fig1 shows a typical internet - based server - client system 100 . the system 100 includes several clients 104 a , 104 b , 104 c , 104 d connected to the internet 102 . a website 106 runs on a server cluster 108 comprised of three servers 110 a , 110 b , 110 c . although the server - client system 100 is shown operating within an internet website context , it is noted that the server - client system may operate in any server - client network context , such as a local area network ( lan ) or a wide area network ( wan ). fig2 depicts a server cluster 200 in accordance with the described implementations . the server cluster 200 comprises a primary server 202 having a processor 204 and a monitor 205 , a first secondary server 206 having a processor 208 and a monitor 209 , and a second secondary processor 210 having a processor 212 and a monitor 213 . the monitors are software devices that collect server parameter values while the server cluster 200 is in operation . the server cluster 200 communicates with a master client 214 via a communications connection 216 . it is noted that several clients ( not shown may be connected to the server cluster 200 . however , only one client is selected by the user to be the master client 214 . the master client 214 includes a simulation test program 217 . the function of the master client 214 and the simulation test program 217 will be discussed in greater detail below . the primary server 202 also includes a memory 218 and runs an operating system 220 . the operating system 220 provides resource management for primary server 202 resources . the memory 218 of the primary server 202 includes a cluster controller 222 , which controls communications between the primary server 202 and the secondary servers 206 , 210 and between the server cluster 200 and the network 214 . to accomplish this , the cluster controller 222 is provided with a communications program 224 . a capacity planner 226 is included in the cluster controller 222 . the function of the capacity planner 226 and its components will be described in greater detail below . generally , the capacity planner 226 comprises benchmark data 228 in which data collected from the server cluster 200 is stored , a calculation module 230 which stores the equations necessary to derive server resource utilization estimates , and plans 232 which stores recommendations that may be made to improve operational configuration of the server cluster . this file of recommendations if pre - defined by the manufacturer to list all the possible recommendations developed for the server cluster 200 . in addition , plans 232 may be updated via a version upgrade or through a connection to the internet . in addition , the capacity planner 226 includes a user interface 234 and an isapi filter 236 . the user interface 234 provides areas wherein a user of the server cluster 200 in general and , more specifically , the capacity planner 222 can enter server parameter values and / or a specified load for which the user wants to see server resource utilization and recommendations . the isapi filter 236 is used to collect actual server parameter values from the server cluster 200 while the server cluster 200 is operating . it is noted that the filter need not be an isapi filter , but can be any type of filter capable of performing the functions listed herein . the capacity planner 222 includes a load simulation tool 238 which is used to construct simulation scripts — such as the simulation test program 217 — that , when run on the master client 214 , simulates , plays or replays a server load scenario using actual operating conditions recorded from the server cluster 200 . the use of the load simulation tool 238 is described in further detail below . the implementations and functions of the components of the server cluster 200 outlined above will become more clear as the discussion progresses with continuing reference to the components of fig2 . the server resources that are discussed herein are : ( 1 ) processor utilization ( also referred to as cpu utilization ), wherein the processor utilization for a given load is expressed as a percentage of total processing power available ; ( 2 ) memory utilization , expressed as a percentage of total memory available is determined by multiplying the memory required for each request by the number of requests ; ( 3 ) communication bandwidth utilization , expressed as a percentage of the average throughput per bytes per second in relation to the total communication bandwidth available ; and ( 4 ) general server utilization , expressed as a ratio between a current service rate ( number of requests per second served ) and the maximum possible service rate ( maximum number of requests the server is capable of serving ). the general server utilization is less specific than showing the processor , bandwidth , and memory utilization , but it is useful for viewing resource constraints that do not fall under the other categories . fig3 shows a screen shot of a user interface 300 for a capacity planning worksheet , wherein the user enters the specified load , for which the user desires to observe the effects on the system of handling such a load . the user is required to manually enter several server parameter values . these server parameter values include : number of servers in the server cluster , available communications bandwidth , server name on which a simulation will be run , client name of the client that will serve as the master test client and execute a simulation script , and the name of the script that will be used to run the simulation . to begin , the user notifies the server cluster 200 to begin collecting data . the monitors 205 , 209 , 213 collect data from each server 202 , 206 , 210 . the isapi filter 236 collects data for other server parameters , namely for communications - related parameters such as number of incoming requests and average response time . the server resource utilization calculations require knowledge of the maximum load that the server cluster 200 can , theoretically , handle . the implementation described herein is more accurate in deriving the maximum load than any other method described to date . to find this maximum load , actual operating parameters are collected from the server cluster 200 through the monitors 205 , 209 , 213 and the isapi filter 236 . the data collected is utilized by the load simulation tool 238 to derive a simulation script that enables the simulation test program 217 on the master client 214 to recreate the server resource utilizations that occurred during the operational period . the simulation is run on only one server , selected by a user via the user interface 300 . it is assumed that the primary server 202 , and the secondary servers 206 , 210 are identical . once the simulation data is derived on one server , the final figures are extrapolated for the total amount of servers in the server cluster . this provides the user with the server resource utilization figures . although not particularly discussed herein , it is noted that if the servers are not identical , the simulation script can be run on each individual server and then the individual results can be summed to provide the final totals . for discussion purposes , it is assumed that servers 202 , 206 , 210 are identical . once a script has been obtained , the user is provided with means to increase the test load on the server to run the script . all the other parameters are the same , so increasing the load will , necessarily , increase the utilization of the server resources . fig4 shows a graph of a load vs . utilization curve 500 . for this example , processor utilization is used , though it will be apparent that a similar graph could be constructed for any of the server resource utilization estimates . as the load increases to point 502 on the load axis , the utilization curve 500 reaches a point 504 which can be considered to be the maximum load that can be handled by the server 202 . the user is may increase the load via the user interface 300 , and re - run the script using the higher load value . a situation will arise in which an increase in the load will not result in an increase of the rate at which the load is handled . this is the maximum load 502 which the server can handle . the load ( l ) at this point is used in the resource utilization estimate calculations below . wherein : u = general server utilization ; l = specified load ; and x = maximum load that can be handled by the server cluster 200 . wherein : u cpu is processor utilization ; l is the specified load ; and a and b are processor regression constants derived from applying linear regression methodology to several load / utilization ( x , y ) pairs measured during the test . u b = f tcp b · ( c + d · l ) wherein : u b is communication bandwidth utilization ; f tcp is a transmission overhead factor that , when applied to a certain size page , results in the actual bandwidth necessary to transmit the page ; l is the specified load ; b is the total communication bandwidth available ; and c and d are bandwidth regression constants derived from applying linear regression methodology to several load / utilization ( x , y ) pairs measured during the test . the memory utilization is derived by first solving the following equation to determine the number of concurrent connections : n = l ( x - l ) + s1 · l wherein : n is the number of concurrent connections ; l is the specified load ; x is the maximum load that can be handled by the server cluster 200 ; and s1 is a connection memory factor that is the adjusted average of the incoming connections at different speeds . for example , suppose that the isapi filter 236 has measured the following percentages for connection types : 56k : 50 % adsl : 20 % *** question : what relation to screen shot ? isdn ? *** t1 : 20 % t3 : 10 %. 56k : 0 . 5 * 5 . 6 = 2 . 8 kbytes / sec + adsl : 0 . 2 * 30 = 6 kbytes / sec + t1 : 0 . 2 * 150 = 30 kbytes / sec + t3 0 . 1 * 4500 = 450 kbytes / sec = 488 . 8 kbytes / sec . u m = n · ( m tcp + m iisstruct ) + m os + m iis m wherein : u m is memory utilization ; n is the number of concurrent connections ; m tcp is an amount of memory for tcp buffers ( 32 kb per connection ); m iis is the amount of memory required by a server communication program ( 50 mb for iis ); m iisstruct is the amount of memory necessary to support communications program data structures associated with each connection ( 50 kb per connection for iis ); m os is the amount of memory required by a server operating system ( 64 mb for windows ® nt by the microsoft corporation of redmond , wash .) and m is the amount of total memory available . it is noted that some figures have been used that are specific to iis , the communications program 224 used for purposes of this discussion . however , it is noted that these numbers may be different for different communications programs . the described implementations advantageously provide for capacity planning for a server - client system and , particularly , to a server cluster within a server - client system . the load simulation tool is an extremely accurate tool for determining the maximum load handled by a server . the maximum load can then be substituted into the server resource estimate equations to give accurate server resource utilization results . although the invention has been described in language specific to structural features and / or methodological steps , it is to be understood that the invention defined in the appended claims is not necessarily limited to the specific features or steps described . rather , the specific features and steps are disclosed as preferred forms of implementing the claimed invention .