# Patent

## Title
Aggregate system resource analysis including correlation matrix and metric-based analysis

## Abstract

A system for gathering data for purposes of analyzing a network. The data is gathered based on values that are passed from node-to-node within processing devices, such as servers, in a network. The values are generated from characteristics obtained from measuring device performance and resource utilization. Values are passed peer-to-peer and successively combined with values at each receiving system so that, ultimately, a value is obtained that reflects the operation of a group of devices. A correlation matrix is maintained to indicate discrepancies in value meanings from different devices. The correlation matrix is used to more accurately combine values to achieve meaningful composite values.

## Background
CLAIM OF PRIORITY
This application claims priority from U.S. Provisional Patent Application No. 60/243,783, filed Oct. 26, 2000.
CROSS-REFERENCES TO RELATED APPLICATIONS
This application is related to the following co-pending applications, each of which is incorporated by reference as if set forth in full in this application:
U.S. Patent Application entitled “System-Wide Optimization Integration Model” filed on Oct. 12, 2001, Ser. No. 09/976,368; U.S. Patent Application entitled “Multi-Platform Optimization Model” filed on Oct. 12, 2001, Ser. No. 09/976,518; and, U.S. Patent Application entitled “Application Program Interface for Optimization Model” filed on Oct. 26, 2001, Ser. No. 10/055,404.


BACKGROUND OF THE INVENTION
Digital computer networks, such as the Internet, are now used extensively in many aspects of commerce, education, research and entertainment. Because of the need to handle high volumes of traffic, many Internet sites are designed using several groups of server computers. An example of a site network system is shown in FIG. 1A.
In FIG. 1A, network system 10 includes four major tiers. These are communications tier 12, web tier 14, application tier 16 and database tier 18. Each tier represents an interface between a group of server computers or other processing, storage or communication systems. Each interface handles communication between two groups of server computers. Note that the tiers are significant in that they represent the communication protocols, routing, traffic control and other features relating to transfer of information between the groups of server computers. As is known in the art, software and hardware is used to perform the communication function represented by each tier.
Server computers are illustrated by boxes such as 20. Database 22 and Internet 24 are represented symbolically and can contain any number of servers, processing systems or other devices. A server in a group typically communicates with one or more computers in adjacent groups as defined and controlled by the tier between the groups. For example, a request for information (e.g., records from a database) is received from the Internet and is directed to server computer 26 in the Web-Com Servers group. The communication takes place in communications tier 12.
Server computer 26 may require processing by multiple computers in the Application Servers group such as computers 20, 28 and 30. Such a request for processing is transferred over web tier 14. Next, the requested computers in the Application Servers group may invoke computers 32, 34, 36 and 38 in the Database Servers group via application tier 16. Finally, the invoked computers make requests of database 22 via database tier 18. The returned records are propagated back through the tiers and servers to Internet 24 to fulfill the request for information.
Of particular concern in today's large and complex network systems is monitoring the performance of, and optimizing, the system. One way that prior art approaches monitor system performance is to use a process at certain points in the network to report data back to a central location such as console 40. In FIG. 1A, the request for database records can be monitored by having a process at server 26 log the time and nature of the request. A process at server 20 then logs the time at which a request from server 26 is received. Similarly, server 32 (or whichever server receives the database request from server 20) logs its participation in the transaction. This “chain” of logged transactions is illustrated by bold arrows in FIG. 1A.
In this manner, the prior art monitoring system can determine how long it takes for a request for a record to propagate through the network. The transaction can also be tracked in the other direction to determine how long it takes to fulfill the request. The nature of such data logging is complex since a server in one tier, or group, may ask multiple other servers for assistance, or processing. Also, different servers can be asked at different points in time. The speed at which requests, processing and transactions occur can cause large amounts of data to be logged very rapidly. At some later time, the data is transferred to console 40. Console 40 acts to resolve the data and produce meaningful results about system performance that can be analyzed by a human administrator.
A problem with the prior art approach is that the logging processes are segregated and do little, if any, communication with each other. This means that complex dependencies among processes, servers, etc., are not accurately analyzed. The logging processes tend to create high overhead in the host servers in which they execute. One approach uses the console to poll the processes. Frequent polling of many processes also creates excessive overhead. Optimization and performance improvement based on the prior art approach is hampered by the use of disparate platforms and the lack of more encompassing analysis. Having to dump data to the console at intervals, and then have the data resolved, ultimately means that monitoring is not performed in real time.
Thus, it is desirable to provide a system that improves upon one or more shortcomings in the prior art.
BRIEF SUMMARY OF THE INVENTION
The invention provides a system for gathering data for purposes of analyzing a network. The data is gathered based on values that are passed from node-to-node within processing devices, such as servers, in a network. The values are generated from characteristics obtained from measuring device performance and resource utilization. Values are passed peer-to-peer and successively combined with values at each receiving system so that, ultimately, a value is obtained that reflects the operation of a group of devices. A correlation matrix is maintained to indicate discrepancies in value meanings from different devices. The correlation matrix is used to more accurately combine values to achieve meaningful composite values.
In one embodiment the invention provides a method for collecting information about a network's operation, wherein the network includes a plurality of devices, the method comprising using peer-to-peer communication among a plurality of devices in the network to obtain a measure of the network performance.



BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1A shows network performance measured in a prior art system;
FIG. 1B shows network performance measured according to the present invention;
FIG. 2A shows intelligence objects and performance value passing in the present invention;
FIG. 2B illustrates architectural components of the present invention; and
FIG. 2C illustrates a network system with multiple platforms.



DETAILED DESCRIPTION OF THE INVENTION
A preferred embodiment of the present invention is incorporated into products, documentation and other systems and materials created and distributed by MetiLinx, Inc. as a suite of products referred to as “Metilinx iSystem Enterprise” system. The Metilinx system is designed to monitor and optimize digital networks, especially networks of many computer servers in large Internet applications such as technical support centers, web page servers, database access, etc.
The system of the present invention uses software mechanisms called “intelligence objects” (IOs) executing on the various servers, computers, or other processing platforms, in a network. The intelligence objects are used to obtain information on the performance of a process or processes, hardware operation, resource usage, or other factors affecting network performance. Values are passed among the intelligence objects so that a composite value that indicates the performance of a greater portion of the network can be derived.
FIG. 2A illustrates intelligence objects and value passing. In FIG. 2A, intelligence objects such as 102 and 104 reside in computer servers. Any number of intelligence objects can reside in a server computer and any number of server computers in the n-tiered system can be equipped with one or more intelligence objects. A first type of intelligence object is a software process called a system level object (SLO) that can monitor and report on one or more aspects of other processes or hardware operating in its host computer server. A second type of intelligence object, called a transaction level object (TLO) is designed to monitor transaction load with respect to its host computer or processes executing within the host computer.
In one embodiment, IO 102 measures a performance characteristic of its host computer and represents the characteristic as a binary value. This value is referred to as the “local” utilization value since it is a measure of only the host computer, or of transaction  information relating to the host computer. The local utilization value is passed to IO 104. IO 104 can modify the passed value to include a measurement of its own host computer. The modified value is referred to as a “composite” utilization value. The composite utilization value can, in turn, be passed on to other intelligence objects that continue to build on, or add to, the measurements so that performance across multiple computer, tiers, operating systems, applications, etc., is achieved.
Ultimately, the utilization value, or values, is passed on to other processes which can display the result of the combined measurements to a human user, use the result to derive other results, use the result to automate optimization of the n-tiered system, or use the result for other purposes. One aspect of the invention provides for redirecting processes and interconnections on the network based on the assessed utilization values of the computers, or nodes, in order to improve, or optimize, network performance. The processes that perform the redirection are referred to as “process redirection objects.”
Note that although the invention is sometimes discussed with respect to a multi-tiered server arrangement that any arrangement of servers, computers, digital processors, etc., is possible. The term “processing device” is used to refer to any hardware capable of performing a function on data. Processing devices include servers, computers, digital processors, storage devices, network devices, input/output devices, etc. Networks need not be in a multi-tiered arrangement of processing devices but can use any arrangement, topology, interconnection, etc. Any type of physical or logical organization of a network is adaptable for use with the present invention.
FIG. 2B illustrates one possible arrangement of more specific components of the present invention. Note that the term “component” as used in this specification includes any type of processing device, hardware or software that may exist within, or may be executed by, a digital processor or system.
Systems such as those illustrated in FIGS. 1, 2A and 2B, along with virtually any type of networked system, can be provided with IOs. In a preferred embodiment, the IOs are installed on each server in the network in a distributed peer-to-peer architecture. The IOs measure real-time behavior of the servers components, resources, etc. to achieve an overall measure of the behavior and performance of the network.
A software system for populating a network with nodes, and for monitoring, analyzing, managing and optimizing a network is provided in the co-pending applications cited above.
A preferred embodiment collects data on low-level system and network parameters such as CPU utilization, network utilization, latency, etc. About 400 different measured characteristics are used. Table I, below, lists some of the characteristics that are monitored in the preferred embodiment.










BEGIN TABLE I















SYSTEM


File Read Operations/sec


File Write Operations/sec


File Control Operations/sec


File Control Operations/sec


File Write Bytes/sec


File Control Bytes/sec


Context Switches/sec


System Calls/sec


% Total Processor Time


% Total User Time


% Total Privileged Time


Total Interrupts/sec


System Up Time


Alignment Fixups/sec


Exception Dispatches/sec


Floating Emulations/sec


% Total DPC Time


% Total Interrupt Time


Total DPCs Queued/sec


Total DPC Rate


Total DPC Bypasses/sec


Total APC Bypasses/sec


% Registry Quota In Use


PROCESSOR


% Processor Time


% User Time


% Privileged Time


% DPC Time


% Interrupt Time


Interrupts/sec


DPCs Queued/sec


DPC Rate


DPC Bypasses/sec


APC Bypasses/sec


MEMORY


Available Bytes


Committed Bytes


Commit Limit


Write Copies/sec


Transition Faults/sec


Cache Faults/sec


Demand Zero Faults/sec


Pages/sec


Pages Input/sec


Page Reads/sec


Pages Output/sec


Page Writes/sec


Pool Paged Bytes


Pool Nonpaged Bytes


Pool Paged Allocs


Pool Nonpaged Allocs


Free System Page Table Entries


Cache Bytes


Cache Bytes Peak


Pool Paged Resident Bytes


System Code Total Bytes


System Code Resident Bytes


System Driver Total Bytes


System Driver Resident Bytes


System Cache Resident Bytes


% Committed Bytes In Use


% Committed Bytes In Use


PHYSICAL DISK


Current Disk Queue Length


% Disk Time


Avg. Disk Queue Length


% Disk Read Time


Avg. Disk Read Queue Length


% Disk Write Time


Avg. Disk Write Queue Length


Avg. Disk sec/Transfer


Avg. Disk sec/Read


Avg. Disk sec/Write


Disk Transfers/sec


Disk Reads/sec


Disk Writes/sec


Disk Bytes/sec


Disk Read Bytes/sec


Disk Write Bytes/sec


Avg. Disk Bytes/Transfer


Avg. Disk Bytes/Read


Avg. Disk Bytes/Write


LOGICAL DISK


% Free Space


Free Megabytes


Current Disk Queue Length


% Disk Time


Avg. Disk Queue Length


% Disk Read Time


Avg. Disk Read Queue Length


% Disk Write Time


Avg. Disk Write Queue Length


Avg. Disk sec/Transfer


Avg. Disk sec/Read


Avg. Disk sec/Write


Disk Transfers/sec


Disk Reads/sec


Disk Writes/sec


Disk Bytes/sec


Disk Read Bytes/sec


Disk Write Bytes/sec


Avg. Disk Bytes/Transfer


THREADS


% Processor Time


% User Time


% Privileged Time


Context Switches/sec


Elapsed Time


Priority Current


Priority Base


Start Address


Thread State


Thread Wait Reason


ID Process


ID Thread


OBJECTS


Processes


Threads


Events


Semaphores


Mutexes


Sections


SERVER


Bytes Total/sec


Bytes Received/sec


Bytes Transmitted/sec


Sessions Timed Out


Sessions Errored Out


Sessions Logged Off


Sessions Forced Off


Errors Logon


Errors Access Permissions


Errors Granted Access


Errors System


Blocking Requests Rejected


Work Item Shortages


Files Opened Total


Files Open


Server Sessions


File Directory Searches


Pool Nonpaged Bytes


Pool Nonpaged Failures


Pool Nonpaged Peak


Pool Paged Bytes


Pool Paged Failures


Pool Paged Peak


Context Blocks Queued/sec


Logon/sec


Logon Total


SERVER WORK QUEUES


Queue Length


Active Threads


Available Threads


Available Work Items


Borrowed Work Items


Work Item Shortages


Current Clients


Bytes Received/sec


Bytes Sent/sec


Bytes Transferred/sec


Read Operations/sec


Read Bytes/sec


Write Operations/sec


Write Bytes/sec


Total Bytes/sec


Total Operations/sec


Context Blocks Queued/sec


CPU PERFORMANCE


processor ID


minor faults


major faults


inter-processor cross-calls


interrupts


interrupts as threads


context switches


involuntary context switches


thread migrations


spins on mutexes


spins on readers/writer locks


system calls


percent user time


percent system time


percent wait time


percent idle time


processor set membership of the CPU


read() + readv() system calls


write() + writev() system calls


forks


vforks


execs


bytes read by rdwr()


bytes written by rdwr()


terminal input characters


chars handled in canonical mode


terminal output characters


msg count (msgrcv() + msgsnd() calls)


CPU PERFORMANCE


semaphore ops count (semop () calls)


pathname lookups


ufs_iget() calls


directory blocks read


inodes taken with attached pages


inodes taken with no attached pages


inode table overflows


file table overflows


proc table overflows


interrupts as threads (below clock)


intrs blkd(swtch)


times idle thread scheduled


involuntary context switches


thread_create()s


cpu migrations by threads


xcalls to other cpus


failed mutex enters (adaptive)


rw reader failures


rw writer failures


times loadable module loaded


times loadable module unloaded


physical block writes (async)


tries to acquire rw lock


reg window user overflows


reg window user underflows


reg window system overflows


reg window system underflows


reg window system user overflows


procs waiting for block I/O


page reclaims (includes pageout)


page reclaims from free list


pageins


pages paged in


pageouts


pages paged out


swapins


pages swapped in


swapouts


pages swapped out


pages zero filled on demand


pages freed by daemon or auto


pages examined by pageout daemon


revolutions of the page daemon hand


minor page faults via hat_fault()


minor page faults via as_fault()


major page faults


copy-on-write faults


protection faults


faults due to software locking req


as_fault()s in kernel addr space


times pager scheduled


executable pages paged in


executable pages paged out


executable pages freed


anon pages paged in


anon pages paged out


anon pages freed


fs pages paged in


fs pages paged out


fs pages free


FILE SYSTEM


file access system routines


buffer activity


data transfers per second


accesses of system buffers.


cache hit ratios


raw transfers


system calls


specific system calls.


characters transferred


block device activity


device load ratio


average wait time


average service time


physical block reads


physical block writes (sync + async)


logical block reads


logical block writes


KERNEL STATISTICS (KSTATS)


Interrupt statistics


Event timer statistics


I/O statistics


NETWORK


Packet log and description


RPC response time


VIRTUAL MEMORY


processes in run queue


processes runnable but swapped


virtual and real memory.


swap space currently available


size of the free list


page faults and paging activity


page reclaims


minor faults


kilobytes paged in


kilobytes paged out


kilobytes freed


anticipated short-term memory shortfall


pages scanned by clock algorithm


disk operations per second


trap/interrupt rates (per second).


device interrupts


system calls


CPU context switches


percentage usage of CPU time


user time


system time


idle time


swap ins


swap outs


pages swapped in


pages swapped out


PROCESSES


forks


vforks


lexecs


cpu context switches


device interrupts


traps


system calls


total name lookups


user cpu


system cpu


idle cpu


wait cpu


process start time


usr + sys cpu time for this process


usr + sys cpu time for reaped children


priority


% of recent cpu time


% of system memory used by the process


CPU PERFORMANCE


user time


system time


idle time


nice time


average load


number of processes


run queue length


VIRTUAL MEMORY


pages swapped in


pages swapped out


Total amount of memory


Amount of memory in use


Currently unused real memory.


Total amount of shared memory


Memory used for buffers


Total swap space(s)


Used disk swap


Free swap space.


Cached memory


NETWORK


For each interface:


Number of received packets from system boot.


Number of received mangled packets from system boot.


Number of received but dropped packets.


Number of received packet with framing error.


Number of transmitted packets.


Number of errors during transmitting. i.e. late detected collision.


Number of dropped packets.


Number of collisions during transmitting.


Number of carrier loses during transmitting.


Allocated buffers


Socket usage per protocol


Resource usage per protocol


Kernel ARP table


NETWORK


network devices with statistics


Lists the Layer2 multicast groups a device is listening to (interface index,


label, number of references, number of bound addresses).


number of pages that are ′code′


number of pages of data/stack


network device status


Firewall chain linkage


Firewall chains


Network statistics


Raw device statistics


Routing cache


Socket statistics


PROCESSES


forks


cpu context switches


device interrupts


state (R, S, D, Z, T)


minor faults


major faults


timeslices


user mode jiffies


kernel mode jiffies


nice value


timeout jiffies


uptime jiffies


Virtual memory size.


Resident number of pages


total program size


size of in memory portions


number of the pages that are shared


number of pages of library


number of dirty pages


PHYSICAL DISK


I/O statistics


Number of read requests to whole disk not only for partitions.


Number of write requests to whole disk.


Number of read blocks from whole disk.


Number of written blocks from whole disk.


LOGICAL DISK


Busy time


Average queue length


Sectors read/written


Blocks read/written


Average waiting time


GENERAL SETS


Local response process latency


Local resource access latency (physical and virtual memory, hdd, sockets,


etc).


Spanned resource access latency (physical and virtual memory, hdd,


sockets, etc). System process flow organized across functional groups


Single arc latency between nodes


Combined arc latency across subnet


Processor time slice assignments


Functional resource arc analysis in tier-specific evaluations of standard


and native parameters (related to web servers, database servers and


different app servers)


Functional resource arc analysis of harmonized Native Sensor Data and


harmonized multi platform data


Application priorities


Transactional priorities







END TABLE I









Data is produced at each node as a four-byte value reflecting the characteristics of the host processing system for the node. These values are referred to as Local Node Values (LNVs). Multiple LNVs from different nodes are combined into a composite value called a Composite Node Value (CNV). CNVs can also include CNVs passed by other nodes.
The CNVs remain four-bytes in size. A CNV is passed along the network hierarchy and used to obtain further composite values by combining with a LNV at successive nodes so that overall system performance is ultimately provided in the composite values. Node value propagation is typically organized into organizational and functional blocks, as described in the related applications. Typically, node value propagation is in the direction of dependencies, or counter to request flow. However, since request flow and dependencies are loosely adhered to in any particular network (and can change with time) the system of the present invention can adapt to changing conditions. In general, the passing of node values can change dynamically, can be one-to-many or many-to-one and is bi-directional. Thus, unlike the limited directional “chaining” of prior art systems as shown in FIG. 1A, the system of the present invention can provide flexible peer-to-peer value passing. Performance and usage information from many nodes can be combined in varied patterns to achieve more versatile analysis structures such as that illustrated in FIG. 1B (by bold arrows).
Naturally, in other embodiments, the local and composite values can be of any size, varying sizes, etc. The values can be more complex data structures as opposed to “values.” Any combination of network characteristics can be measured.
LNVs and CNVs are made up of four sub-values. Each sub-value is a byte of data with a higher value (e.g., 255) indicating optimal functioning in the sub-value's associated network property. A first sub-value is a System Balance Value (SBV). The SBV measures the balanced operation of server nodes within functional groups. Functional groups are designated by a user/administrator and are used by the system of the present invention to define groups among which CNVs accumulate values. A higher SBV value indicates that functional groupings of server nodes are operating in good balance.
A second sub-value is the System Utilization Value (SUV). The SUV represents the system resource utilization, based on analyses of individual and aggregated resource nodes. A higher values indicates that resources are being utilized more efficiently.
A third sub-value is the Performance Optimization Value (POV). The POV represents the metric for speed or response of the system resources. A higher value means that response times are shorter, or that speed of response is higher.
A fourth, and final, sub-value is called the MetiLinx Optimization Value (MOV). The MOV indicates the degree of total system optimization. A high value indicates that functional groups are more optimally balanced. The MOV reflects the other sub-values of balance, resource utilization and speed of response.
In order to meaningfully composite LNV and CNV values received from other nodes, each node maintains a “correlation matrix.” The correlation matrix includes numerical weighting factors based on differences in characteristics of different node environments in the network. For example, best performance values can be maintained for every node in the system. Node A might be recorded at a best performance combination of 90% utilization and a 3 second response. Node B might have a 90% utilization with a 2 second response. When node C receives LNV or CNV values indicating 90% utilization with a 3 second response for each node, node C is now aware that node A's host environment is operating at a high performance while node B's environment is operating at a lower than desired utilization since the response time is slower than previously achieved. In generating a CNV from node A and B values, node C's process combines the utilization and response times by weighting according to the correlation matrix. In this simplified example, if “A” is the dependency of node C on node A's utilization (for node C's efficient operation and utilization), while “B” is the dependency of node C on node B's utilization, then the CNV at node C can be computed as A+(B* 2)/3.
Each node's correlation matrix is updated based on information the node receives from other nodes. For example, if node C is informed that node B is now operating at 90% utilization with a 1 second response time, node C's correlation matrix factors with respect to node B are updated. Note that the correlation matrix is multi-dimensional. With the simplified example, alone, there can be a two dimensional array for utilization versus response time for each node.
In a preferred embodiment the correlation matrix is stored locally to the node process. Usually the correlation matrix resides in fast RAM in the node's host processing system. However, other embodiments can use variations on the correlation matrix and can maintain and access the correlation matrix in different ways. For example, correlation matrices can be stored on, and accessed from, a central console computer.
Nodes may be removed from the network as, for example, when an administrator deactivates the node, the node's host processor is brought down, etc. When a node is brought down the optimization system traffic of the present invention is routed to different nodes. It is advantageous to transfer the correlation matrix of the node taken down to the one or more nodes to which traffic is being re-routed so that the information in the correlation matrix does not have to be recreated.
A preferred embodiment of the invention uses varying latency cycles to allow nodes to gather characteristics data to generate local values at varying frequencies. For example, a latency cycle can vary from 0 to 100. A larger number means that a characteristic is obtained less frequently. A value of 0 for a latency cycle means that a characteristic value is obtained as often as possible. Naturally, a lower latency cycle means that the host CPU is spending more time acquiring characteristic data and, possibly, generating values, also.
Although the present invention has been discussed with respect to specific embodiments, these embodiments are merely illustrative, and not restrictive, of the invention.
Thus, the scope of the invention is to be determined solely by the appended claims.

## Claims

1. A computer implemented method for assessing computer server and communications network capacity, the method comprising:
calculating two integer values representing a Local Node Value (LNV) and a Composite Node Value (CNV);
calculating the LNV of a server as an integer value through a combination of measured counters at the same instantaneous point in time, the LNV reflecting the capacity of the server to receive work loads; and,
calculating the CNV of a beginning server as an integer value through a combination of the LNVs of a sub-network of servers that begins with the beginning server, the calculation of the CNV including the LNV of the beginning server, the CNV reflecting the capacity of the sub-network beginning with the beginning server to collectively receive work-loads wherein said LNV and CNV allow a user to analyze network device performance and network resource utilization in a peer-to-peer, real-time relationship, without requiring a multi-tier polling data collection process via a central console, said peer-to-peer real-time relationship comprising a peer-to-peer value including said CNV and said LNV and capable of changing dynamically, in one to many, many to one, and bi-directional relationships between a plurality of calculated values wherein said CNV and LNV values are displayed in a client interface device and associated with a new network resource configuration wherein one or more network elements are communicatively coupled in a different manner from a previous network resource configuration.

2. The method in accordance with claim 1, further comprising:
obtaining a plurality of characteristics or counters at a specific point in time that are combined through correlation matrixes and weighted sums to produce the two integer values for the same point of time, the correlation matrixes and weighted sums being updated over a period of time to reflect historical changes over the period of time wherein an intelligent object (IO) is installed on each server in the network in a peer-to-peer architecture, said IO's measuring real-time behavior of network components.

3. A network performance aggregate resource analysis system comprising:
one or more processor readable storage devices having processor readable code embodied on said processor readable storage devices, said processor readable code for programming one or more processors to perform a method of providing a control function for a computer system, the method comprising: receiving a process code from a remote server; and operating the computer system according to the process code; to implement the method comprising the following steps:
providing one or more intelligence objects (IO) populating a network communicatively coupled to one or more nodes executing on one or more servers, computers, or other network components;
providing a composite value wherein said composite value is derived from said lO's;
providing one or more system level object's (SLO) that monitor and report on one or more network performance parameters in a host computer server;
providing one or more transaction level objects (TLO) that monitor transaction loads with respect to said host computer wherein said IO's are installed on each server in the network in a peer-to-peer network architecture, said IO's measuring real-time behavior of network components.

4. A network performance aggregate resource analysis system comprising: one or more processor readable storage devices having processor readable code embodied on said processor readable storage devices, said processor readable code for programming one or more processors to perform a method of providing a control function for a computer system; receiving a process code from the remote server; and operating the computer system according to the process code; to implement the method comprising the following steps:
providing one or more intelligence objects (IO) populating a network communicatively coupled to one or more nodes executing on one or more servers, computers, or other network components;
providing a composite value wherein said composite value is derived from said IO's;
providing one or more system level object's (SLO) that monitor and report on one or more network performance parameters in a host computer server;
providing one or more transaction level objects (TLO) that monitors transaction loads with respect to said host computer wherein said IO's are installed on each server in the network in a peer-to-peer network architecture, said IO's measuring real-time behavior of network components wherein said objects measure a plurality of network performance values;
representing said values in a binary value, said binary value represented in an initial local utilization value and subsequent local utilization values, said initial local utilization value relating only to said host computer server;
providing said localization values capable of being modified to a first composite utilization value;
providing said first composite utilization value capable of being passed directly to a second composite utilization value communicatively coupled to a data record tag;
providing a plurality of tags and segments to said first and second composite utilization values such that a plurality of local utilization values, composite utilization values and initial local utilization values differentiate host data in a bi-directional relationship.

5. A computer implemented method for assessing computer server and communications network capacity, said network having processor readable storage devices and processor readable code stored thereon for executing instructions on a processor, the method comprising:
calculating two integer values representing a Local Node Value (LNV) and a Composite Node Value (CNV), by;
calculating the LNV of a server as an integer value through a combination of measured counters at the same instantaneous point in time, the LNV reflecting the capacity of the server to receive work loads;
calculating the CNV of a beginning server as an integer value through a combination of the LNVs of a sub-network of servers that begins with the beginning server, the calculation of the CNV including the LNV of the beginning server, the CNV reflecting the capacity of the sub-network beginning with the beginning server to collectively receive work-loads wherein said LNV and CNV allow a user to analyze network device performance and network resource utilization in a peer-to-peer, real-time relationship, without requiring a multi-tier polling data collection process wherein said CNV and LNV values are displayed in a client interface device and associated with a new network resource configuration and wherein one or more network elements are communicatively coupled in a different manner from a previous network resource configuration.

6. A computer implemented method for assessing computer server and communications network capacity, said network having processor readable storage devices and processor readable code stored thereon for executing instructions on a processor, the method comprising:
calculating two integer values representing a Local Node Value (LNV) and a Composite Node Value (CNV), by;
calculating the LNV of a server as an integer value through a combination of measured counters at the same instantaneous point in time, the LNV reflecting the capacity of the server to receive work loads;
calculating the CNV of a beginning server as an integer value through a combination of the LNVs of a sub-network of servers that begins with the beginning server, the calculation of the CNV including the LNV of the beginning server, the CNV reflecting the capacity of the sub-network beginning with the beginning server to collectively receive work-loads wherein said LNV and CNV allow a user to analyze network device performance and network resource utilization in a peer-to-peer, real-time relationship, without requiring a multi-tier polling data collection process and capable of changing dynamically, in one to many, many to one, and bi-directional relationships between a plurality of calculated values wherein said CNV and LNV values are displayed in a client interface device and associated with a new network resource configuration and wherein one or more network elements are communicatively coupled in a different manner from a previous network resource configuration.

7. A computer implemented method for assessing computer server and communications network capacity, said network having processor readable storage devices and processor readable code stored thereon for executing instructions on a processor, the method comprising:
calculating two integer values representing a Local Node Value (LNV) and a Composite Node Value (CNV), by;
calculating the LNV of a server as an integer value through a combination of measured counters at the same instantaneous point in time, the LNV reflecting the capacity of the server to receive work loads;
calculating the CNV of a beginning server as an integer value through a combination of the LNVs of a sub-network of servers that begins with the beginning server, the calculation of the CNV including the LNV of the beginning server, the CNV reflecting the capacity of the sub-network beginning with the beginning server to collectively receive work-loads wherein said LNV and CNV allow a user to analyze network device performance and network resource utilization in a peer-to-peer, real-time relationship, without requiring a multi-tier polling data collection process via a central console, said peer-to-peer real-time relationship comprising a peer-to-peer value including said CNV and said LNV wherein said CNV and LNV values are displayed in a client interface device and associated with a new network resource configuration and wherein one or more network elements are communicatively coupled in a different manner from a previous network resource configuration.