one of time ranges (e.g., from 9 to 12 o&#39;clock) in the day on which the job J starts to be carried out. 
     Further, segment the time axis, e.g., on a weekly basis. Then, the expected period of time is one of weeks in which the job J is expected to start to be carried out (e.g., tenth week in the year 2011). Further, the specified period of time is one of days of the week in which the job J starts to be carried out or a group of consecutive days of the week (e.g., Monday-Wednesday). 
     The load information indicating the load condition of the machine M in the specified period of time in the expected period of time indicates, e.g., an average amount of work loaded onto the machine M in the specified period of time in the expected period of time. For example, the load information indicating the load condition of the machine M includes an average amount of use of the CPU and an average amount of use of the I/O of the machine M. 
     For example, the obtaining unit  503  may obtain the load information indicating an average amount of work loaded onto the machine M (e.g., average amounts of use of the CPU and the I/O) in the specified period of time in the expected period of time from the load information DB  110  via the load information DB  110 . 
     Further, the obtaining unit  503  has a function to obtain load information indicating load condition of the machine M in a specified period of time in each of periods of time in which the machine M carried out the job J. For example, the obtaining unit  503  may obtain, from the load information DB  110  via the information collecting device  202 , load information indicating an average amount of work loaded onto the machine M in a specified period of time in each of periods of time in which the machine M carried out the job J at particular time. 
     Any time may be set as the particular time. The particular time may be, e.g., an interval between a certain period of time (e.g., a couple of days, months or years) ago and at present. The information indicating the particular time may be stored in the storage device such as the ROM  302 , the RAM  303 , the magnetic disk  305 , or the optical disk  307 . 
     Each of periods of time of a certain segment length into which the time axis is segmented on a daily basis will be explained below as an example. Further, suppose that a specified period of time in each of the periods of time is a particular time range in the relevant period of time. 
     Further, the obtaining unit  503  has a function to obtain data processing time for the job J carried out by the machine M. For example, the obtaining unit  503  obtains data processing time for the job J from the machine M each time the machine M carries out the job J. The obtained data processing time for the job J is stored, e.g., in a data processing time table  600  depicted later in  FIG. 6 . 
     The identifying unit  504  identifies one of periods of time in which the machine M carried out the job J (called “running days” hereafter) giving a sign that the load condition of the machine M in a particular time range on each of the running days is similar to the load condition of the machine M in the relevant time range in an expected period of time (called “scheduled day” hereafter). The particular time range may be set to any time range in one day by means, e.g., of an input operation done by a user using the keyboard  310  or the mouse  311 . 
     An exemplary case will be explained below in which a time range until present time on a scheduled day when the job J starts to be carried out (i.e., a time range until scheduled day and time) is set as the particular time range. What the identifying unit  504  specifically processes will be exemplarily explained (particular exemplary processes  1 - 3  described below). 
     Particular Exemplary Process  1   
     The identifying unit  504  identifies one of running days giving a sign that a difference between an average amount of work loaded onto the machine M in the time range until the scheduled day and time and an average amount of work loaded onto the machine M in the same time range as the relevant time range on each of the running days is not greater than a threshold. The identifying unit  504  identifies the average amount of work loaded onto the machine M in the time range until the scheduled day and time, e.g., from load information indicating obtained load condition in the time range until the scheduled day and time. Further, the identifying unit  504  identifies the average amount of work loaded onto the machine M in the same time range as the relevant time range on each of the running days, e.g., from obtained load information indicating the average amount of work loaded onto the machine M in the same time range as the relevant time range. 
     For example, the identifying unit  504  may identify one of running days on which a difference d 1  between an average amount of use of the CPU of the machine M in the time range until the scheduled day and time and an average amount of use of the CPU of the machine M in the same time range as the relevant time range on each of the running days is not greater than a threshold α. 
     Further, for example, the identifying unit  504  may identify one of running days on which a difference d 2  between an average amount of use of the I/O of the machine M in the time range until the scheduled day and time and an average amount of use of the I/O of the machine M in the same time range as the relevant time range on each of the running days is not greater than a threshold β. 
     Further, for example, the identifying unit  504  may identify a running day on which the above difference d 1  is not greater than the threshold α and the above difference d 2  is not greater than the threshold β. Incidentally, the above threshold α is preset and stored in the storage device such as the ROM  302 , the RAM  303 , the magnetic disk  305 , or the optical disk  307 , and so is the threshold β. 
     Particular Exemplary Process  2   
     The identifying unit  504  identifies one of running days on which a difference between an average amount of work loaded onto the machine M in the time range until the scheduled day and time and an average amount of work loaded onto the machine M in the same time range as the relevant time range on each of the running days has one of smallest K values (e.g., K=5 or 10). 
     For example, the identifying unit  504  may identify one of running days on which a difference d 1  between an average amount of use of the CPU of the machine M in the time range until the scheduled day and time and an average amount of use of the CPU of the machine M in the same time range as the relevant time range on each of the running days has one of the smallest K values. 
     Further, for example, the identifying unit  504  may identify one of running days on which a difference d 2  between an average amount of use of the I/O of the machine M in the time range until the scheduled day and time and an average amount of use of the I/O of the machine M in the same time range as the relevant time range on each of the running days has one of the smallest K values. 
     Particular Exemplary Process  3   
     Further, the identifying unit  504  may identify one of running days on which a difference between an average amount of work loaded onto the machine M in the time range until the scheduled day and time and an average amount of work loaded onto the machine M in the same time range as the relevant time range on each of the running days is not greater than a threshold and has one of smallest K values. 
     For example, the identifying unit  504  may identify a running day on which the difference d 1  is not greater than the threshold α and the difference d 1  has one of the smallest K values. Further, the identifying unit  504  may identify a running day on which the difference d 2  is not greater than the threshold β and the difference d 2  has one of the smallest K values. 
     The identified running days will be written as “running days D 1 -DK” (K is a natural number). Further, any one of the running days D 1 -DK will be written as a “running day Dk” (k=1 through k). 
     The first calculating unit  505  has a function to calculate predicted data processing time for the job J to be carried out after the scheduled day and time on the basis of the data processing time for the job J carried out on the identified running days D 1 -DK. For example, the first calculating unit  505  may calculate an average length of the data processing time for the job J carried out on the running days D 1 -DK as the predicted data processing time for the job J. 
     At this time, the first calculating unit  505  may exclude plural longest (and/or shortest) values of the data processing time for the job J carried out on the running days D 1 -DK. The first calculating unit  505  may thereby exclude values of the data processing time extended too long because of some failure, etc., so that the predicted data processing time for the job J does not vary widely. 
     Further, the first calculating unit  505  may identify a running day on which the job J was not carried out in the same time range as the time range until the scheduled day and time. Then, the first calculating unit  505  may calculate predicted data processing time for the job J to be carried out after the scheduled day and time on the basis of the data processing time for the job J carried out on the identified running day. 
     The first calculating unit  505  may thereby calculate predicted data processing time for the job J on the basis of data processing time for the job J carried out on a running day on which load condition while the job J is not being carried out is similar to the load condition on the scheduled day. The data processing time for the job J may thereby be predicted more precisely. 
     Further, when the job J was carried out plural times on the running day Dk, an average length of the data processing time for the job J carried out on the running day Dk may be given as the data processing time for the job J carried out on the running day Dk. The data processing time for the job J carried out on each of the running days D 1 -DK may be identified, e.g., on a data processing time table  600  depicted in  FIG. 6 . 
     What is stored in the data processing time table  600  will be explained with reference to an exemplary job J 1  carried out by the machine M 1  in the past. Incidentally, the data processing time table  600  is made for every job J carried out by each of the machines M, and is implemented by the storage device such as the RAM  303 , the magnetic disk  305 , or the optical disk  307 . 
       FIG. 6  is a diagram illustrating an example of what is stored in a data processing time table, according to an embodiment. As depicted in  FIG. 6 , the data processing time table  600  may be configured to include job name, who&#39;s loaded, running day, and data processing time fields. Set data to the respective fields, so that the data processing time for the job J 1  carried out by the machine M 1  is stored. 
     The job name field includes an identifier of a job J to be processed. The who&#39;s loaded field includes an identifier of the machine M to be loaded with the job. The running day field includes a date on which the job J was carried out. The data processing time field includes data processing time [in minutes] for the job J carried out by the machine M in the past. The data processing time for the job J 1  carried out by the machine M 1  on Mar. 23, 2011, e.g., is fifteen [minutes]. 
     Name, e.g., the job J to be processed and the machine M to be loaded with the job J “job J 1 ” and “machine M 1 ”, respectively. Further, let the identified running days D 1 -DK be running days D 1  “Mar. 23, 2011”, D 2  “Apr. 6, 2011”, D 3  “Apr. 20, 2011”, and D 4  “May 11, 2011”. 
     In this case, the first calculating unit  505  refers to the data processing time table  600  at first, so as to identify the data processing time “15 [minutes], 21 [minutes], 41 [minutes] and 11 [minutes]” for the job J 1  carried out by the machine M 1  on the running days D 1 -D 4 , respectively. Then, the first calculating unit  505  calculates an average of the data processing time “15 [minutes], 21 [minutes], 41 [minutes] and 11 [minutes]” for the identified job J 1 , so as to calculate predicted data processing time “22 [minutes]” for the job J 1 . 
     Further, the running days D 1 -DK identified on the basis of the amount of use of the CPU of the machine M may differ from the running days D 1 -DK identified on the basis of the amount of use of the I/O of the machine M in some cases in the identifying process run by the identifying unit  504  described above. When that is the case, calculate the predicted data processing time for the job J, e.g., as follows. 
     For example, the first calculating unit  505  calculates the predicted data processing time P 1  for the job J to be carried out by the machine M after the scheduled day and time on the basis of the data processing time for the job carried out on the running days D 1 -DK identified on the basis of the amount of use of the CPU of the machine M. Further, the first calculating unit  505  calculates the predicted data processing time P 2  for the job J to be carried out by the machine M after the scheduled day and time on the basis of the data processing time for the job J carried out on the running days D 1 -DK identified on the basis of the amount of use of the I/O of the machine M. Then, the first calculating unit  505  may choose the calculated predicted data processing time of a longer time length, either P 1  or P 2 , as the predicted data processing time for the job J. 
     A result of the calculation is stored, e.g., in the schedule table  400 . When, e.g., predicted data processing time “22 [minutes]” for the job J 1  is calculated, set “22” to the predicted data processing time field in the schedule information  400 - 1 . Further, e.g., set “10:22” to the predicted ending time field in the schedule information  400 - 1  so as to indicate time after the expected starting time of the job J 1  by the predicted data processing time. 
     The data processing time for the job J carried out on the respective running days D 1 -DK is identified from, although not limited to, the data processing time table  600  as explained above. The first calculating unit  505  may, e.g., directly ask the machine M and identify the processing time for the job J carried out on the respective running days D 1 -DK. 
     Further, the job management device  201  identifies running days D 1 -DK giving a sign that the load condition of the machine M is similar to the load condition until the scheduled day and time, although not limited to, by itself. The job management device  201  may, e.g., ask the information collecting device  202  and identify the running days D 1 -DK giving a sign that the load condition of the machine M is similar to the load condition until the scheduled day and time. 
     Return to  FIG. 5  for continued explanation. The output unit  509  has a function to output the calculated predicted data processing time for the job J to be carried out by the machine M after the scheduled day and time. For example, the output unit  509  may output schedule information related to the job to be processed in the schedule table  400 . Exemplary schedule information to be outputted will be explained. 
       FIG. 7  is a diagram illustrating an example of schedule information, according to an embodiment. As depicted in  FIG. 7 , schedule information  700  includes a job name of the job J 1  to be processed, a machine name of the machine M 1  to be loaded, a starting day, expected starting time, expected ending time, and predicted data processing time, related to one another. 
     A user may identify expected starting time, expected ending time, and predicted data processing time for the job J 1  which starts to be carried out on a starting day “May 18, 2011”, which are “10:00”, “10:15” and “22 minutes”, respectively, according to the schedule information  700 . Further, the user may be aware that the time “10:22” after the expected starting time of the job J 1  by the predicted data processing time is over the expected ending time of the job J 1 . The user may thereby take action such as to change the expected staring time of the job J 1  in order that the job J 1  finishes being carried out before the expected ending time. 
     Incidentally, the output unit  509  may output the predicted data processing time by indicating the output on the display monitor  308 , printing out the output through the printer  313 , and by sending the output to an external computer through the I/F  309 . Further, the output may be stored in a storage area in the RAM  303 , the magnetic disk  305 , the optical disk  307 , etc. 
     Return to  FIG. 5  for continued explanation. The deciding unit  506  has a function to decide whether the predicted ending time, i.e., after the expected starting time of the job J by the calculated predicted data processing time for the job J is over the expected ending time of the job J. The expected starting time and the expected ending time of the job J may be identified, e.g., from the schedule table  400 . 
     Let, e.g., the predicted data processing time for the job J 1  to be processed be “22 minutes”. In this case, the predicted ending time, i.e., after the expected starting time “10:00” by the predicted data processing time “22 minutes” is “10:22”. In this case, the deciding unit  506  decides that the predicted ending time “10:22” is over the expected ending time “10:15” of the job J 1 . 
     Further, the output unit  509  has a function to output a result of the decision having been made. For example, when the predicted ending time of the job J is over the expected ending time, the output unit  509  may output warning information indicating that the predicted ending time of the job J is over the expected ending time. An example of the warning information will be explained. 
       FIG. 8  is a diagram illustrating an example of warning information, according to an embodiment. As depicted in  FIG. 8 , warning information  800  indicates that the predicted ending time of the job J 1  to be processed is over the expected ending time. Specifically, e.g., the warning information  800  includes a job name of the job J 1 , a machine name of the machine M 1  to be loaded, a starting day, expected starting time, expected ending time, and predicted data processing time, related to one another. Further, the warning information  800  includes warning text indicating that the predicted ending time of the job J 1  is over the preset expected ending time by seven minutes. 
     According to the warning information  800 , the user may identify the expected starting time “10:00”, expected ending time “10:15”, and predicted data processing time “22 minutes” for the job J 1  which starts to be carried out on the starting day, May 18, 2011. Further, the user may be aware that the predicted ending time of the job J 1  is over the expected ending time by seven minutes. The user may thereby take action such as to change the expected staring time of the job J 1  in order that the job J 1  finishes being carried out before the expected ending time. 
     Return to  FIG. 5  for continued explanation. The changing unit  507  has a function to change the expected starting time of the job J in order that the predicted ending time of the job J is not over the expected ending time if the predicted ending time of the job J 1  were over the expected ending time without the change. For example, the changing unit  507  changes and brings forward the expected starting time of the job J by a time length by which the predicted ending time were over the expected ending time. 
     Further, the changing unit  507  may change the expected starting time of the job J in order that the predicted ending time is not over the expected ending time and that the time range in which the job J is carried out does not overlap a time range in which another job is carried out. Incidentally, what is specifically processed by the changing unit  507  will be described later by the use of  FIGS. 12-14 . 
     As exemplarily described above, the predicted ending time of the job J 1  is over the expected ending time by seven minutes. In this case, e.g., the changing unit  507  brings forward and changes the expected starting time of the job J 1  by seven minutes to “9:53”. A result of the change is stored, e.g., in the schedule table  400 . 
     An exemplary transition in what is stored in the schedule table  400  will be explained in an exemplary case where the expected starting time of the job J 1  is changed. 
       FIG. 9  is a diagram illustrating an example of transition in what is stored in a schedule table, according to an embodiment. A table ( 9 - 1 ) depicted in  FIG. 9  includes schedule information  400 - 1  in the schedule table  400  indicating the predicted processing time “22 minutes” and the predicted ending time “10:22” both predicted in a case where the machine M 1  to be loaded carries out the job J 1  to be processed. 
     In this case, the predicted ending time of the job J 1  is over the expected ending time by seven minutes as described above. Thus, the changing unit  507  brings forward and changes the expected starting time of the job J 1  by seven minutes to “9:53”. As a result, the expected starting time of the job J 1  is brought forward by seven minutes and changed from the former “10:00” to “9:53” on a table ( 9 - 2 ) depicted in  FIG. 9 . Further, the predicted ending time of the job J 1  is brought forward by seven minutes and changed from the former “10:22” to “10:15”. 
     Return to  FIG. 5  for continued explanation. The output unit  509  has a function to output the expected starting time of the job J having been changed. For example, the output unit  509  may output schedule information in which the expected starting time of the job J having been changed (e.g., schedule information  400 - 1  depicted in ( 9 - 2 ) in  FIG. 9 ) is set. 
     The user may thereby be aware of the expected starting time of the job J arranged in order that the predicted ending time of the job J is not over the expected ending time. Further, e.g. upon providing an existing scheduler with the expected starting time of the job J having been changed, the user may schedule the job J to finish being carried out before the expected ending time. 
     Further, when the predicted ending time of the job J is over the expected ending time, the changing unit  507  may decide whether the expected starting time may be brought forward. For example, the changing unit  507  decides whether the expected starting time of the job J brought forward by a time length by which the predicted ending time was over the expected ending time is at least after the present time. 
     When the expected starting time is after the present time, the changing unit  507  decides that the changing unit  507  may bring forward the expected starting time of the job J. Then, the changing unit  507  changes the expected starting time of the job J so that the predicted ending time of the job J is not over the expected ending time. 
     Meanwhile, when the expected starting time is before the present time, the changing unit  507  decides that the changing unit  507  may hardly bring forward the expected starting time of the job J. In this case, the output unit  509  may output warning information indicating that the predicted ending time of the job J is over the expected ending time (e.g., warning information  800  depicted in  FIG. 8 ). 
     The second calculating unit  508  has a function to calculate a progress value indicating to what extent the job J has progressed on the basis of the time when the job J started being carried out on that day, the present time, and the predicted processing time for the job J. For example, the second calculating unit  508  may calculate a progress percentage indicating to what extent the job J has progressed by using an equation (1) described below, where a variable F indicates the progress percentage of the job J. A variable P indicates the predicted processing time for the job J in minutes. A variable p indicates an interval between the time when the job J started and the present time in minutes.
 
 F =( p/P )×100  (1)
 
     Let, e.g., the time when the job J 1  started be “9:53”, the present time “10:00”, and the predicted processing time “P=22 [minutes]”. In this case, the interval between the time when the job J started and the present time is “p=7 [minutes]”. Thus, the progress percentage F of the job J 1  is “F=32 [%]=(7/22)×100”. 
     Further, the output unit  509  has a function to output a progress degree indicating to what extent the job J has progressed as calculated. For example, the output unit  509  may present a progress status screen  1000  on the display monitor  308 . Then, an example of the progress status screen  1000  will be explained. 
     (Exemplary Progress Status Screen) 
       FIG. 10  is a diagram illustrating an example of a progress status screen, according to an embodiment.  FIG. 10  depicts a progress status screen  1000  indicating progress status of the job J to be processed. For example, the progress status screen  1000  includes job names, progress percentages, expected ending time, and Gantt charts indicating progress status of respective jobs J 1 , J 3 , J 5 , and J 8 . 
     The progress status screen  1000  indicates, e.g., a progress percentage “32%” and expected ending time “10:15” of the job J 1 , and a Gantt chart  1001  indicating the progress status of the job J 1 . The user may be aware of the progress percentage “32%” of the job J 1  at the present time “10:00” according to such information. 
     Further, the progress status screen  1000  indicates a progress percentage “80%” and expected ending time “11:00” of the job J 3 , and a Gantt chart  1002  indicating the progress status of the job J 3 . The user may be aware of the progress percentage “80%” of the job J 3  at the present time “10:00” according to such information. 
     Further, the progress status screen  1000  indicates a progress percentage “100%” and expected ending time “10:00” of the job J 5 , and a Gantt chart  1003  indicating the progress status of the job J 5 . The user may be aware that the job J 5  finished being carried out sooner than expected at the present time “10:00” according to such information. 
     Further, the progress status screen  1000  indicates a progress percentage “0%” and expected ending time “11:15” of the job J 8 , a Gantt chart  1004  indicating the progress status of the job J 8 , and a piece of warning information  1005 . The job J 8  has not started to be carried out at the present time “10:00”. The warning information  1005  indicates that predicted ending time of the job J 8  is over its expected ending time. 
     According to the Gantt chart  1004  and the warning information  1005 , the user may intuitively know that the job J 8  will not finish being carried out before the expected ending time. Thus, the user may take action such as to change expected starting time of the job J 8  so that the job J 8  will finish being carried out before the expected ending time. 
     (What is Specifically Processed by the Selecting Unit  502 ) 
     Then, an example of what is specifically processed by the selecting unit  502  which selects the machine M to be loaded with the job J to be processed will be explained. The machines M 1 -Mn and the cloud environment  203  depicted in  FIG. 2  will be exemplarily explained as a group of machines to be conceivably loaded with the job J. 
     To begin with, the selecting unit  502  chooses one of the machines M 1 -Mn and the cloud environment  203  (called “candidate machine” hereafter). For example, the selecting unit  502  refers to priorities set to the respective machines and chooses a candidate machine of a high priority from the machines M 1 -Mn and the cloud environment  203 . 
     The priorities mentioned here indicate order in which the selecting unit  502  chooses a candidate machine from members of the group of machines to be conceivably loaded. The user may set the priorities at will, e.g., through input operations done on a setting screen  1100  depicted later in  FIG. 11 . 
     Then, the selecting unit  502  obtains an amount of work loaded onto the chosen candidate machine in the past. The amount of work loaded onto the candidate machine is, e.g., an average amount of use of the CPU or I/O of the candidate machine at particular time (e.g., a couple of years or months in the past). 
     For example, when the job J to be processed is carried out every Monday, the selecting unit  502  obtains an average amount of work loaded onto the candidate machine every Monday at the particular time from the information collecting device  202 . Further, when the job J to be processed is carried out on 25th every month, the selecting unit  502  obtains an average amount of work loaded onto the candidate machine on 25th every month at the particular time from the information collecting device  202 . 
     Then, the selecting unit  502  decides whether the obtained amount of work loaded onto the candidate machine is not smaller than a threshold of the amount of work set to the candidate machine. The threshold of the amount of work is a criterion for deciding whether the candidate machine is employed as the machine M to be loaded with the job J. The average amount of use of the CPU or I/O of each of the machines, e.g., may be set as the threshold of the amount of work of the candidate machine. 
     For example, the threshold of the amount of work may be set in such a way that the candidate machine is loaded too much and the processing time for the job J is extended upon the amount of work loaded onto the candidate machine being over the threshold. The user may set the threshold of the amount of work to each of the machines at will, e.g., through input operations done on a setting screen  1100  depicted later in  FIG. 11 . 
     When the amount of work loaded onto the candidate machine is smaller than the threshold, the selecting unit  502  selects the chosen candidate machine as the machine M to be loaded with the job J to be processed. Meanwhile, when the amount of work loaded onto the candidate machine is not smaller than the threshold, the selecting unit  502  chooses one of the machines M 1 -Mn and the cloud environment  203  having a high priority and not having been chosen, and repeats the consecutive process described above. 
     An example of the setting screen  1100  that is displayed on the display monitor  308  when setting the priority and the threshold of the amount of work of each of the machines will be explained. An exemplary case where the machine M to be loaded with the job J is selected from the machines M 1 -M 4  and the cloud environment  203  will be explained. 
       FIG. 11  is a diagram illustrating an example of a setting screen, according to an embodiment.  FIG. 11  depicts a setting screen  1100  on which the priority and the threshold of the amount of work loaded, for each of members of the group of the machines to be conceivably loaded, are set, and so is a cloud sizing threshold of the cloud environment  203 . 
     The setting screen  1100  includes a machine name field (column  1101  in  FIG. 11 ) representing an identifier of the machine to be conceivably loaded with the job J 1  to be processed. Incidentally, “cloud” is set in the setting screen  1100  as a machine name representing the cloud environment  203 . 
     The setting screen  1100  includes a priority field (column  1102  in  FIG. 11 ) representing order in which the candidate machine is chosen from the group of the machines to be conceivably loaded as described above. Let the priority be higher as it is indicated by a smaller number. Further, a symbol “x” is set to the priority of a particular machine to be excluded from the members that may be chosen. 
     The setting screen  1100  includes a field of the threshold of the amount of work (column  1103  in  FIG. 11 ) representing a criterion for deciding whether the candidate machine is employed as the machine M to be loaded with the job J as described above. An upper limit of an average amount of use of the CPU of each of the machines is set to the threshold of the amount of work in  FIG. 11 . 
     The cloud sizing threshold (area  1104  in  FIG. 11 ) is a threshold of an amount of work loaded onto the cloud environment  203  representing a criterion for deciding whether the cloud environment  203  is sized. An average amount of use of CPUs or I/Os of members of a group of servers included in the cloud environment  203 , e.g., is set to the threshold of the amount of work. 
     Upper and lower limits of the average amount of use of the CPUs of the members of the group of servers included in the cloud environment  203  are set to the clod sizing threshold in  FIG. 11 . When the amount of work loaded onto the cloud environment  203  is greater than the upper limit, e.g., increase processing capability of the cloud environment  203 . Meanwhile, when the amount of work loaded onto the cloud environment  203  is smaller than the lower limit, decrease the processing capability of the cloud environment  203 . 
     The user may set various kinds of values on the setting screen  1100  by using the keyboard  310  or the mouse  311 . In  FIG. 11 , e.g., values “1” and “50” are set to the priority of the machine M 1  and the threshold of the amount of work loaded onto the machine M 1 , respectively. Further, the symbol “×” is set to the priority of the machine M 2 , and to the threshold of the amount of work loaded onto the machine M 2 . 
     Further, values “2” and “50” are set to the priority of the machine M 3  and the threshold of the amount of work loaded onto the machine M 3 , respectively. Further, values “3” and “50” are set to the priority of the machine M 4  and the threshold of the amount of work loaded onto the machine M 4 , respectively. 
     Further, values “4” and “60” are set to the priority of the cloud environment  203  and the threshold of the amount of work loaded onto the cloud environment  203 , respectively. Further, values “50” and “20” are set to the upper and lower limits of the cloud sizing threshold, respectively. 
     Incidentally, although the average amount of use of the CPU is used as the threshold of each of the amounts of work, what is used as the threshold is not limited to the above. An average amount of use of the I/O, e.g., may be used as the threshold of each of the amounts of work, and so may both the average amounts of use of the CPU and of the I/O. 
     In  FIG. 11 , e.g., the selecting unit  502  chooses the machine M 1  of the highest priority as the candidate machine from the machine M 1 -M 4  and the cloud environment  203  at first. Then, the selecting unit  502  obtains the average amount of use of the CPU of the chosen machine M 1 . The job J 1  is carried out every Wednesday. 
     Thus, the selecting unit  502  obtains an average amount of use of the CPU of the machine M 1  on every Wednesday at particular time from the information collecting device  202 . Then, the selecting unit  502  decides whether the obtained average amount of use of the CPU of the machine M 1  is not smaller than the threshold of the amount of work “50” set to the machine M 1 . 
     When the average amount of use of the CPU of the machine M 1  is smaller than the threshold, the selecting unit  502  selects the chosen machine M 1  as the machine M to be loaded with the job J to be processed. Meanwhile, when the average amount of use of the CPU of the machine M 1  is not smaller than the threshold, the selecting unit  502  chooses a machine M 3  having a high priority and not having been chosen from the machines M 1 -Mn and the cloud environment  203 , and repeats the consecutive process described above. 
     The selecting unit  502  may thereby choose a machine M to be loaded with an amount of work being smaller than the preset threshold of the amount of work as the machine to be loaded with the job J to be processed. The job management device  201  may thereby avoid a situation in which the job J is allotted to the machine M to be loaded resulting in that the machine M is loaded too much and that the data processing time for the job J is extended. 
     Incidentally, how the cloud environment  203  is sized on the basis of the cloud sizing threshold will be described later by the use of a flowchart depicted in  FIG. 19 . 
     (What is Specifically Processed by the Changing Unit  507 ) 
     Then, an example of what is specifically processed by the changing unit  507  which changes the expected starting time of the job J to be carried out by the machine M in order that the predicted ending time of the job J is not over the expected ending time will be explained by the use of  FIGS. 12-14 . 
       FIG. 12  is a schematic diagram illustrating an example of a change in the expected starting time of the job J, according to an embodiment.  FIG. 12  depicts a Gantt chart  1201  indicating a time range in which the job J to be processed is carried out by the machine M. Further, a Gantt chart  1202  indicates a time range in which another job different from the job J to be processed is carried out by the machine M. 
     In  FIG. 12 , time t 1  is expected starting time of the different job, and time t 2  is expected (or predicted) ending time of the different job. Further, time t 3  is expected starting time of the job J to be processed. Time t 6  is predicted ending time of the job J to be processed. Time t 5  is expected ending time of the job J to be processed. 
     The predicted ending time of the job J is over the expected ending time t 5  in  FIG. 12 . The changing unit  507  changes the expected starting time of the job J so that an overlap degree between the job J and other jobs remains not so big and that the predicted ending time of the job J is not over the expected ending time t 5 . 
     For example, the changing unit  507  brings forward and changes the expected starting time of the job J by at least T 1  (in minutes), i.e., an interval between the expected ending time t 5  and the predicted ending time t 6  of the job J in order that the overlap degree between the job J and the other jobs remains not so big. Incidentally, the overlap degree means the number of jobs to be carried out in time ranges overlapping one another. 
     In  FIG. 12 , e.g., the expected starting time is brought forward by an interval T 1 ′ (T 1 ′&gt;T 1 ) and changed to time t 2 , i.e., expected ending time of the different job. In this case, predicted ending time t 4  of the job J is not over the expected ending time t 5  of the job J. Further, the overlap degree is zero as the time ranges in which the job J and the different job are carried out do not overlap each other. 
     The job management device  201  may change the expected starting time of the job J in such a way that the overlap degree with other jobs remains not so big and that the predicted ending time of the job J is not over the expected ending time as described above, so that the machine M can carry out the group of the jobs (job J and other jobs) efficiently. 
       FIG. 13  is a schematic diagram illustrating an example of a change in the expected starting time of the job J, according to an embodiment.  FIG. 13  depicts a Gantt chart  1301  indicating a time range in which the machine M carries out a job Ja. A Gantt chart  1302  indicates a time range in which the machine M carries out a job Jb. A Gantt chart  1303  indicates a time range in which the machine M carries out a job Jc. 
     The job Jc is a job whose expected starting time is to be changed. Further, the jobs Ja, Jb, and Jc are arranged in order for being carried out in such a way that the job Jb starts to be carried out after the job Ja finishes being carried out, and that the job Jc starts to be carried out after the job Jb finishes being carried out. 
     Before the expected starting time of the job Jc to be processed is changed as depicted on the upper side in  FIG. 13 , time t 3  is expected starting time of the job Ja, and time t 5  is expected (or predicted) ending time of the job Ja. Further, the time t 5  is expected starting time of the job Jb, and time t 6  is expected (or predicted) ending time of the job Jb. Further, the time t 6  is expected starting time of the job Jc, and time t 8  is predicted ending time of the job Jc. 
     In  FIG. 13 , time t 7  is expected ending time of the job Jc, and the predicted ending time t 8  of the job Jc is over the expected ending time t 7 . Further, as the expected ending time t 6  of the job Jb equals the expected starting time t 6  of the job Jc, it is impractical to bring forward only the expected starting time of the job Jc. 
     Thus, the changing unit  507  individually changes the expected starting time of the jobs Ja, Jb and Jc which are arranged in order for being carried out so that the predicted ending time of the job Jc is not over the expected ending time t 7 . For example, the changing unit  507  individually brings forward and changes the expected starting time of the jobs Ja, Jb and Jc by at least T 2  (in minutes), i.e., an interval between the expected ending time t 7  and the predicted ending time t 8  of the job Jc. 
     As depicted on the lower side in  FIG. 13 , e.g., the expected starting time of the job Ja is brought forward and changed to time t 1  by T 2  minutes, the expected starting time of the job Jb is brought forward and changed to time t 2  by T 2  minutes, and the expected starting time of the job Jc is brought forward and changed to time t 4  by T 2  minutes. In this case, the predicted ending time of the job Jc is the time t 7 , and is not over the expected ending time t 7  of the job Jc. 
       FIG. 14  is a schematic diagram illustrating an example of a change in the expected starting time of the job J, according to an embodiment.  FIG. 14  depicts a Gantt chart  1401  indicating a time range in which the machine M carries out a job Jd. A Gantt chart  1402  indicates a time range in which the machine M carries out a job Je. The jobs Jd and Je are not arranged in order for being carried out. The job Je is a job whose expected starting time is to be changed. 
     Before the expected starting time of the job Je is changed as depicted on the upper side in  FIG. 14 , time t 1  is expected starting time of the job Jd, and time t 2  is expected (or predicted) ending time of the job Jd. Further, the time t 2  is expected starting time of the job Je, and time t 3  is predicted ending time of the job Je. 
     In  FIG. 14 , the time t 2  is expected ending time of the job Je, and the predicted ending time t 3  of the job Je is over the expected ending time t 2 . Meanwhile, time t 4  is expected ending time of the job Jd, and the predicted ending time of the job Jd is not over the expected ending time t 4 . 
     In such a case, the changing unit  507  individually changes the expected starting time of the jobs Jd and Je so that the predicted ending time of the job Jd or Je is not over the expected ending time. For example, the changing unit  507  exchanges the expected starting time of the job Jd for the expected starting time of the job Je, and exchanges the expected starting time of the job Je for the expected starting time of the job Jd. 
     As depicted on the lower side in  FIG. 14 , e.g., the expected starting time of the job Jd is changed to the time t 2 , and the expected starting time of the job Je is changed to the time t 1 . In this case, the predicted ending time of the job Je is the time t 2 , and is not over the expected ending time t 2  of the job Je. Further, the predicted ending time of the job Jd is the time t 3 , and is not over the expected ending time t 4  of the job Jd. 
     (Procedure for Managing Jobs by Means of the Job Management Device  201 ) 
     Then a procedure for managing jobs by means of the job management device  201  of the second embodiment will be explained. 
       FIG. 15  is a diagram illustrating an example of an operational flowchart for managing jobs by means of a job management device, according to an embodiment. In the flowchart depicted in  FIG. 15 , choose a job J to be processed by means of the choosing unit  501  at first (step S 1501 ). Then, set priorities to respective members of a group of machines to be conceivably loaded with the job J by means of the job management device  201  (step S 1502 ). 
     Further, set a threshold of an amount of work to be loaded onto each of the members of the group of machines to be conceivably loaded with the job J by means of the job management device  201  (step S 1503 ). Further, set upper and lower limits Cmax and Cmin, respectively, of an amount of work loaded onto the cloud environment  203  as a cloud sizing threshold for the cloud environment  203  by means of the job management device  201  (step S 1504 ). 
     Then, carry out a process for selecting a machine M to be loaded with the job J to be processed by means of the selecting unit  502  (step S 1505 ). Then, carry out a process for calculating predicted processing time for the job J to be processed by means of the first calculating unit  505  (step S 1506 ). 
     Then, carry out a process for changing expected starting time of the job J to be processed by means of the changing unit  507  (step S 1507 ). Finally, carry out a process for sizing the cloud environment  203  by means of the job management device  201  (step S 1508 ), and finish the consecutive process according to the flowchart. 
     The expected starting time of the job J may thereby be changed in order that the predicted ending time of the job J is not over the expected ending time. 
     Incidentally, the various kinds of values may be set at the steps S 1502 -S 1504 , e.g., through input operations done by the user on the setting screen  1100  depicted in  FIG. 11 , or automatically according to a preset schedule. 
     (Procedure for Selecting Machine to be Loaded) 
     Then, a specific procedure for the process for selecting a machine to be loaded at the step S 1505  will be explained. Let a group of machines to be conceivably loaded with the job J be the machines M 1 -Mn and the cloud environment  203  depicted in  FIG. 2 . 
       FIG. 16  is a diagram illustrating an example of an operational flowchart for selecting a target computer to be loaded, according to an embodiment. In the flowchart depicted in  FIG. 16 , sort members of a group of machines to be conceivably loaded with the job J in descending order of priorities by means of the selecting unit  502  at first (step S 1601 ). 
     As explained below, suppose that the selecting unit  502  sorts the machines M 1 -Mn and the cloud environment  203  in descending order of priorities into what is written as “machines M[ 0 ]-M[n]”. Further, any one of the machines M[ 0 ]-M[n] is written as a “machine M[i]” (i is an integer 0 through n). 
     Then, set “i=0” for the machine M[i] (step S 1602 ), and choose the machine M[i] from the machines M[ 0 ]-M[n] by means of the selecting unit  502  (step S 1603 ). Incidentally, when a symbol “×” is set as the priority of the machine M[i], the selecting unit  502  shifts to a step S 1609  without choosing the machine M[i] (remark that the priority “×” is treated as the lowest priority). 
     Then, obtain an average amount of work w[i] of the machine M[i] at particular time (e.g., a couple of years or months in the past) from the information collecting device  202  by means of the selecting unit  502  (step S 1604 ). Then, decide whether the obtained average amount of work w[i] of the machine M[i] is a threshold W[i] of an amount of work set to the machine M[i] and over by means of the selecting unit  502  (step S 1605 ). 
     When the average amount of work w[i] of the machine M[i] is smaller than the threshold W[i] (step S 1605 : No), select the machine M[i] as the machine to be loaded with the job J by means of the selecting unit  502  (step S 1606 ), and shift to the step S 1506  depicted in  FIG. 15 . 
     Meanwhile, when the average amount of work w[i] of the machine M[i] is the threshold W[i] and over (step S 1605 : Yes), incrementally update “i” of the machine M[i] by means of the selecting unit  502  (step S 1607 ). Then, decide whether “i” of the machine M[i] is greater than “n” by means of the selecting unit  502  (step S 1608 ). 
     When “i” of the machine M[i] is not greater than “n” (step S 1608 : No), return to the step S 1603 . Meanwhile, when “i” of the machine M[i] is greater than “n” (step S 1608 : Yes), identify, from the machines M[ 0 ]-M[n], a machine M[i] whose additional portion of the average amount of work is a minimum by means of the selecting unit  502  (step S 1609 ), and shift to the step S 1606 . 
     Incidentally, the additional portion of the average amount of work is a difference between the average amount of work w[i] loaded onto the machine M[i] and the threshold W[i] of the amount of work loaded onto the machine M[i]. 
     Thus, the machine M whose average amount of work at particular time is smaller than a preset threshold of the amount of work may thereby be chosen. 
     (Procedure for Calculating Predicted Data Processing Time) 
     Then, a procedure for calculating predicted data processing time at the step S 1506  depicted in  FIG. 15  will be explained. 
       FIG. 17  is a diagram illustrating an example of an operational flowchart for calculating predicted data processing time, according to an embodiment. At first in the operational flowchart depicted in  FIG. 17 , obtain load information indicating average amounts of use of the CPU and the I/O of the machine M in a time range until the scheduled day and time when the job J to be processed starts to be carried out by means of the obtaining unit  503  (step S 1701 ). 
     Then, obtain load information indicating average amounts of use of the CPU and the I/O of the machine M in the same time range as the time range until the scheduled day and time, on each of running days when the job J was carried out by the machine M at particular time, by means of the obtaining unit  503  (step S 1702 ). 
     Then, identify one of the running days of the job J giving a sign that the average amount of use of the CPU of the machine M in the same time range as the time range until the scheduled day and time is similar to the average amount of use of the CPU of the machine M in the time range until the scheduled day and time, by means of the identifying unit  504  (step S 1703 ). The identifying unit  504  carries out the process at the step S 1703  on the basis of the load information obtained at the steps S 1701  and S 1702 . 
     Then, identify data processing time for the job J carried out by the machine M on each of the identified running days, by means of the first calculating unit  505  (step S 1704 ). Then, calculate an average length of the data processing time for the job J carried out on the respective identified running days so as to calculate data processing time P 1  for the job J, by means of the first calculating unit  505  (step S 1705 ). 
     Then, identify one of running days of the job J giving a sign that the average amount of use of the I/O of the machine M in the same time range as the time range until the scheduled day and time is similar to the average amount of use of the I/O of the machine M in the time range until the scheduled day and time by means of the identifying unit  504  (step S 1706 ). The identifying unit  504  carries out the process at the step S 1706  on the basis of the load information obtained at the steps S 1701  and S 1702 . 
     Then, identify data processing time for the job J carried out by the machine M on each of the identified running days, by means of the first calculating unit  505  (step S 1707 ). Then, calculate an average length of the data processing time for the job J carried out on the respective identified running days so as to calculate data processing time P 2  for the job J by means of the first calculating unit  505  (step S 1708 ). 
     Then, decide whether the calculated predicted data processing time P 1  is not shorter than the predicted data processing time P 2 , by means of the first calculating unit  505  (step S 1709 ). When the predicted data processing time P 1  is not shorter than the predicted data processing time P 2  (step S 1709 : Yes), let the predicted data processing time P 1  be the data processing time for the job by means of the first calculating unit  505  (step S 1710 ), and shift to the step S 1507  depicted in  FIG. 15 . 
     Meanwhile, when the predicted data processing time P 1  is shorter than the predicted data processing time P 2  (step S 1709 : No), let the predicted data processing time P 2  be the data processing time for the job J by means of the first calculating unit  505  (step S 1711 ), and shift to the step S 1507  depicted in  FIG. 15 . 
     The job management device  201  may thereby calculate predicted data processing time for the job J on the scheduled day on the basis of the data processing time for the job J carried out in the past in load condition similar to the load condition of the machine M in the time range until the scheduled day and time. 
     Procedure for Changing Process 
     Then, a specific procedure for the changing process at the step S 1507  depicted in  FIG. 15  will be explained. 
       FIG. 18  is a diagram illustrating an example of an operational flowchart for changing a starting time of a job, according to an embodiment. At first in the flowchart in  FIG. 18 , decide whether the predicted ending time after the expected starting time of the job J by the calculated predicted processing time for the job J is over the expected ending time of the job J, by means of the deciding unit  506  (step S 1801 ). 
     When the predicted ending time of the job J is not over the expected ending time (step S 1801 : No), shift to the step S 1508  depicted in  FIG. 15 . Meanwhile, when the predicted ending time of the job J is over the expected ending time (step S 1801 : Yes), decide whether the expected starting time of the job J is allowed to be brought forward, by means of the changing unit  507  (step S 1802 ). 
     When the expected starting time of the job J is allowed to be brought forward (step S 1802 : Yes), change the expected starting time of the job J in order that the predicted ending time of the job J is not over the expected ending time, by means of the changing unit  507  (step S 1803 ). 
     Then, after changing the expected starting time of the job J, decide whether running time ranges in which the job J and another different job are carried out resultantly overlap each other, by means of the changing unit  507  (step S 1804 ). When the running time ranges do not overlap with the different job (step S 1804 : No), shift to the step S 1508  depicted in  FIG. 15 . 
     Meanwhile, when the running time ranges overlap with the different job (step S 1804 : Yes), decide whether there is a job related to the different job, by means of the changing unit  507  (step S 1805 ). Incidentally, related information for identifying a related group of jobs is included, e.g., in the schedule information  400 - 1  through  400 - m  depicted in  FIG. 4 . 
     When there is a related job (step S 1805 : Yes), change expected starting time of each of jobs in the related group of jobs, by means of the changing unit  507 , in order that the predicted ending time of each of the jobs is not over the expected ending time (step S 1806 ). Then, shift to the step S 1508  depicted in  FIG. 15 . 
     Meanwhile, when there is no related job (step S 1805 : No), change the expected starting time of the different job, by means of the changing unit  507 , in order that the predicted ending time of the different job is not over the expected ending time (step S 1807 ), and shift to the step S 1508  depicted in  FIG. 15 . 
     Further, when it is impractical to bring forward the expected starting time of the job J at the step S 1802  (step S 1802 : No), output warning information indicating that the predicted ending time of the job J is over the expected ending time, by means of the output unit  509  (step S 1808 ). Then, shift to the step S 1508  depicted in  FIG. 15 . 
     The expected starting time of the job J may thereby be changed in order that the overlap degree with the different job does not grow and that the predicted ending time of the job J is not over the expected ending time. 
     Incidentally, when it is impractical to change the expected starting time of each of the jobs in the related group of jobs at the step S 1806  in order that the predicted ending time of each of the jobs is not over the expected ending time, the output unit  509  may output warning information indicating such condition. 
     When it is similarly impractical to change the expected starting time of the different job at the step S 1807  in order that the predicted ending time of the different job is not over the expected ending time, the output unit  509  may output warning information indicating such condition. 
     (Procedure for Sizing Process) 
     Then, a specific procedure for the sizing process at the step S 1508  depicted in  FIG. 15  will be explained. 
       FIG. 19  is a diagram illustrating an example of an operational flowchart for a sizing process, according to an embodiment. At first in the operational flowchart in  FIG. 19 , obtain an average amount of work C loaded onto a group of servers included in the cloud environment  203  (average amount of use of CPUs in this case) from the information collecting device  202 , by means of the job management device  201  (step S 1901 ). 
     Incidentally, the group of servers included in the cloud environment  203  means operating system (OS) instances in the cloud environment  203 . 
     Then, decide whether the obtained average amount of work C loaded onto the cloud environment  203  is not smaller than the upper limit Cmax of the cloud sizing threshold (step S 1902 ). When the average amount of work C is not smaller than the upper limit Cmax (step S 1902 : Yes), decide whether the CPU capability of the OS instances is a maximum, by means of the job management device  201  (step S 1903 ). 
     When the CPU capability of the OS instances is not a maximum (step S 1903 : No), increase the CPU capability of the OS instances by means of the job management device  201  (step S 1904 ), and finish the consecutive process according to the flowchart. 
     Meanwhile, when the CPU capability of the OS instances is a maximum (step S 1903 : Yes), increase the number of the OS instances by means of the job management device  201  (step S 1905 ), and finish the consecutive process according to the flowchart. 
     Further, when the average amount of work C is smaller than the upper limit Cmax at the step S 1902  (step S 1902 : No), decide whether the obtained average amount of work C of the cloud environment  203  is not greater than the lower limit Cmin of the cloud sizing threshold, by means of the job management device  201  (step S 1906 ). 
     When the average amount of work C is not greater than the lower limit Cmin (step S 1906 : Yes), decide whether the CPU capability of the OS instances is a minimum (step S 1907 ). When the CPU capability of the OS instances is a minimum (step S 1907 : Yes), decrease the number of the OS instances by means of the job management device  201  (step S 1908 ), and finish the consecutive process according to the flowchart. 
     Meanwhile, when the CPU capability of the OS instances is not a minimum (step S 1907 : No), decrease the CPU capability of the OS instances by means of the job management device  201  (step S 1909 ), and finish the consecutive process according to the flowchart. 
     Further, when the average amount of work C is greater than the lower limit Cmin at the step S 1906  (step S 1906 : No), finish the consecutive process according to the flowchart. 
     The job management device  201  may thereby increase or decrease the number of OS instances or the CPU capability in accordance with the work loaded onto the cloud environment  203 , so that a service provided by a job allotted to the cloud environment  203  do not delay and that wasteful use of computing resources is controlled. 
     Incidentally, although an average amount of use of the CPUs is used as an average amount of work C loaded onto the group of servers included in the cloud environment  203  as explained above, an average amount of use of the I/Os may be used instead. In this case, what is increased or decreased in time of sizing the cloud environment  203  is memories and OS instances. 
     (Machine Replacement) 
     Then, machine replacement in the scheduling system  200  will be explained. The machine replacement means replacing an old or broken machine with a new machine. 
       FIG. 20  is a schematic diagram illustrating an example of an operation for machine replacement, according to an embodiment. The upper half ( 20 - 1 ) in  FIG. 20  depicts the machines M 1 -M 4  and the cloud environment  203 . Let the machine M 2  be a machine to be replaced. 
     In this case, set the symbol “×” to the priorities of the machines M 1 -M 4  on the setting screen  1100  concerning a group of jobs used to be expected to be carried out by the machine M 2  to be replaced, so that the machines M 1 -M 4  are excluded from candidates conceivably loaded with the group of jobs. 
     Further, set “1” to the priority of the cloud environment  203  on the setting screen  1100  concerning the group of jobs used to be expected to be carried out by the machine M 2  to be replaced. As a result, the job management device  201  may allot the group of jobs to the cloud environment  203  so that predicted ending time of each of the jobs in the group that are expected to be carried out by the machine M 2  is not over its expected ending time (see the lower half ( 20 - 2 ) in  FIG. 20 ). 
     Let the cloud environment  203  temporarily carry out the group of jobs expected to be carried out by the machine M 2  to be replaced, in time of machine replacement in the operation environment, in this way, so that an environmental change in time of machine replacement is flexibly dealt with. 
     (Shift to Cloud) 
     Then, a case where a group of jobs used to be expected to be carried out by the machines M 1 -M 4  in the scheduling system  200  is shifted to the cloud environment  203  will be explained. 
       FIGS. 21 and 22  are diagrams illustrating an example of operations in time of a shift to a cloud environment, according to an embodiment. The machines M 1 -M 4  and the cloud environment  203  are depicted in ( 21 - 1 ) in  FIG. 21 . 
     Let the machine M 4  be an object of the shift. In this case, set the symbol “×” to the priorities of the machines M 1 -M 4  and set “1” to the priority of the cloud environment  203  on the setting screen  1100  concerning a group of jobs used to be expected to be carried out by the machine M 4  to be an object of the shift. 
     As a result, the job management device  201  may allot the group of jobs to the cloud environment  203  so that predicted ending time of each of the jobs in the group used to be expected to be carried out by the machine M 4  is not over its expected ending time (see ( 21 - 2 ) in  FIG. 21 ). 
     Then, let the machine M 3  be an object of the shift. In this case, set the symbol “×” to the priorities of the machines M 1 -M 4  and set “1” to the priority of the cloud environment  203  on the setting screen  1100  concerning a group of jobs used to be expected to be carried out by the machine M 3  to be an object of the shift. 
     As a result, the job management device  201  may allot the group of jobs to the cloud environment  203  so that predicted ending time of each of the jobs in the group used to be expected to be carried out by the machine M 3  is not over its expected ending time (see ( 21 - 3 ) in  FIG. 21 ). 
     Then, let the machine M 2  be an object of the shift. In this case, set the symbol “×” to the priorities of the machines M 1 -M 4  and set “1” to the priority of the cloud environment  203  on the setting screen  1100  concerning a group of jobs used to be expected to be carried out by the machine M 2  to be an object of the shift. 
     As a result, the job management device  201  may allot the group of jobs to the cloud environment  203  so that predicted ending time of each of the jobs in the group used to be expected to be carried out by the machine M 2  is not over its expected ending time (see ( 21 - 4 ) in  FIG. 22 ). 
     Then, let the machine M 1  be an object of the shift. In this case, set the symbol “×” to the priorities of the machines M 1 -M 4  and set “1” to the priority of the cloud environment  203  on the setting screen  1100  concerning a group of jobs used to be expected to be carried out by the machine M 1  to be an object of the shift. 
     As a result, the job management device  201  may allot the group of jobs to the cloud environment  203  so that predicted ending time of each of the jobs in the group used to be expected to be carried out by the machine M 1  is not over its expected ending time (see ( 21 - 5 ) in  FIG. 22 ). 
     All the jobs used to be expected to be carried out by the machines M 1 -M 4  in the scheduling system  200  are shifted to the cloud environment  203 , in this way, by gradually increasing a percentage of the jobs to be carried out by the cloud environment  203 , so that the shift to the cloud is seamlessly achieved. 
     The job management device  201  of the second embodiment described above may calculate the predicted data processing time for the job J on the scheduled day on the basis of the data processing time for the job J carried out on a running day in the past giving a sign that the load condition of the machine M is similar to the load condition on the scheduled day in the same time range as the time range until the scheduled day and time. 
     Further, the job management device  201  may identify running days D 1 -DK each giving a sign that a difference between an average amount of work loaded onto the machine M in the time range until the scheduled day and time and an average amount of work loaded onto the machine M in the same time range on the running days in the past when the job J was carried out is not greater than a threshold. The job management device  201  may thereby identify the running days in the past when the job J was carried out in load condition similar to the load condition in the time range until the scheduled day and time. 
     Further, the job management device  201  may identify running days D 1 -DK each giving a sign that a difference between an average amount of work loaded onto the machine M in the time range until the scheduled day and time and an average amount of work loaded onto the machine M in the same time range on the running days in the past when the job J was carried out has one of the smallest K values. The job management device  201  may identify the running days in the past when the job J was carried out in load condition similar to the load condition in the time range until the scheduled day and time. 
     Further, when the predicted ending time after the expected starting time of the job J by the predicted processing time for the job J is over the expected ending time of the job J, the job management device  201  may output warning information indicating that the predicted ending time of the job J is over the expected ending time. The user may thereby be aware that the predicted ending time of the job J is over the expected ending time, and may take action such as to change expected starting time of the job J so that the job J will finish being carried out before the expected ending time. 
     Further, when the predicted ending time of the job J is over the expected ending time, the job management device  201  may change the expected starting time of the job J to be carried out at that time. The user may thereby be aware of the expected starting time of the job J after which the predicted ending time of the job J is not over the expected ending time. Further, the job management device  201  may provide an existing scheduler with the expected starting time of the job J having been changed so as to schedule the job J to finish being carried out before the expected ending time. 
     Further, the job management device  201  may calculate a progress percentage of a job J that has started being carried out on that day, on the basis of starting time of the job J, the present time, and predicted data processing time for the job J, and present the calculated progress percentage on the progress status screen  1000 . The user may thereby be aware of the progress percentage of the job J. 
     Further, the job management device  201  may choose a machine M to be loaded with a job J to be processed on the basis of priorities set to respective machines to be conceivably loaded with the job J. The user may thereby operate the scheduling system  200  in such a way that a machine M of high performance carries out a job J being important and that a machine M of low performance carries out a job J being not so important. 
     Further, the job management device  201  may choose a machine M whose amount of work is smaller than a preset threshold of the amount of work as the machine to be loaded with the job J to be processed. The job management device  201  may thereby avoid a situation in which the job J is allotted to the machine M to be loaded resulting in that the machine M is loaded too much and that the data processing time for the job J is extended. 
     Further, set a lower priority to the cloud environment  203  than priorities set to the machines M 1 -Mn in an on-premises environment, so that the cloud environment  203  is operated to carry out the job J only in a case where the machines in the on-premises environment are heavily loaded. As a result, use of the cloud environment  203  may be charged at a rate as low as possible and the charge may be minimized. 
     It may be concluded from the above that the job management device  201  may optimize the expected starting time of the job J in accordance with load condition of the machine M which changes depending upon an amount of work or data size which changes day by day so that the job J finishes being carried out within specified time. 
     Incidentally, the prediction method of the embodiment explained above may be implemented by means of a prepared program run on a computer such as a personal computer or a workstation. The prediction program may be recorded on a computer-readable recording medium such as a hard disk, a flexible disk, a CD-ROM, an MO, a DVD, etc. The prediction program may be read from the recording medium and run by the computer. Further, the prediction program may be distributed via a network such as the Internet. 
     All examples and conditional language recited herein are intended for pedagogical purposes to aid the reader in understanding the invention and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions, nor does the organization of such examples in the specification relate to a showing of the superiority and inferiority of the invention. Although the embodiments of the present invention have been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention.

Technology Classification (CPC): 6