Abstract:
In a distributed computing system, an artificial intelligence system may be employed to configure the network variables. A metric describing the overall system performance may be derived during network operation or simulation and compared to an ideal metric describing the same distributed system performance. The difference between the derived metric and the ideal metric may then be used with an artificial intelligence system to modify the network variables to evolve the system toward the ideal performance standard.

Description:
[0001]     This application is a continuation-in-part of prior application Ser. No. 11/089,894, “System and Method for Monitoring and Reacting to Peer-to-Peer Network Metrics” to Horton et al., filed Mar. 25, 2005. 
     
    
     BACKGROUND  
       [0002]     Distributed computer systems may operate in a distributed environment where many different systems or nodes communicate across a network to perform common tasks. Particularly, a distributed system is a collection of independent computers that appears to its users as a single coherent system; the individual differences between other system members as well as the system&#39;s internal organization may be hidden. Furthermore, users and applications may interact with a distributed system in a consistent and uniform way, regardless of where and when the interaction takes place. These systems may also be substantially scalable as no single element may control the system&#39;s behavior. Further, distributed systems may be continuously available, although an individual member may become temporarily or permanently unusable. Users or applications of a distributed system may not notice or control system maintenance or system performance. In short, a distributed system may easily connect users with resources, it may hide the fact that resources are distributed across a network, it may be open, and it may be scalable.  
         [0003]     For example, the Internet Domain Name System (DNS) is an example of a widely-used distributed system. DNS is primarily used for looking up Internet host addresses and mail servers. The system is hierarchically organized as a rooted tree. Each node or leaf in the tree is associated with resource records that hold the information associated with the domain name. One of the functions of the DNS is associating Internet protocol addresses with domain names. The DNS is implemented as a hierarchical set of servers wherein each domain or subdomain has one or more authoritative DNS servers that publish information about that domain and the name servers of any domains beneath it. To resolve a domain name, a local host need only have information concerning the topmost DNS server. The local host will query the topmost DNS server for the location of a particular domain. The topmost DNS server, in turn, will point the requesting entity to a lower-level server of the DNS until the domain name is finally resolved. Therefore, the task of resolving Internet domain names may be distributed across several nodes of the DNS.  
         [0004]     The Peer Name Resolution Protocol (PNRP) is another example of a distributed system. P2P distributed systems may be composed of many individual peer computers called nodes. PNRP may enable each node to dynamically publish and resolve names, register multiple names on a node, register multiple computers to a single name, and register names for applications.  
         [0005]     Regardless of the distributed system, network variables that may be generally transparent to the individual user may determine how data propagates through the system and how each node processes the data. For example, a node “timeout” variable may define how long the node may try to connect to another node before recognizing connection success or failure. Also a system may share a common, Distributed Hash Table (DHT) of key/value pairs. Distributed hash tables may partition ownership of the keys among system nodes and may efficiently route messages or files to the unique owner of any given key. Each node may be associated with a “keyspace” or set of unique keys, and each node may store data associated with all keys in the node&#39;s keyspace. However, a system implementing a DHT may behave differently depending on the value of a network variable setting the size of the keyspace. For example, a system including a network variable that assigns only one key to each node may result in very high data lookup costs, while a system with many keys assigned to each node may require each node to store a large amount of data. For any distributed system using a DHT, the overall system may behave differently by either globally or locally adjusting the keyspace assignment variable.  
         [0006]     Finally, P2P systems may maintain a routing table at each node to direct messages traveling through the network. However, each node may not need to store a routing table describing every system node to achieve a suitable level of accurate message routing. A system allowing a very large routing table may route messages more efficiently because each node will have more information about the location of a destination node. However, maintaining the accuracy of a large routing table may be too cumbersome for the system. Each time the routing table must be refreshed, the table information must be transmitted to each participating node. As more nodes are represented on the routing table, more nodes must necessarily receive the new routing table information. Likewise, a system allowing a small routing table may be less cumbersome for system maintenance, but messages will take much longer to arrive at their destination node because each node only has a limited amount of information concerning the location of other nodes on the network. Further, this increase in transmission time may result in more lost or “timed out” messages depending on the value of the previously-described system timeout variable. As messages take more time to propagate through the network, the message may exceed the network timeout variable. Many other system variables that are transparent to the user may affect the overall performance of the distributed system.  
         [0007]     As explained above, each network variable may individually affect the performance of the distributed system, however, each variable may also interact with other network variables. The variables may be changed and the distributed system may be observed in operation or simulation to determine the effect of the variable adjustment on the system performance. The performance of a distributed system may be monitored or recorded by the method disclosed in U.S. application Ser. No. 11/089,894, “System and Method for Monitoring and Reacting to Peer-to-Peer Network Metrics” to Horton et al., the entire disclosure of which is hereby incorporated by reference. The distributed system performance may be generally described as a system “health index.” 
       SUMMARY  
       [0008]     Modifying network variables while simultaneously monitoring or considering the system performance may result in a more efficient distributed system. In a distributed computing system, an artificial intelligence system may be employed to configure the network variables. A metric describing the overall system performance may be derived during network operation or simulation and compared to an ideal metric describing the distributed system performance. The difference between the derived metric and the ideal metric may then be used to modify the network variables to evolve the system toward the ideal performance standard. The complex and multiply-dependent characteristics of network variables may be analyzed and optimized using the node interaction of a neural network. By employing artificial intelligence approaches, the method may allow a distributed system to automatically modify and evolve during operation or simulation to improve performance. 
     
    
     DRAWINGS  
       [0009]      FIG. 1  is a diagram of one example of a distributed system;  
         [0010]      FIG. 2  is a block diagram of one example of a computing system that may operate in accordance with the claims;  
         [0011]      FIG. 3  is a diagram of one example of a peer-to-peer network;  
         [0012]      FIG. 4  is a diagram of one example of a neural network;  
         [0013]      FIG. 5  is a flowchart describing a method of one example of improving a distributed system&#39;s performance; and  
         [0014]      FIG. 6  is another flowchart describing a method of one example of improving a distributed system&#39;s performance. 
     
    
     DESCRIPTION  
       [0015]     Although the following text sets forth a detailed description of numerous different embodiments, it should be understood that the legal scope of the description is defined by the words of the claims set forth at the end of this patent. The detailed description is to be construed as exemplary only and does not describe every possible embodiment since describing every possible embodiment would be impractical, if not impossible. Numerous alternative embodiments could be implemented, using either current technology or technology developed after the filing date of this patent, which would still fall within the scope of the claims.  
         [0016]     It should also be understood that, unless a term is expressly defined in this patent using the sentence “As used herein, the term ‘______’ is hereby defined to mean . . . ” or a similar sentence, there is no intent to limit the meaning of that term, either expressly or by implication, beyond its plain or ordinary meaning, and such term should not be interpreted to be limited in scope based on any statement made in any section of this patent (other than the language of the claims). To the extent that any term recited in the claims at the end of this patent is referred to in this patent in a manner consistent with a single meaning, that is done for sake of clarity only so as to not confuse the reader, and it is not intended that such claim term by limited, by implication or otherwise, to that single meaning. Finally, unless a claim element is defined by reciting the word “means” and a function without the recital of any structure, it is not intended that the scope of any claim element be interpreted based on the application of 35 U.S.C. § 112, sixth paragraph.  
         [0017]      FIG. 1  illustrates a network  10  that may be used to support a distributed system. The network  10  may be the Internet, a virtual private network (VPN), or any other network that allows one or more computers, communication devices, databases, etc., to be communicatively connected to each other. The network  10  may be connected to a personal computer  12  and a computer terminal  14  via an Ethernet  16  and a router  18 , and a landline  20 . On the other hand, the network  10  may be wirelessly connected to a laptop computer  22  and a personal data assistant  24  via a wireless communication station  26  and a wireless link  28 . Similarly, a server  30  may be connected to the network  10  using a communication link  32  and a mainframe  34  may be connected to the network  10  using another communication link  36 .  
         [0018]      FIG. 2  illustrates a computing device in the form of a computer  110  that may be connected to the network  10  and may participate in a distributed system. Further,  FIG. 2  illustrates an example of a suitable computing system environment  100  on which a system for the steps of the claimed method and apparatus may be implemented. The computing system environment  100  is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the method of apparatus of the claims. Neither should the computing environment  100  be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment  100 .  
         [0019]     The steps of the claimed method and apparatus are operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known computing systems, environments, and/or configurations that may be suitable for use with the methods or apparatus of the claims include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.  
         [0020]     The steps of the claimed method and apparatus may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. The methods and apparatus may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.  
         [0021]     With reference to  FIG. 2 , an exemplary system for implementing the steps of the claimed method and apparatus includes a general purpose computing device in the form of a computer  110 . Components of computer  110  may include, but are not limited to, a processing unit  120 , a system memory  130 , and a system bus  121  that couples various system components including the system memory to the processing unit  120 . The system bus  121  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus, and the Peripheral Component Interconnect Express (PCI-E) bus.  
         [0022]     Computer  110  typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer  110  and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by computer  110 . Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of the any of the above should also be included within the scope of computer readable media.  
         [0023]     The system memory  130  includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM)  131  and random access memory (RAM)  132 . A basic input/output system  133  (BIOS), containing the basic routines that help to transfer information between elements within computer  110 , such as during start-up, is typically stored in ROM  131 . RAM  132  typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit  120 . By way of example, and not limitation,  FIG. 1  illustrates operating system  134 , application programs  135 , other program modules  136 , and program data  137 .  
         [0024]     The computer  110  may also include other removable/non-removable, volatile/nonvolatile computer storage media. By way of example only,  FIG. 2  illustrates a hard disk drive  140  that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive  151  that reads from or writes to a removable, nonvolatile magnetic disk  152 , and an optical disk drive  155  that reads from or writes to a removable, nonvolatile optical disk  156  such as a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive  141  is typically connected to the system bus  121  through a non-removable memory interface such as interface  140 , and magnetic disk drive  151  and optical disk drive  155  are typically connected to the system bus  121  by a removable memory interface, such as interface  150 .  
         [0025]     The drives and their associated computer storage media discussed above and illustrated in  FIG. 2  provide storage of computer readable instructions, data structures, program modules and other data for the computer  110 . In  FIG. 2 , for example, hard disk drive  141  is illustrated as storing operating system  144 , application programs  145 , other program modules  146 , and program data  147 . Note that these components can either be the same as or different from operating system  134 , application programs  135 , other program modules  136 , and program data  137 . Operating system  144 , application programs  145 , other program modules  146 , and program data  147  are given different numbers here to illustrate that, at a minimum, they are different copies. A user may enter commands and information into the computer  110  through input devices such as a keyboard  162  and pointing device  161 , commonly referred to as a mouse, trackball or touch pad. Other input devices (not shown) may include a microphone, joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  120  through a user input interface  160  that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). A monitor  191  or other type of display device is also connected to the system bus  121  via an interface, such as a video interface  190 . In addition to the monitor, computers may also include other peripheral output devices connected through an output peripheral interface  195 .  
         [0026]     The computer  110  may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer  180 . The remote computer  180  may be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer  110 , although only a memory storage device  181  has been illustrated in  FIG. 2 . The logical connections depicted in  FIG. 2  include a local area network (LAN)  171  and a wide area network (WAN)  173 , but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet.  
         [0027]     When used in a LAN networking environment, the computer  110  is connected to the LAN  171  through a network interface or adapter  170 . When used in a WAN networking environment, the computer  110  typically-includes a modem  172  or other means for establishing communications over the WAN  173 , such as the Internet. The modem  172 , which may be internal or external, may be connected to the system bus  121  via the user input interface  160 , or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer  110 , or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,  FIG. 2  illustrates remote application programs  185  as residing on memory device  181 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used.  
         [0028]     A method and apparatus for evolving a distributed system may execute or reside on a computing device  110  or a remote computing device  180  as an application in memory. For example, the method and apparatus may execute or reside in system memory  130  and may include, without limitation, an application in the BIOS  133 , operating system  134 , application programs  135 , other program modules  136 , and the program data  137 . Additionally, the method and apparatus may execute or reside in non-removable, non-volatile memory  141  and may operate as part of, without limitation, the operating system  144 , application programs  145 , other program modules  146 , or the program data  147 . Further, the method and apparatus may execute or reside in removable, non-volatile memory  151 ,  155  such as a floppy disk  152  or an optical disk  156 . The method and apparatus may also relate to remote application programs  185  executing on the remote computer  180 . Additionally the method and apparatus may relate to any hardware device, interface, network connection, internal or external connection associated with the computers  110 ,  180 .  
         [0029]      FIG. 3  is a diagram of nodes, such as the computers described in  FIGS. 1 and 2 , participating in a distributed system  300 . The distributed system  300  may be a point-to-point peer-to-peer network, with communication between nodes on a one-to-one basis. The distributed system  300  may also be a multicast peer-to-peer network, with communication on a one-to-many basis. Each node may have a cache of known other nodes. In general, nodes are likely to have more information about nodes closer to them than nodes farther away for some metric of proximity, for example, numerical proximity. When a first node searches for another node, it may first ask neighbors numerically closer to the destination if they have data regarding the target node. If the neighbor node does, it may be asked to forward the message or request. To illustrate using  FIG. 3 , node  305  is trying to connect to node  390 . Node  305  may first ask node  310  to forward a message on its behalf. Node  310  does not know about node  390  but has in its cache an entry for node  315 , which has a closer peer-to-peer identifier to node  390 . Node  315  may then forward the message to node  320 . Node  320  has a cache entry for node  390  and forwards the message to the ultimate destination. In this example, each node was progressively closer to the destination node. While this is not always the case, and some retries may occur, this is sufficient to illustrate the basic routing scheme. On a small scale this distributed system is relatively simple to construct and maintain. However, when scaled to millions or hundreds of millions of nodes, the performance of the network fabric may become difficult to evaluate when making changes to the hardware and software of the distributed system.  
         [0030]     With reference to  FIG. 4  and  FIG. 5 , variables affecting the performance of the distributed system  300  may be dynamically derived through a neural network  400 , and the neural network  400  may represent the complex interaction of multiple network variables. The neural network  400  may derive an optimal set of system variables, save the variables, and use the derived variables in future system operations.  
         [0031]     A neural network may be an interconnected group of computational units generically referred to as “nodes” (noting that the use of the term “node” in reference to the neural network is different than the “node” associated with distributed systems), or neurons, that uses a mathematical model for information processing based on a connectionist approach to computation. A neural network may be an adaptive system that changes its structure based on external or internal information that flows through the network. More generally, a neural network may be a non-linear statistical modeling tool that may model complex relationships between inputs and outputs or it may find patterns in data.  
         [0032]     A simple neural network  400  may have three layers: an input layer  405 , a hidden layer  410 , and an output layer  415 . The neural network  400  of  FIG. 4  is for illustration purposes only and persons having ordinary skill in the art should recognize that the system disclosed herein may utilize a neural network having multiple hidden layers and any number of inputs, nodes, and outputs at each layer described depending on the complexity of the system that the neural network  400  describes. The input layer  405  may represent a starting point for the input of different system metrics  420 ,  425 ,  430 . The metrics may be a numerical representation or the performance of a particular portion or all of a distributed system. The metrics may be generally described by U.S. patent application Ser. No. 11/089,894 entitled “System and Method for Monitoring and Reacting to Peer-to-Peer Network Metrics” to Horton, et al. and may include a distributed system number of nodes, an average system latency, an average packet loss, system operation frequencies, cache churn rates, or any other quantifiable aspect of the system performance.  
         [0033]     At step  502 , an ideal system health index may be derived according to U.S. patent application Ser. No. 11/089,894 entitled “System and Method for Monitoring and Reacting to Peer-to-Peer Network Metrics” to Horton, et al., or via some other algorithm. The ideal system health index may represent a distributed system that is optimized for a particular system metric, or optimized for an overall system performance. The ideal system health index may represent any desired performance characteristic of the distributed system  300  and may be described as a quantifiable representation of a user-desired performance characteristic of the distributed system  300 .  
         [0034]     At step  505 , the system metrics  420 ,  425 ,  430 , may be multiplied by weights  432  as they pass from the input layer  405  to the hidden layer  410 . As illustrated in  FIG. 4 , each arrow comprising the weights  432  may represent a different weight by which each system metric  420 ,  425 ,  430 , may be multiplied. Therefore, each metric  420 ,  425 ,  430 , may be represented as an n-dimensional vector, where “n” represents the number of nodes at the hidden level. In this example, each system metric  420 ,  425 ,  430 , may be represented by a 4-dimensional vector (four hidden layer  410  nodes) after being processed by the weights  432 .  
         [0035]     At step  510 , the system metrics  420 ,  425 ,  430 , may be further processed by functions at each hidden node  435 ,  440 ,  445 ,  450 . The functions at the hidden nodes may represent the complex interaction of the system metrics  420 ,  425 ,  430 , or any other system metrics, and may account for multiple system metric dependencies. For example, the function at hidden node  435  may represent the previously-described relationship between system packet loss and the number of system nodes.  
         [0036]     At step  515 , if the neural network  400  includes additional hidden layers  410 , the system metrics  420 ,  425 ,  430  may be multiplied by additional weights and processed by additional functions as in step  505  and  510  at subsequent hidden layer nodes.  
         [0037]     At step  520 , if there are no additional hidden layer  410  nodes, the results from each hidden node  435 ,  440 ,  445 ,  450 , may again be multiplied by weights  452  and converted into distributed system variables  455 ,  460 .  
         [0038]     At step  525 , the distributed system variables  455  and  460  are passed to the output layer  415  as an n-dimensional vector (here, a 2-dimensional vector) represented at nodes  455  and  460 . The neural network  400  may result in any number of output layer  415  nodes representing values for individual network variables or combinations of network variables. The system variables may include a node timeout value, a DHT keyspace value, a routing table size, or any other value that may be used to modify the performance of a particular distributed system node or combination of nodes.  
         [0039]     At step  530 , the system variable values  455 ,  460 , may then be used to modify the performance of either a live or simulated distributed system  300 . The simulated or live distributed system may then be observed for its performance.  
         [0040]     At step  535 , the metrics describing the performance of the distributed system  300  with the derived network variables  455 ,  460 , may be aggregated into a measured system health index as described in paragraphs 25-29 of U.S. patent application Ser. No. 11/089,894 entitled “System and Method for Monitoring and Reacting to Peer-to-Peer Network Metrics” to Horton, et al., or via another algorithm. Instrumentation at the distributed system  300  nodes, or a subset of the nodes, may collect performance statistics about the particular node. Individual node statistics may be reported to a system controller at an individual system node, or may be sent to a node outside of the distributed system  300  where the statistics may be aggregated or configured into a distributed system health index.  
         [0041]     At step  540 , the measured system health index may be compared against the ideal system health index as described at step  502  to derive a health index error value.  
         [0042]     At step  545 , if the health index error value falls into a range of acceptable tolerance or matches the ideal system health index of step  502 , then, at step  550 , the method may save the derived network variables  430 ,  432  and use them in future distributed system  300  operations, thus ending the method.  
         [0043]     At step  555 , if the health index does not indicate an optimal or acceptable distributed system  300  performance, the health index error value may be used as a correction factor for the values of the distributed system variables  430 ,  432  derived by the neural network in step  515 .  
         [0044]     At step  560 , the corrected distributed system variables may be used in a learning algorithm to modify the neural network  400  to derive a set of system variables  430 ,  432 , that may result in a distributed system that may perform more closely to the ideal health index derived in step  502 . One example of a suitable neural network learning algorithm may be the back propagation algorithm described in “Explorations in Parallel Distributed Processing”, Rumelhart, et al., MIT Press, 1988 the entire disclosure of which is hereby incorporated by reference. After completing step  560 , the method may return to step  505  to modify the neural network  400  and re-calculate the network variables.  
         [0045]     With reference to  FIG. 4  and  FIG. 6 , a neural network  400  may continuously or periodically evolve the network variables  430 ,  432  of a simulated or live distributed system  300  to adapt to changing distributed system  300  configurations.  
         [0046]     At step  602 , an ideal system health index representing a distributed system that is optimized for a particular system metric, or optimized for an overall system performance may be derived according to U.S. patent application Ser. No. 11/089,894 entitled “System and Method for Monitoring and Reacting to Peer-to-Peer Network Metrics” to Horton, et al. as previously described. At step  605 , the system metrics  420 ,  425 ,  430 , may be multiplied by weights  432  as they pass from the input layer  405  to the hidden layer  410 . At step  610 , the system metrics  420 ,  425 ,  430 , may be further processed by functions representing the complex interaction the of system metrics  420 ,  425 ,  430 , or any other system metrics, and may account for multiple system metric dependencies at each hidden node  435 ,  440 ,  445 ,  450 . At step  615 , if the neural network  400  includes additional hidden layers  410 , the system metrics  420 ,  425 ,  430  may be multiplied by additional weights and processed by additional functions as in step  605  and  610  at subsequent hidden layer nodes. At step  620 , if there are no additional hidden layer  410  nodes, the results from each hidden node  435 ,  440 ,  445 ,  450 , may again be multiplied by weights  452  and converted into distributed system variables  430 ,  432 . At step  625 , the distributed system variables  430  and  432  are passed to the output layer  415  resulting in any number of output layer  415  nodes representing values for individual network variables or combinations of network variables. The system variables may include a node timeout value, a DHT keyspace value, a routing table size, or any other value that may be used to modify the performance of a particular distributed system node or combination of nodes. At step  630 , the system variable values  430 ,  432 , may then be used to modify the performance of either a live or simulated distributed system  300 . The simulated or live distributed system may then be observed for its performance and, at step  635 , the metrics describing the performance of the distributed system  300  with the derived network variables  430 ,  432 , may be aggregated into a measured system health index as described in U.S. patent application Ser. No. 11/089,894. At step  640 , the measured system health index may be compared against the ideal system health index as described at step  602  to derive a health index error value.  
         [0047]     At step  645 , if the health index error value falls into a range of acceptable tolerance or matches the ideal system health index of step  602 , then, at step  650 , the method may save the derived network variables  430 ,  432  and utilize them in the distributed system operation.  
         [0048]     At step  655 , the method may monitor the distributed system  300  by observing the distributed system in operation with the variables as determined by the neural network in the previous steps. During monitoring, the method may periodically or continuously derive a measured system health index according to U.S. patent application Ser. No. 11/089,894, paragraphs 25-29, by returning to step  635  and completing the previously-described, subsequent steps.  
         [0049]     At step  660 , if the health index error value falls outside of an acceptable range or below a threshold tolerance as determined at step  645 , the method may determine that the system  300  is not performing optimally, and correct the variables  430 ,  432  using the health index error as a factor.  
         [0050]     At step  665 , the method may initiate a suitable learning algorithm to modify the neural network to derive another set of system variables  430 ,  432  that may reduce the health index error. One suitable neural network learning algorithm may be the back propagation algorithm described in “Explorations in Parallel Distributed Processing”, Rumelhart, et al., MIT Press, 1988.  
         [0051]     At step  670 , after modifying the neural network  400  using a suitable learning algorithm, the method may derive a new set of system variables  430 ,  432  and return to step  630  to modify the distributed system with the new system variables and continue the remaining steps.  
         [0052]     Although the forgoing text sets forth a detailed description of numerous different embodiments, it should be understood that the scope of the patent is defined by the words of the claims set forth at the end of this patent. The detailed description is to be construed as exemplary only and does not describe every possible embodiment because describing every possible embodiment would be impractical, if not impossible. Numerous alternative embodiments could be implemented, using either current technology or technology developed after the filing date of this patent, which would still fall within the scope of the claims.  
         [0053]     Thus, many modifications and variations may be made in the techniques and structures described and illustrated herein without departing from the spirit and scope of the present claims. Accordingly, it should be understood that the methods and apparatus described herein are illustrative only and are not limiting upon the scope of the claims.