Abstract:
A methods and systems for capacity planning of server resources are described wherein a load simulation tool is used to use actual data gathered from a server cluster during operation to simulate server cluster operation in which the load (requests per second) can be increased, and the effects on the utilization of resources can be observed. Plans containing recommendations are then presented to a system user so the user can make decisions necessary regarding whether to change configuration hardware to meet expected load increases in the future.

Description:
RELATED APPLICATIONS 
   This application is a continuation of U.S. patent application Ser. No. 09/577,118, filed on May 23, 2000, entitled “Load Simulation Tool For Server Resource Capacity Planning”, issued on 24 May 2005 as U.S. Pat. No. 6,898,564, and naming Matt Odhner, Giedrius Zizys and Kent Schliiter as inventors, the disclosure of which is hereby incorporated herein by reference. This application is also related to U.S. patent application Ser. No. 10/999,308, filed on an even date herewith, (i.e. filed Nov. 30, 2004), and issued on 22 Jul. 2008 as U.S. Pat. No. 7,403,886, which is also a continuation of U.S. patent application Ser. No. 09/577,118. 

   TECHNICAL FIELD 
   This invention relates to server systems, and more particularly to systems and methods for server resource capacity planning in server systems. 
   BACKGROUND 
   Capacity planning is forward-looking resource management that allows a computer system administrator to plan for expected changes of system resource utilization and make changes to the system to adequately handle such changes. Server performance and capacity planning is a top concern of computer administrators and business managers. If a lack of proactive and continuous capacity planning procedure leads to unexpected unavailability and performance problems, the downtime that results can be financially devastating to a company that depends heavily on server performance, such as an Internet-based merchant. 
   The importance of superior capacity planning is heightened by the continuous growth in server-dependent companies and potential customers for such companies. Even a solid company that has millions of customers can quickly decline in popularity if it does not increase its resources to handle a constant increase in customers. Excessive downtime of such a company can cause customers to take their business elsewhere. 
   Capacity planning requires both scientific and intuitive knowledge of a server system. It requires in-depth knowledge of the resource being provided and an adequate understanding of future server traffic. The difficulty of the problem has increased by technology in which multiple servers, or server clusters, are employed to handle a network or an Internet website. 
   Current capacity planning methods do not adequately estimate a number of servers having certain resources that a system will need to handle expected loads (requests per second). Therefore, a capacity planning method and system is needed in which a user can provide an expected load that the system needs to handle and receive information on how to increase servers and/or resources to adequately handle that load. 
   SUMMARY 
   A method and system for providing capacity planning of server resources is described herein. The methods and systems contemplate using measured data, extrapolation, and a load simulation tool to provide capacity planning results that are more accurate than current schemes. The load simulation tool and its implementation are also described. Server resources for which utilization is calculated are processor utilization, communication bandwidth utilization, memory utilization, and general server utilization. 
   Utilization is expressed in terms of actual use of the resource in relation to the total amount of resource available for use. For example, processor utilization is expressed as a percentage of procession power utilized for a given load in relation to the total processing power available. Communication bandwidth utilization is expressed as a percentage of an average server throughput per bytes per second in relation to the total communication bandwidth available. Memory utilization is expressed as a percentage of memory required per request times the length of a request queue in relation to the total memory available. General server utilization is 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). This 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 three categories. 
   The calculations that are used to derive utilization percentages of server resources require that the maximum load that can be handled by the server cluster (maximum requests / second) be determined. Other methods to estimate this maximum load are described in a related patent application entitled, “Capacity Planning For Server Resources,” by Odhner et al., U.S. patent application Ser. No. 09/577,118, filed on Apr. 14, 2000. It is noted that the inventors of the referenced patent application are the same of those of the present application, and that Microsoft Corp. is the assignee of both disclosures. 
   The implementation described herein derives the maximum load of a server cluster by collecting actual server parameter values during operation of the server system. This is accomplished through the use of a filter, such as an Internet Server Application Program Interface (ISAPI) filter, that collects actual server traffic information as data is transmitted to and from the server cluster. In addition, a monitor on each server in the server cluster collects other server parameter values that are used in subsequent calculations. 
   After the filter and the monitors have collected the required data, a system user selects a client computer from which to run a load simulation tool. The load simulation tool, in effect, replays the data that has been collected from the server cluster, such as the actual requests made to the server, the time intervals at which requests were made, etc. The load simulation tool is then used to increase the load on the system until a maximum service rate that the system can support is found. 
   There are several ways to calibrate the server load to find the maximum service rate. The number of users from the actual recorded data can be multiplied to simulate a greater number of users, which will increase the load on the system. Another way is to decrease the amount of time between requests, as recorded by the system, which will increase the load on the system. As the load increases, a service rate is monitored. When a further increase in the load does not increase the service rate, the load on the system at that point is considered to be the maximum service rate that can be delivered by the server. 
   It is noted that the user can create a script manually, instead of replaying the recorded data to calibrate the maximum load, but this will not provide a similarly accurate outcome, since the user in that situation, is required to estimate certain server usage parameters. 
   After the system is calibrated to find the maximum load that can be handled by the system, the maximum load value is used in subsequent calculations to determine server resource utilization estimates for any number of hypothetical situations. For instance, a user can enter information regarding a particular load that the user wants the current system to handle. The described implementation provides that user with estimates as to the utilization that the specified load will cause for the processor, the memory, the communications bandwidth, and the server in general. Also, the user may want to see how adding or removing a server from a current system will affect the utilization of these server resources. This situation can be adequately determined using the implementation described herein. 
   Finally, after the user runs the load simulation tool to calibrate the system as to the maximum load and make determinations regarding utilization of server resources, the system provides a plan that recommends any changes in configuration, if any, that should be made to the system to optimize system performance. These recommendations are stored for each test result, thereby enabling the user to run several tests, and contrast and compare results and recommendations for different situations that the user may expect in the future. The user is thus enabled to adequately plan for future situations. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     A more complete understanding of the various methods and arrangements of the present invention may be had by reference to the following detailed description when taken in conjunction with the accompanying drawings, wherein: 
       FIG. 1  is an illustration of a prior art server-client system having a server cluster that supports a website on the Internet. 
       FIG. 2  is a high-level block diagram of a server cluster having a stress simulation tool for capacity planning. 
       FIG. 3  is a screen shot of a capacity planning worksheet utilized in a capacity planning process using a stress simulation tool. 
       FIG. 4  is a graph of load vs. processor utilization for a calibrated method of capacity planning. 
   

   DETAILED DESCRIPTION 
     FIG. 1  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). 
     FIG. 2  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  FIG. 2 . 
   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. 
     FIG. 3  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. 
     FIG. 4  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. 
   General server utilization is derived by solving: 
   
     
       
         
           U 
           = 
           
             L 
             X 
           
         
       
     
   
   wherein: 
   U=general server utilization; 
   L=specified load; and 
   X=maximum load that can be handled by the server cluster  200 . 
   Processor utilization is derived by solving: 
   
     
       
         
           
             U 
             CPU 
           
           = 
           
             a 
             
               ⅇ 
               
                 b 
                 · 
                 L 
               
             
           
         
       
     
   
   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. 
   Communications bandwidth utilization is derived by solving: 
   
     
       
         
           
             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 
                 
                 ) 
               
             
             + 
             
               S 
               ⁢ 
               
                   
               
               ⁢ 
               
                 1 
                 · 
                 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%. 
   Then S1 is the adjusted average of these connection speeds: 
   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. 
   Then S1=488.8 KBytes/second. 
   The memory utilization is thus derived by solving: 
   
     
       
         
           
             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. 
   CONCLUSION 
   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.