Abstract:
The present invention relates to a method of assigning objects to processing units of a cluster of processing units, each one of the objects having an object size and an object load, each one of the processing units having a storage capacity and a load capacity, the method comprising the steps of:
       a) calculating an index based on object size and object load for each one of the objects,   b) sorting of the objects by index to provide a sequence of objects;   c) for each processing unit of the cluster:
           assigning of one or more of the objects to the processing unit in sequential order until a remaining storage capacity and/or a remaining load capacity of the processing unit is too small for consecutive objects of the sequence;   deleting of the objects that are assigned to the processing unit from the sequence.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This is a 371 national phase application of PCT/EP2004/009102, filed Aug. 13, 2004, claiming priority of European Patent Application No. 03 026 773.6, filed Nov. 21, 2003, which is incorporated herein by reference. 
     FIELD OF THE INVENTION 
     The present invention relates to the field of data processing, and more particularly without limitation, to balancing the assignment of objects in a multi-computing environment. 
     BACKGROUND AND PRIOR ART 
     Various multi-computing architectures are known from the prior art where a plurality of processing units is coupled to form a cluster. Such architectures are used in parallel processing and also in the emerging field of blade computing. 
     Blade computing relies on blade servers, which are modular, single-board computers. An overview of blade computing is given in “Architectures and Infrastructure for Blade Computing”, September 2002, Sun Microsystems and “THE NEXT WAVE: BLADE SERVER COMPUTING”, Sun Microsystems (www.sun.com/servers/entry/blade). 
     A content load balancing blade is commercially available from Sun microsystems (“Sun Fire™ B10n). This blade provides traffic and content management functionalities. Content load balancing is achieved based on URLs, CGI scripts and cookies; server load balancing is achieved based on server loads, response times, and weighted round-robin algorithms. 
     US patent application no. 20030105903 shows a web edge server, which comprises a number of blade servers. A switch and an information distribution module are provided for the purpose of balancing. The information distribution module receives an information message, performs processing on the message to determine a destination, and forwards a message toward the determined destination via an internal communications network. 
     SUMMARY OF THE INVENTION 
     The present invention provides for a method of assigning objects to processing units of a cluster of processing units. Each one of the processing units has a certain storage capacity and load capacity. The storage capacity of a processing unit determines the maximum aggregated size of objects that can be stored by the processing unit. The load capacity of the processing unit determines the maximum processing load that the processing unit can handle. 
     For example, the load capacity of a processing unit can be indicative of the maximum number of access operations the processing unit can provide. Access operations may comprise both read accesses (select) and write accesses (update, insert, delete) to objects stored on the processing unit. For example the load capacity can be expressed as the maximum number of access operations per time unit the processing units can handle. 
     In order to make maximum usage of the available data processing capacity provided by the cluster of processing units the distribution of the objects over the processing units needs to be balanced. This is accomplished by calculating an index for each object based on object size and object load. For example, the object load is expressed as the expected mean number of access operations per time unit to the object. The objects are sorted by index in order to provide a sequence. 
     In the following it assumed without restriction of generality that the sequence is sorted in descending order. In this instance the procedure for assigning of objects to processing units starts with the first object of the sequence. One or more of the objects of the sequence are assigned to one processing unit in sequential order until a remaining storage capacity and/or a remaining load capacity of that processing unit is too small for consecutive objects of the sequence. When this condition is fulfilled, the procedure is carried out for the next processing unit, whereby the objects that have been previously assigned to the preceding processing unit are deleted from the sequence. This way a minimum number of processing units that are required for handling a given set of objects can be determined. 
     In accordance with a preferred embodiment of the invention each processing unit is a single-board computer that has a bus interface to a bus system that couples a plurality of the single-board computers. Each of the single-board computers has its private processing and data storage resources. Data processing tasks or sub-tasks of a complex data processing task are assigned to the single-board computers by a control unit. The control unit can be a separate hardware unit or a software process that runs on one of the single-board computers. An example of such a distributed data processing system is a cluster of blades. 
     In accordance with a preferred embodiment of the invention the remaining storage capacity of a processing unit is determined by the difference between the storage capacity of the unit and the aggregated size of the objects that have been assigned to the processing unit. Likewise the remaining load capacity of a processing unit is determined by the difference between the load capacity of the unit and the aggregated loads of objects that have been assigned to the processing unit. On the basis of these definitions of the remaining storage capacity and of the remaining load capacity the minimum number of processing units is determined. 
     In accordance with a further preferred embodiment of the invention the balancing procedure is performed again in order to further improve the quality of the balancing. For this purpose the largest gap between the aggregated sizes of objects being assigned to one of the processing units and the largest gap between the aggregated loads of objects being assigned to one of the processing units and the load capacity are determined. 
     The size gap is divided by the minimum number of processing units and the result of the division is subtracted from the maximum storage capacity to provide a size threshold level. Likewise, the load gap is divided by the number of processing units and the result of the division is subtracted from the load capacity to provide a load threshold level. When the procedure for assigning the objects to the processing units is performed again, the definition of the remaining storage capacity is the difference between the aggregated size of objects being assigned to the processing unit and the size threshold level whereas the definition of the remaining load capacity is the difference between the aggregated load of the objects being assigned to the processing unit and the load threshold level. As a result of the renewed performance of the assignment procedure, the gap can be substantially reduced. 
     In accordance with a further preferred embodiment of the invention the theoretical storage capacity limit is used as a size threshold. This size threshold is obtained by calculating the difference between the total of the storage capacities of the processing units and the total of the sizes of the objects and dividing the difference by the minimum number of processing units. The result of the division is subtracted from the storage capacity which provides the theoretical limit. 
     Likewise the theoretical load capacity limit is used as a load threshold. This load threshold is obtained by calculating the difference between the total of the load capacities of the processing units and the total of the loads of the objects and dividing the difference by the minimum number of processing units. The result of the division is subtracted from the load capacity which provides the theoretical load capacity limit. 
     On this basis the assignment procedure is performed again whereby the remaining storage capacity is defined as the difference between the aggregated size of the objects of the processing unit and the size threshold whereas the remaining load capacity is defined a the difference between the aggregated load of the objects of the processing units and the load threshold. Typically it will not be possible to assign all of the objects to the minimum number of processing units on this basis. If this is the case one or more iterations are performed. 
     For one iteration an excess amount of memory is divided by the minimum number of processing units. The result of the division is added to the size threshold. Likewise an excess load is divided by the minimum number of processing units. The result of the division is added to the load threshold. On the basis of the incremented size threshold and/or load threshold the assignment procedure is performed again. This process continues until all objects have been assigned to the minimum number of processing units. This way the quality of the balancing is further improved. 
     In according with a further preferred embodiment of the invention the size threshold for performing the assignment procedure is varied between the theoretical storage capacity limit and the actual storage capacity. Likewise the load threshold is varied between the theoretical load capacity limit and the actual load capacity. Preferably a new assignment procedure is performed for each permutation of the size threshold/load threshold that can be thus obtained. For each of the resulting assignments of objects to processing units a statistical measure is calculated. This statistical measure is a basis to select one of the assignments for optimal balancing. 
     In according with a further preferred embodiment of the invention the standard deviation or variance of the sum of the indices of objects assigned to a processing unit is used as a statistical measure. The assignment having the lowest overall quality measure is selected. 
     In accordance with a preferred embodiment of the invention the object sizes and object loads are normalised for the calculation of the indices. Preferably an index of an object is calculated on the basis of the sum of the normalised object size and normalised object load and the absolute difference of the normalised object size and normalised object load. Preferably the index is obtained by calculating a linear combination of the sum of the normalised object size and normalised object load and the absolute value of the difference of the normalised object size and normalised object load. 
     In accordance with a preferred embodiment of the invention each one of the processing units is a blade or a blade server. One of the blades can have a program that implements the principles of the present invention in order to perform balancing. This way the number of swap-operations between the blades can be minimised. 
     In accordance with a preferred embodiment of the invention the principles of the invention are implemented in an application program running on a personal computer. The application program is provided with a list of objects and the estimated sizes and loads of the objects that need to be handled by the cluster of processing units. On the basis of the object sizes and the object loads the minimum number of processing units that are required for the processing task are determined. This information can form the basis for a corresponding investment decision of a customer. 
     It is to be noted that the present invention is not restricted to a particular type of object. For example, data objects such as tables, arrays, lists and trees are distributed to processing units, e.g. blades, in accordance with the principles of the present invention. For example, each one of the processing units runs a data processing task to which the respective objects are assigned. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the following preferred embodiments of the invention will be described in greater detail by way of example only, by making reference to the drawings in which: 
         FIG. 1  is a schematic block diagram of a modular computer system having a cluster of blades, 
         FIG. 2  is illustrative of a flow diagram for assigning of objects to blades and for determining the minimum number of blades, 
         FIG. 3  is an example for tables that need to be assigned to blades, 
         FIG. 4  shows the normalised size distribution of the tables of  FIG. 3 , 
         FIG. 5  shows the loads of the tables in terms of the mean number of read accesses per second, 
         FIG. 6  shows the normalised loads, 
         FIG. 7  shows the normalised sizes and loads per table, 
         FIG. 8  shows a sorted sequence of the tables, 
         FIG. 9  shows the sorted sequence of the tables illustrating the normalised sizes and loads of each table, 
         FIGS. 10 to 14  show the assignment of tables to blade  1  of the cluster of blades 
         FIGS. 15 and 16  show the assignment of tables to blade  2 , 
         FIG. 17  shows the resulting assignment of tables to blades that is obtained on the basis of the method of  FIG. 2 , 
         FIG. 18  is illustrative of a preferred embodiment of the invention where the procedure of  FIG. 2  is performed again with lower size and load thresholds, 
         FIG. 19  is illustrative of a preferred embodiment of a method of the invention where the load and size thresholds are varied iteratively, 
         FIG. 20  is illustrative of a further preferred embodiment of the invention where the size and load thresholds are varied in predetermined steps, 
         FIG. 21  is illustrative of a computer system performing the assignment of objects to blades. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  shows cluster  100  of blades B 1 , B 2 , B 3 , . . . BN. Each one of the blades has processor  102  and memory  104 . In the example considered here, all memories  104  have the same storage capacity and all blades have the same load capacity. The blades are coupled by a network  106 , such as a bus system. The number N of blades of cluster  100  needs to be chosen such that a given number of M objects of varying sizes and loads can be handled. 
     For example, cluster  100  implements a so-called search engine. In this instance identical search processes run on each one of the blades. The assignment of data objects, such as index tables, to blades can be stored in a dispatcher unit (not shown on the drawing) of cluster  100 . This way data objects are assigned to blades and data processing tasks running on the blades. 
       FIG. 2  shows a procedure for assigning the objects to blades and to thereby determine the minimum value for N. 
     In step  200  an sorting index is calculated for each one of the M objects. An sorting index of an object is indicative of the amount of blade resources the object requires. The sorting index serves to sort the objects in decreasing order of blade resource requirements. 
     For example the sorting index is calculated on the basis of the sum of the normalised object load and normalised object size plus the absolute value of the difference of the normalised load and size or a linear combination thereof. 
     In step  201  a sorting operation is performed in order to sort the M objects by sorting index. The corresponding object sequence is provided in step  202 . In step  204  the index i for the blades is initialised to 1. 
     In step  206  processing of the object sequence starts with the first object of the sequence, i.e. the object having the largest sorting index value. The first object of the sequence is assigned to a first one of the blades, i.e. blade B 1 , in step  206 . In step  208  the first object that has been assigned to blade B 1  is deleted from the sequence. 
     In step  210  the sizes of the objects that have already been assigned to blade B 1  are summed up in order to provide an aggregated object size of blades B 1 . Next the size of a gap G S  between the aggregated object size of blade B 1  and a size threshold T S  is calculated. When the assignment procedure of  FIG. 2  is carried out the first time, the size threshold T S  is the storage capacity of one of the blades. 
     In step  211  the loads of the objects that have already been assigned to blade B 1  are summed up in order to provide an aggregated load of blade B 1 . Next a gap G L  between the aggregated object loads of blade B 1  and a load threshold T L  is calculated. When the assignment procedure of  FIG. 2  is carried out the first time, the load threshold T L  is the load capacity of one of the blades. 
     In step  212  it is determined whether there is a next object in the ordered sequence that fits into both gaps G S  and G L . In other words, a consecutive object following the first object in the object sequence that has an object size small enough to fit into gap G S  and at the same time has an object load that is small enough to fit into G L  is searched. 
     The next consecutive object in the sequence that fulfils this condition is assigned to blade B 1  in step  214  and deleted from the sequence in step  216  before the control goes back to step  210 . 
     If there is no such object that fulfils the condition of step  212 , step  218  is carried out. In step  218  it is determined whether all objects have already been assigned to blades. In other words, in step  218  it is checked whether the sequence is empty. If this is not the case the index i is incremented in step  220  and the control goes back to step  206  in order to assign remaining objects of the sequence to the next blade B 2 . 
     If the contrary is the case the index i is the minimum number N of blades that are required to handle the M objects, i.e. i=N. This number is output in step  220 . The minimum number N of blades that are required to handle the M objects can be a basis for an investment decision for purchasing of a corresponding number of blades. The assignment of objects to blades is output in step  224  in order to visualise the quality of the object size balancing. 
       FIG. 3  shows an example of objects to be assigned to blades. In the example considered here the objects are a number of 20 different tables having various sizes between 50 MB and 3,566 MB as indicated in  FIG. 3 . For example, table  1  has a size of 3,250 MB, table  2  has 250 MB, table  3  has 750 MB, etc. The table sizes can be actual table sizes or average table sizes that have been obtained by monitoring a real-life data processing system. Alternatively the table sizes are estimates for the purpose of planning cluster  100  (cf.  FIG. 1 ). 
       FIG. 4  shows the normalised table sizes of the tables of  FIG. 3 . The normalised size of a table is obtained by dividing the table size by the maximum storage capacity of one of the blades. 
       FIG. 5  shows the loads of the tables. In the preferred embodiment considered here the mean number of access operations (selects) per second that are performed or expected to be performed with respect to a table is used as a load indicator. In the example considered here the load as expressed by the number of read accesses per second to a table varies between five read accesses per second (table  6 ) and 950 read accesses per second (table  8 ). The table loads given in  FIG. 5  can be actual table loads or average table loads that have been obtained by monitoring a real-life data processing system. Alternatively the table loads are estimates or simulation results for the purpose of planning cluster  100 . 
       FIG. 6  shows the normalised table loads. The normalised table load of a given table is obtained by dividing the number of read accesses per second of that table by the load capacity of one of the blades. 
     In the preferred embodiment considered here the storage capacity of a blade is 4,096 MB. Hence a normalised table size of one indicates that the table has the absolute maximum size that can be handled by a given blade hardware. 
     The load capacity of a blade is the maximum possible access load that can be handled by a core engine running on one of the blades in the example considered here. This maximum value can be determined by benchmarks, by experiment or simulation. The load capacity depends on various parameters such as hardware and software characteristics and network bandwidth if a network is used to perform the table accesses. In the preferred embodiment considered here, the load capacity of one of the blades is 1,000 read accesses per second. For the purpose of explanation only read accesses are considered here. However, other typical data processing tasks, such as accesses that involve changes to the data, can also be taken into consideration for determining load capacity and table loads. 
       FIG. 7  shows a diagram illustrating the various normalised table sizes and table loads. As apparent from  FIG. 7  there are tables that require only a minimum amount of blade resources, especially table  13 , that has a small table size and a low table load. On the other hand there are tables that require a large amount of blade resources as they have both large table size and high table load. The distribution of tables to blades aims to balance the usage of the respective blade resources with respect to both size and load. The two constraints, i.e. size and load, are combined into a common sorting index that provides an objective measure for the amount of blade resources a given table requires. 
     The following definition of the sorting index is used for the purposes of explanation only and without restriction of generality:
 
Sorting index= W 1*(size+load)+ W 2*absolute value(size−load),
 
where size is the table size,
 
load is the table load
 
W1 is a weighting factor for (size+load) and
 
W2 is a weighting factor for the absolute value of the difference of size and load.
 
     For the purposes of the following example the weighting factors W1 and W2 are set to one without restriction of generality. 
     In this case the above expression evaluates as follows: 
     If size&gt;load: sorting index=2*size 
     If size=load: sorting index=2*size=2*load 
     If size&lt;load: sorting index=2*load. 
       FIG. 8  shows a sorted sequence in descending order of the sorting indices of the tables. The table that has the largest sorting index value and thus requires the largest amount of blade resources is table  8 , whereas the table having the smallest sorting index value and which does require the lowest amount of blade resources is table  13  as apparent from  FIG. 8 . 
       FIG. 9  shows the sorted sequence of  FIG. 8  illustrating the normalised table sizes and table loads of the individual tables. 
       FIG. 10  illustrates the first iteration for assigning tables to blades based on the ordered sequence as shown in  FIGS. 8 and 9 . It is assumed that each one of the blades has a storage capacity that corresponds to a normalised size threshold T S =1 and a load capacity that corresponds to a normalised load threshold T L . 
     The assignment procedure starts with the first table of the sorted sequence, i.e. table  8 . Table  8  is assigned to blade  1  as illustrated in  FIG. 10 . This results in a normalised gap G S  of unused storage capacity G S =0.6948 and a gap G L  of unused load capacity G L =0.05. 
     Next consecutive tables in the ordered sequence are searched that have table sizes and table loads that fit into the respective gaps G S  and G L . These are tables  6  and  13 . As table  6  precedes table  13  in the ordered sequence, it is assigned to blade  1  as illustrated in  FIG. 11 . 
     Next the aggregated table size and the aggregated table load of blade  1  is updated as illustrated in  FIG. 12 . This results in a updated normalised gap G S =0.6362 and an updated normalised gap G L =0.045 as illustrated in  FIG. 13 . Now tables of the ordered sequence that are consecutive to table  8  that fit into both gaps G S  and G L  are searched. 
     The only table that fulfils both conditions is table  13  which is thus assigned to blade  1  as illustrated in  FIG. 13 . The result is shown in  FIG. 14 . The updated normalised gap G S  is 0.6167 and the updated normalised gap G L  is 0.03. No table within the ordered sequence satisfies both constraints. 
     As there remain unassigned tables in the sequence an additional blade  2  is required as illustrated in  FIG. 15 . By definition the first table of the remaining ordered sequence, i.e. table  20  is assigned to blade  2 . This results in normalised gaps G S =0.1294 and G L =0.5600. The only table that satisfies both constraints is table  12  which is thus moved onto blade  2  as illustrated in  FIG. 15 . 
       FIG. 16  illustrates the result of this assignment. The updated normalised gap G S  is 0.0024 and the updated normalised gap G L  is 0.4500. None of the tables of the remaining ordered sequence satisfies these constraints such that an additional blade  3  is required etc. This procedure is carried out until the ordered sequence is empty, i.e. after all tables have been assigned to one blade. The resulting assignment of blades and the resulting minimum number of blades is illustrated in  FIG. 17 . In the example considered here the minimum number N of blades that is required for handling of all of the tables is N=10. 
     In order to further improve the quality of the balancing the method of  FIG. 18  can be carried out on the basis of the result provided by the method of  FIG. 2 . In step  800  the largest remaining gaps, G S  and G L  are determined as illustrated in  FIG. 17 . In the example considered in  FIG. 17  the largest remaining gap G S  is on blade  1  and the largest remaining gap G L  is on blade  10 . 
     In step  802  the largest remaining gap G S  is divided by N which yields delta  1  and the largest remaining gap G L  is divided by N which yields delta  2 . 
     In step  804  the size threshold T S  is reduced by delta  1  and the load threshold T L  is reduced by delta  2 . In step  806  the method of  FIG. 2  is performed again with the reduced thresholds as determined in step  804 . In case the sorting indices and the original object sequence obtained from the method of  FIG. 2  by performing steps  200  to  202  have been saved, these steps can be skipped in order to start processing of the method of  FIG. 2  in step  204 . The result is a further improved balancing. 
       FIG. 19  shows an alternative approach for refining the balancing. In step  900  delta  3  is calculated by calculating the difference of the sum of the storage capacity of the blades and the sum of the object sizes of all objects to be assigned to the blades and by dividing the difference by the minimum number of blades N. In step  902  delta  4  is calculated by calculating the difference of the sum of the load capacities of the blades and the sum of the object loads of all objects to be assigned to the blades and by dividing the difference by the minimum number of blades N. In the example considered here no normalized values are used in steps  900  and  902 . 
     In step  904  the size threshold T S  is reduced by the normalised value of delta  3 . The normalised value of delta  3  is obtained by dividing delta  3  by the storage capacity of one of the blades. 
     Likewise the load threshold T L  is updated in step  906  by the normalised delta  4 . Normalisation of delta  4  is performed by dividing delta  4  by the load capacity of one of the blades. 
     The reduced size threshold T S  and the reduced load threshold T L  correspond to the theoretical limit of blade resources that are required for handling of the given objects. As the object granularity is finite the theoretical threshold limits will be surpassed in most cases: 
     In order to refine the balancing the method of  FIG. 2  is performed again (step  908 ) on the basis of the size threshold T S  obtained from step  904  and the reduced load threshold T L  obtained from step  906  with the modification that the number of blades for the renewed performance of the method of  FIG. 2  is limited by the minimum number of blades N that has been determined previously. In other words, the assignment procedure of  FIG. 2  stops when the minimum number N of blades has been used up; this may have the consequence that there will be an excess amount of memory and/or load requirement for the last blade of the minimum number N of blades considered for the assignment procedure that surpasses the respective thresholds T S  and/or T L . Again steps  200  to  202  of the method of  FIG. 2  can be skipped in case the sorting indices and the sorted sequence has been stored when the method of  FIG. 2  was performed the first time. In step  910  it is determined whether there is such an excess amount of memory requirement and/or load requirement for the last blade N, i.e. blade  10 . If this is not the case the resulting assignment of objects to blades is output in step  912 . 
     If the contrary is the case step  914  is carried out in order to calculate the values of delta  5  and/or delta  6 . Delta  5  is obtained by dividing the excess amount of memory, if any, by the number of blades N. Likewise delta  6  is obtained by dividing the excess load requirement, if any, by the number of blades N. 
     On this basis the size and/load thresholds are incremented in step  916 . From there the control goes back to step  908 . 
     Steps  908  to  916  are carried out repeatedly until there is no longer an excess amount of memory and/or load requirement that cannot be provided by the given number N of blades. 
       FIG. 20  shows a further alternative for refinement of the balancing. First steps  900  and  902  of the method of  FIG. 19  are carried out in order to obtain delta  3  and delta  4 . The gaps delta  3  and delta  4  are now scanned by stepwise variation of the respective size and load thresholds in order to identify a more balanced assignment of objects to blades. For this purpose delta  3  and delta  4  are divided into equidistant intervals having step sizes delta  7  and delta  8 , respectively, as shown in step  1000 . The number of increments for performing the respective scans can be predefined or is user selectable. 
     It is to be noted that the number of increments for scanning G S  and for scanning G L  does not need to be the same. Preferably the scans are performed independently from each other such that the total number of assignments that is considered is the number of increments for the G S  scan multiplied by the number of increments for the G L  scan. 
     In step  1002  the size and load thresholds are set to the respective theoretical minima that are required to provide sufficient blade resources for handling of the given number of objects. On this basis the method of  FIG. 2  is performed again in step  1004 . In step  1006  a statistical measure is calculated as a quality measure for the balancing of the assignment of objects to blades obtained as a result of step  1006 , if any. For example, the standard deviation of the aggregated sorting index values of objects assigned to each one of the blades is calculated. In other words, for each blade the total of the sorting index values of the objects that have been assigned to the blade is calculated. This provides one total sorting index value per blade. Next the standard deviation is calculated for the total sorting index values. 
     In step  1008  at least one of the thresholds T S  or T L  is incremented by the normalised value of delta  7  or the normalised value of delta  8 , respectively. Next step  1004  is carried out again on the basis of the incremented size and load thresholds. Steps  1004  to  1008  are carried out repeatedly until the respective scans through G S  and G L  have been completed and the corresponding assignments of objects to blades have been obtained. In step  1010  one of the assignments is selected based on the statistical measures. For example, the assignment having the lowest standard deviation is selected. 
     It is to be noted that this procedure is limited by the minimum number of blades N. For assignments that do not fit on this given minimum number of blades N no statistical measure needs to be calculated as these assignments are not considered further in the procedure. 
       FIG. 21  shows a computer  108  that has processor  110  for running program  112 . Program  112  has module  113  for calculating a sorting index value for each object to be assigned to a blade. Further program  112  has module  114  for sorting of the objects by sorting index value and module  116  for assigning of objects to blades. 
     Further computer  108  has storage  118  for storing a table listing the objects, object sizes, and object loads of objects to be assigned to blades, storage  120  for storage of a storage capacity value of the blades, storage  121  for storage of a load capacity value of the blades, and storage  122  for storing of the number of blades. Further computer  108  has interface  124  for coupling to workstation  126 . 
     In operation the table with the object names/numbers, object sizes and object loads is entered via interface  124  and stored in storage  118 . Further a storage capacity value for the storage capacity of each individual blade is entered via interface  124  and stored in storage  120 . Likewise a load capacity value for the load capacity of each individual blade is entered via interface  124  and stored in storage  121 . 
     Next program  112  is invoked. Program  112  calculates a sorting index for each object contained in the table stored in storage  118  on the basis of the normalised object size and object load. The resulting indices are entered into the table stored in storage  118  by module  113 . Next module  114  sorts the table of storage  118  by decreasing storage index to provide a sequence of objects (cf. the sequence of  FIG. 8 ). Next module  116  performs the method of  FIG. 2  in order to determine the minimum number of required blades. 
     This minimum number is stored in storage  122  and is output via user interface  124 . This number can be a basis for a users investment decision for purchasing the number of blades to realise the data processing system being capable of handling the objects as listed in the table. 
     In addition, module  116  can perform the methods of  FIGS. 18 ,  19  and/or  20  for refining of the balancing. 
     Alternatively, computer  108  is one of the blades. In this instance computer  108  can dynamically change the assignment of objects to blades when the object size changes. This way frequent swapping operations can be avoided. In other words, the creation of “hot spots” is prevented by balancing the load. 
     LIST OF REFERENCE NUMERALS 
     
         
           100  cluster 
           102  processor 
           104  memory 
           106  network 
           108  computer 
           110  processor 
           112  Program 
           113  module 
           114  module 
           116  module 
           118  storage 
           120  storage 
           121  storage 
           122  storage 
           124  interface 
           126  workstation