Abstract:
In one embodiment the present invention includes a computer-implemented method of measuring bottlenecks in a computer program implemented in a hardware client-server environment. The method includes defining an execution parameter to measure of the hardware client-server environment. The method further includes modifying code blocks to include instrumentation code. The instrumentation code relates to the execution parameter. The method further includes executing, in the hardware client-server environment, the code blocks having been modified. The method further includes generating instrumentation data, related to the execution parameter, that result from executing the code blocks. The method further includes outputting the instrumentation data having been generated. In this manner, the bottlenecks may be measured; the measured bottlenecks may be sorted; noteworthy bottlenecks may be identified; and the aspects of the hardware client-server environment may be adjusted to address the bottlenecks. Sorting is helpful because thousands of synchronization points may be detected and measured, but many of them do not represent a problem that requires correction.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
       [0001]    Not Applicable. 
       BACKGROUND 
       [0002]    The present invention relates to measuring bottlenecks, and in particular, to measuring bottlenecks in a client-server environment. 
         [0003]    Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section. 
         [0004]    The Java™ programming language is a high-level language that may be characterized by all of the following buzzwords: simple, object oriented, distributed, multithreaded, dynamic, architecture neutral, portable, high performance, robust, and secure. Further details regarding each of these buzzwords can be found in “The Java™ Language Environment” by James Gosling and Henry McGilton. 
         [0005]    In the Java™ programming language, source code is first written in plain text files ending with the .java extension. These source files are then compiled into .class files by a Java™ compiler such as javac. A .class file does not contain code that is native to a physical processor; it instead contains bytecodes—the machine language of the Java™ Virtual Machine (Java™ VM). A launcher tool (java.exe or other Java™ runtime environment) then runs the application with an instance of the Java™ VM. 
         [0006]    The Java™ VM runs the application by converting the Java™ bytecodes into native instructions that are specific to the actual operating system and processor of the computing device. Since the bytecode is designed to be portable, but the Java™ VM is specific to the actual computing device, the Java™ VM may be modified in order to perform a wider variety of tasks yet still remain compliant with the Java™ standard. 
         [0007]    In general, a Java™ program may be provided by a server to a client for execution. In a client-server enterprise environment such as that provided by the Java™ Enterprise Edition, the server may also execute a Java™ program that communicates with the Java™ program executed by the client, and that interfaces with database applications executed by the server. These Java™ programs may involve bottlenecks as the client accesses information stored by the server. For example, a bottleneck may be created at the server when the server locks data being accessed by the client. The delays these bottlenecks cause may be increased as the Java™ program is executed by multiple clients. 
         [0008]    One way to measure bottlenecks is as follows. First, a high load is provided to the server. This helps to identify bottlenecks resulting from scalability issues. Second, the software is executed and the realized wait times are measured. Third, the bottlenecks are identified and addressed. Fourth, the steps of execution, measurement, identification and addressing are iteratively performed to identify further bottlenecks, since some bottlenecks may hide others. 
         [0009]    In the above manner, many existing systems serially detect and remove bottlenecks. 
         [0010]    Furthermore, bottlenecks are not confined to Java™ language implementations. Bottlenecks may be present in other distributed computing environments where access is coordinated to maintain data integrity. 
       SUMMARY 
       [0011]    Embodiments of the present invention improve the performance of a hardware client-server environment. In one embodiment the present invention includes a computer-implemented method of measuring bottlenecks in a computer program implemented in a hardware client-server environment. The method includes defining an execution parameter to measure of the hardware client-server environment. The method further includes modifying code blocks to include instrumentation code. The instrumentation code relates to the execution parameter. The method further includes executing, in the hardware client-server environment, the code blocks having been modified. The method further includes generating instrumentation data, related to the execution parameter, that result from executing the code blocks. The method further includes outputting the instrumentation data having been generated. 
         [0012]    In this manner, the bottlenecks may be measured; the measured bottlenecks may be sorted; noteworthy bottlenecks may be identified; and the aspects of the hardware client-server environment may be adjusted to address the bottlenecks. Sorting is helpful because thousands of synchronization points may be detected and measured, but many of them do not represent a problem that requires correction. 
         [0013]    The following detailed description and accompanying drawings provide a better understanding of the nature and advantages of the present invention. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0014]      FIGS. 1A-1B  illustrate how a bottleneck can hide another bottleneck. 
           [0015]      FIG. 2  is a block diagram of a client-server environment according to an embodiment of the present invention. 
           [0016]      FIG. 3  is a flowchart of a process of measuring bottlenecks according to an embodiment of the present invention. 
           [0017]      FIG. 4  illustrates bottleneck metrics according to an embodiment of the present invention. 
           [0018]      FIGS. 5A-5B  illustrate an example of code block modification according to an embodiment of the present invention. 
           [0019]      FIG. 6  is a block diagram of an example computer system and network  1400  for implementing embodiments of the present invention. 
           [0020]      FIGS. 7A-7C  illustrate how bottlenecks may be detected and removed in computer program systems. 
           [0021]      FIGS. 8A-8B  illustrate how bottlenecks are measured according to an embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0022]    Described herein are techniques for identifying bottlenecks in computer programs. In the following description, for purposes of explanation, numerous examples and specific details are set forth in order to provide a thorough understanding of the present invention. It will be evident, however, to one skilled in the art that the present invention as defined by the claims may include some or all of the features in these examples alone or in combination with other features described below, and may further include modifications and equivalents of the features and concepts described herein. 
         [0023]    In general, web servers are designed to handle multiple requests in parallel. Ideally no server would serialize the requests one after the other. However, some components of the server need synchronization (e.g., data storage units), so the server synchronizes these requests and causes synchronized code segments to wait on each other. Such a critical section is referred to as a “bottleneck”. When two requests want to enter such a section, only one of them enters, and the other is waiting. If further requests arrive, they wait as well. This may be referred to as the “waiting time” for a single request (or single routine). Furthermore, if a critical section is inside another critical section (referred to as “nesting”), then no request would wait upon the internal section, thus is remains “hidden”. Any other nested critical sections would likewise remain hidden. 
         [0024]      FIGS. 1A-1B  illustrate how a bottleneck can hide another bottleneck.  FIG. 11A  illustrates bottleneck hiding and  FIG. 1B  illustrates bottleneck serialization. The arrows represent subprocesses running in parallel, for example, Java™ threads; they may correspond to client requests processed by the server. In  FIG. 1A , the bottleneck  12  results in a wait time  14 , and the bottleneck  16  results in a wait time  18 . The bottleneck  12  hides the bottleneck  16 . The bottleneck  16  is not apparent until the bottleneck  12  has been identified and addressed. In  FIG. 1B , the bottleneck  22  results in a wait time  24 , and the bottleneck  26  results in a wait time  28 . The bottleneck  22  serializes the routines so that the bottleneck  26  is not related to any concurrency effects such as wait time and contention. Once the bottleneck  22  has been identified and addressed, the wait time  28  of the bottleneck  26  can be identified and addressed.  FIGS. 7A-7C  provide more details regarding bottleneck serialization. 
         [0025]      FIGS. 7A-7C  illustrate how bottlenecks may be detected and removed in computer program systems.  FIG. 7A  illustrates a processing flow  700  in a computer program system that can be visualized as a pipe. The processing flow  700  has two bottlenecks B 1  and B 2 . The bottlenecks B 1  and B 2  may correspond to computer program synchronization points. 
         [0026]      FIG. 7B  illustrates that a high processing load  702  is applied into the processing flow  700 . The bottleneck B 1  constrains the processing load  702 . The bottleneck B 1  is detected by measuring the processing flow  700  at point  704  and at point  706 , and noting the difference. Notice that the bottleneck B 2  may not be detected because there is no difference between the processing flow at point  706  and point  708 . 
         [0027]      FIG. 7C  illustrates the processing flow  700  after the bottleneck B 1  has been detected and removed (compare to  FIG. 7B ). Again, the high processing load  702  is applied to the processing flow  700 , and now the bottleneck B 2  constrains the processing load  702 . The bottleneck B 2  is detected by noting the difference between the processing flow at point  706  and point  708 . 
         [0028]      FIG. 2  is a block diagram of a client-server environment  100  according to an embodiment of the present invention. The client-server environment  100  includes an application server  102 , a client  104  and a database server  124  connected via a network. The client-server environment  100  may be described as a “three-tier architecture”. The client-server environment  100  may implement the Java™ Enterprise Edition. More specific hardware details of the client-server environment  100  may be seen in  FIG. 6 . 
         [0029]    The network may be a local area network, a wide area network, or another type of network, such as the internet. 
         [0030]    The client  104  implements a virtual machine  112 . The virtual machine  112  may be a Java™ virtual machine that executes Java™ programs that the client  104  receives from the application server  102 . The client may implement the “presentation tier” of the three-tier architecture. More than one client  104  may be present. 
         [0031]    The application server  102  implements a virtual machine  122 , applications (computer programs)  128 , and a concurrency profiler  130 . The virtual machine  122  executes the applications  128  (which may be modified by the concurrency profiler  130  as detailed below). The virtual machine  122  may be a Java™ virtual machine that executes Java™ programs. One or more of the computer programs  128  may be provided to the client  104  for execution. The computer programs  128  may be Java™ programs. The application server  102  may implement the “application tier” of the three-tier architecture. More than one application server  102  may be present. 
         [0032]    The database server  124  implements a database  126 . The database  126  stores the underlying data that is queried, added, deleted, etc. The database server  124  may implement the “data tier” of the three-tier architecture. More than one database server  124  may be present. 
         [0033]    In everyday operation of the client-server environment  100 , the concurrency profiler  130  may be omitted. An example of everyday operation is as follows. The database  126  stores accounting data. The database server  124  interfaces between the database  126  and other hardware or user interface components, for example, to add data to the database  126 , to send queries or other data manipulations to the database  126 , to extract information from the database  126  for display or reporting, etc. The virtual machine  122  interfaces between the database server  124  and the client  104 , for example, to execute programs that receive data from the client  104  to be added to the database  126 , that receive requests for data manipulation from the client  104 , or that send extracted information to the client  104  for display, etc. These actions of the virtual machine  122  are controlled by executing the computer programs  128 . These actions of the virtual machine  122  may encounter bottlenecks. 
         [0034]    The concurrency profiler  130  may be implemented in the application server  102  to detect the bottlenecks that result when the virtual machine  122  executes the computer programs  128 . These bottlenecks may result from data locks when interacting with the database server  124  or accessing the database  126 . In a client-server environment, these bottlenecks may also result from interaction with the client  104 . Further details of the concurrency profiler  130  are provided below. 
         [0035]      FIG. 3  is a flowchart of a process  200  of measuring bottlenecks according to an embodiment of the present invention. The process  200  may be implemented by the concurrency profiler  130 . The concurrency profiler  130  may execute a computer program to implement the process  200 . The computer program may be stored with the computer programs  128 . The computer program that implements the process  200  may be written in the Java™ language. 
         [0036]    In step  202 , one or more execution parameters are defined for measurement. The execution parameters relate to the computer programs  128  that the application server  102  executes. The execution parameters may include parallelism, throughput, through time, limit throughput, and utilization parameters. The execution parameters are related to bottlenecks in the computer programs  128 . The execution parameters are discussed in more detail below. 
         [0037]    In step  204 , the computer programs to be evaluated for bottlenecks are modified to include instrumentation code. The computer programs include code blocks such as applets, classes, functions, procedures, methods, objects, interfaces, variables, etc. The instrumentation code is added to one or more code blocks so that the execution parameters may be measured for that code block. 
         [0038]    In step  206 , the modified code blocks are executed. When implemented by the application server  102 , the concurrency profiler  130  provides the modified code blocks to the virtual machine  122  for execution. 
         [0039]    In step  208 , as the modified code blocks are executed in step  206 , instrumentation data is generated. The instrumentation data corresponds to the execution parameters and results from executing the modified code blocks. The instrumentation data may include information related to each bottleneck such as a location identifier, a thread identifier, a monitor identifier, an enter time, and an exit time. The instrumentation data may include performance information such as a throughput measurement, a through time measurement, a limit throughput measurement, and a utilization measurement. 
         [0040]    In step  210 , the instrumentation data is outputted. According to one embodiment, the concurrency profiler  130  instructs the virtual machine  122  to send the instrumentation data to the client  104 , and the virtual machine  112  instructs the client  104  to display the instrumentation data for perusal by a user. The instrumentation data may be sorted or otherwise displayed in decreasing order by a parameter such as the utilization measurement so that important bottlenecks may be easily identified for correction or removal. 
         [0041]    Further details regarding the client-server environment  100 , the concurrency profiler  130 , and the process  200  are provided below. 
         [0042]    Software Bottlenecks 
         [0043]    As discussed above, a bottleneck results in a lessening of throughput. In computer programs, bottlenecks are typically caused by synchronization points between different routines. These bottlenecks are dangerous because they represent a logical limitation that cannot be solved by adding new hardware. 
         [0044]    Examples of computer program bottlenecks include concurrency locks and resource limits. Concurrency locks are used in different routines in order to prevent concurrency problems. An example is setting an exclusive (write) lock on a file before modifying the file, and setting a shared (read) lock on a file before reading the file. Resource limits are when multiple routines acquire a resource from a limited set of resources. An example is a program that keeps a set of ten database connections. The program can serve multiple clients in parallel, but a maximum of ten clients can use the connection at one point in time. 
         [0045]    According to an embodiment of the present invention, scalability problems are analyzed at low load. Large, expensive hardware is not required. 
         [0046]    According to an embodiment of the present invention, all possible bottlenecks are listed. None are hidden. 
         [0047]    According to an embodiment of the present invention, the bottlenecks are measured and sorted in order from most significant bottleneck to least significant bottleneck. 
         [0048]      FIGS. 8A-8B  illustrate how bottlenecks are measured according to an embodiment of the present invention.  FIG. 8A  is the same as  FIG. 7A , showing the processing flow  700  and the bottlenecks B 1  and B 2 .  FIG. 8B  shows a low processing load  712 . As an example, the low processing load  712  may be a single routine executing through the computer program. The single routine may then be used to trace the bottlenecks and describe their metrics. One metric is the number of routines that can pass through a bottleneck at one time. Another metric is the time needed for the single routine to pass through a bottleneck. The metrics are described in more detail below. 
         [0049]    Defining Bottleneck Metrics 
         [0050]    Consider the following example. A small web server has one database connection. Many parallel requests do the following: 1. Obtain an explicit lock (a request may wait here). 2. Get the connection. 3. Execute a database command, e.g., SQL (structured query language) operation, for approximately 0.05 seconds. 4. Release the explicit lock. 
         [0051]    Assuming a load at a rate of seven requests per second, the following calculations result. Each operation takes 0.05 seconds so a maximum of 20 operations per second may be performed (1/0.05=20). The current load is seven operations per second, so the bottleneck is “utilized” at 35%. If the load reaches the maximum of 20 operations per second, the utilization becomes 100%. 
         [0052]    The following metrics are defined: parallelism [P], throughput [T], through time [Δt], limit throughput [μt], and utilization [U]. 
         [0053]    Parallelism [P] is the maximum number of routines that can run inside the bottleneck. The metric is implied from the computer program logic and does not necessarily depend on the hardware or the load. In the example above, [P]=1 since only one request may work with the connection at one point in time. If we have N connections, the parallelism would be equal to N. Thus, the parallelism does not depend upon the load, but on the bottleneck design. 
         [0054]    Throughput [T] is the number of operations per second—how many routines pass through the bottleneck per second. This metric depends upon the load. Normally we expect doubled throughput from a doubled load. 
         [0055]    Through time [Δt] is the time needed for one routine to pass through the bottleneck. According to an embodiment, the through time is measured in a non-concurrency environment (i.e., one routine). This allows a “clean” through time measurement, without including concurrency side effects like wait times. According to an embodiment, a low load is used when operating the concurrency profiler  130 . 
         [0056]    Limit throughput [ 82  t] is the maximum number of operations that may be performed in a specific time (e.g., the maximum number of operations per second). The limit throughput is derived from the through time, as follows: 
         [0000]    
       
      
       [μT]=[P]/[Δt] 
      
     
         [0000]    In the example above, [μt]=1/0.05=20 operations per second. 
         [0057]    Utilization [U] is the ratio between the throughput (measured) and the limit throughput, as follows: 
         [0000]    
       
      
       [U]=[T]/[μT] 
      
     
         [0000]    In the example above, [U]=7/20=35%. Since the throughput cannot be larger than the limit throughput, 
         [0000]      0≦[U]≦1 
         [0058]      FIG. 4  illustrates these bottleneck metrics. The through time [Δt] is represented by the height of the bottleneck  400 . The parallelism [P] is represented by the width between parts of the bottleneck  400 . The throughput [T] is represented by the routines (arrows  402 ) passing through the bottleneck  400 . 
         [0059]    According to an embodiment, the concurrency profiler  130  measures the metrics defined above in order to calculate the utilization of as many as all the bottlenecks presented. The concurrency profiler  130  instruments each code block and records one or more of the following information: 
         [0060]    Location: This is the class, method, and line number of the bottleneck. 
         [0061]    Thread: This is the thread identifier (routine identifier). 
         [0062]    Monitor: This is the resource that the routines are fighting for (e.g., a synchronization monitor or lock). 
         [0063]    Enter time: This is the time that the thread enters the bottleneck. 
         [0064]    Exit time: This is the time that the thread exits the bottleneck. 
         [0065]    The instrumentation of the computer program may be performed via bytecode modification. According to an embodiment where the concurrency profiler  130  is measuring Java™ programs, synchronized code blocks may be modified, as shown in  FIGS. 5A-5B . 
         [0066]      FIGS. 5A-5B  illustrate an example of code block modification according to an embodiment of the present invention.  FIG. 5A  shows the code block prior to modification, and  FIG. 5B  shows the code block after modification. In a Java™ implementation, the monitor enter and monitor exit commands may be used by the Java™ synchronized statement to coordinate access to an object among multiple threads. Since each thread is writing its own trace file, any contention that may be caused by the concurrency profiler  130  is avoided. 
       EXAMPLE  
       [0067]    An embodiment of the concurrency profiler  130  was used to find bottlenecks in the SPECjAppServer benchmark application, to enhance scalability. (For example, with perfect scalability, doubling the hardware parameters doubles the load capability. The presence of bottlenecks is a barrier to perfect scalability, so identifying and removing bottlenecks improves scalability.) The concurrency profiler  130  transformed all binaries of the Java™ server installation, which was approximately 350 .jar files. A valid run was executed with transaction rate 5 (50 HTTP [hypertext transfer protocol] clients and 15 RMI [remote method invocation] clients). (These parameters are load test parameters that show the load executed, for example, corresponding to number of client computers.) The concurrency profiler  130  generated a report for which TABLE 1 summarizes the top level of information. 
         [0000]    
       
         
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                   
                   
                 Through 
                 Limit 
                   
               
               
                 Location 
                 Throughput/s 
                 Time (ms) 
                 Throughput/s 
                 Utilization % 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 com/sap/jms/client/connection/ 
                 48.318 
                 19.05596738 
                 52.477 
                 92.07 
               
               
                 RemoteAdapter.sendAndWait.14 
               
               
                 erverSessionPool.getServerSession.10 
                 14.813 
                 62.13881812 
                 16.093 
                 92.04 
               
               
                 com/sap/tc/logging/ 
                 97.712 
                 3.246753247 
                 308 
                 31.72 
               
               
                 Log.getEffectiveSeverity.228 
               
               
                 com/sap/jms/server/dc/consumer/ 
                 27.711 
                 6.248281723 
                 160.044 
                 13.57 
               
               
                 Consumer.redeliver.288 
               
               
                 com/sap/jms/server/dc/DeliveryTask.- 
                 15.443 
                 8.099133393 
                 123.47 
                 12.51 
               
               
                 execute.6 
               
               
                 com/sap/engine/session/state/ 
                 98.592 
                 1.153469058 
                 866.95 
                 11.37 
               
               
                 SessionRequest.getSession.20 
               
               
                 com/sap/engine/services/connector/jca/ 
                 240.346 
                 0.246410416 
                 4058.27 
                 5.92 
               
               
                 ConnectionHashSet.match.97 
               
               
                   
               
             
          
         
       
     
         [0068]    With the information in the report (e.g., TABLE 1), a configuration expert may then determine what aspects of the application server  102  may be involved in the bottlenecks, and may adjust these aspects to improve performance. The aspects that may be adjusted broadly include configuration problems, programming inefficiencies, etc. More specifically, the configuration expert may adjust the hardware components used to implement the application server  102 , the programming of the computer programs executed by the application server  102 , the configuration of the virtual machine  122 , the programming of the computer programs executed by the database server  124 , the programming of the Java Database Connectivity (JDBC) API drivers, the configuration of the network, etc. 
         [0069]    As discussed above, the code blocks are instrumented, and the performance measurement is executed using (for example) one request/routine (referred to as a “tracing routine”). Such a tracing routine does not require a high load on the system components. The bottlenecks are measured according to the measurement metrics set forth above. Once the bottlenecks are measured, they may be sorted in order to identify the most important bottlenecks. Then the configuration expert may, if desired, concentrate on addressing the important bottlenecks, so that a given amount of effort achieves the greatest results. 
         [0070]      FIG. 6  is a block diagram of an example computer system and network  1400  for implementing embodiments of the present invention. Computer system  1410  includes a bus  1405  or other communication mechanism for communicating information, and a processor  1401  coupled with bus  1405  for processing information. Computer system  1410  also includes a memory  1402  coupled to bus  1405  for storing information and instructions to be executed by processor  1401 , including information and instructions for performing the techniques described above. This memory may also be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor  1401 . Possible implementations of this memory may be, but are not limited to, random access memory (RAM), read only memory (ROM), or both. A storage device  1403  is also provided for storing information and instructions. Common forms of storage devices include, for example, a hard drive, a magnetic disk, an optical disk, a CD-ROM, a DVD, a flash memory, a USB memory card, or any other medium from which a computer can read. Storage device  1403  may include source code, binary code, or software files for performing the techniques or embodying the constructs above, for example. 
         [0071]    Computer system  1410  may be coupled via bus  1405  to a display  1412 , such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information to a computer user. An input device  1411  such as a keyboard and/or mouse is coupled to bus  1405  for communicating information and command selections from the user to processor  1401 . The combination of these components allows the user to communicate with the system. In some systems, bus  1405  may be divided into multiple specialized buses. 
         [0072]    Computer system  1410  also includes a network interface  1404  coupled with bus  1405 . Network interface  1404  may provide two-way data communication between computer system  1410  and the local network  1420 . The network interface  1404  may be a digital subscriber line (DSL) or a modem to provide data communication connection over a telephone line, for example. Another example of the network interface is a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links is also another example. In any such implementation, network interface  1404  sends and receives electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information. 
         [0073]    Computer system  1410  can send and receive information, including messages or other interface actions, through the network interface  1404  to an Intranet or the Internet  1430 . In the Internet example, software components or services may reside on multiple different computer systems  1410  or servers  1431 ,  1432 ,  1433 ,  1434  and  1435  across the network. A server  1431  may transmit actions or messages from one component, through Internet  1430 , local network  1420 , and network interface  1404  to a component on computer system  1410 . 
         [0074]    The computer system and network  1400  may be the hardware used to implement the application server  102 , the client  104  and the database server  124 . 
         [0075]    Although the above description has focused on the Java™ environment, similar embodiments may be implemented to identify and measure bottlenecks in other distributed computing environments, including an ABAP™ environment, a C# environment, a .NET™ environment, etc. 
         [0076]    The above description illustrates various embodiments of the present invention along with examples of how aspects of the present invention may be implemented. The above examples and embodiments should not be deemed to be the only embodiments, and are presented to illustrate the flexibility and advantages of the present invention as defined by the following claims. Based on the above disclosure and the following claims, other arrangements, embodiments, implementations and equivalents will be evident to those skilled in the art and may be employed without departing from the spirit and scope of the invention as defined by the claims.