RELATED APPLICATIONS 
     The present invention is related to the subject matter of the following commonly assigned, copending United States patent applications: Ser. No. 09/345,626 entitled “A SCALABLE, DISTRIBUTED, ASYNCHRONOUS DATA COLLECTION MECHANISM” and filed, Jun. 30, 1999; and Ser. No. 09/345,627 entitled “A DATA COLLECTOR FOR USE IN A SCALABLE, DISTRIBUTED, ASYNCHRONOUS DATA COLLECTION MECHANISM” and filed, Jun. 30, 1999. The content of the above-identified applications is incorporated herein by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Technical Field 
     The present invention generally relates to scheduling for distributed data collection processes and in particular to scheduling for a distributed data collection process involving a large number of data generation nodes. Still more particularly, the present invention relates to scheduling for distributed data collection through localized scheduling and bifurcation of the input and output scheduling processes. 
     2. Description of the Related Art 
     Distributed applications which operate across a plurality of systems frequently require collection of data from the member systems. A distributed inventory management application, for example, must periodically collect inventory data for compilation from constituent systems tracking local inventory in order to accurately serve inventory requests. 
     Large deployments of distributed applications may include very large numbers of systems (e.g., than 10,000) generating data. Even if the amount of data collected from each system is relatively small, this may result in large return data flows, consuming substantial bandwidth and time. Keeping all data generation nodes available for data collection throughout the distributed collection mechanism would be extremely wasteful of resources. However, scheduling collection from each data generation node presents a daunting problem with a large number of nodes. 
     The scheduling problem for distributed data collection among large numbers of nodes is further complicated when nodes are not always available, but only have intermittent or irregular periods of availability, which is likely to occur in data collection for certain information types such inventory or retail customer point-of-sale data. Nodes from which data must be collected may be mobile systems or systems which may be shut down by the user. As a result, certain nodes may not be accessible in a deterministic manner. 
     It would be desirable, therefore, to provide a scheduler for a distributed data collection process which is capable of handling large numbers of data generation nodes while accommodating nondeterministic node availability. 
     SUMMARY OF THE INVENTION 
     It is therefore one object of the present invention to provide improved scheduling for distributed data collection processes. 
     It is another object of the present invention to provide scheduling for a distributed data collection process involving a large number of data generation nodes. 
     It is yet another object of the present invention to provide scheduling for distributed data collection through localized scheduling and bifurcation of the input and output scheduling processes. 
     The foregoing objects are achieved as is now described. Scheduling in a distributed data collection process is performed locally, within collectors. Scheduling of data transfers from endpoints or downstream collectors or to upstream collectors is based on local queues, without global management. Additionally, scheduling for the collector input queue, which manages data collection from endpoints or downstream collectors, is bifurcated from scheduling for the output queue, which manages notifications to upstream collector(s) regarding the availability of collection data for pickup. Such bifurcation permits simpler scheduling logic and different functional responses to similar events, and further localizes scheduling. Scheduling of collection data transfer is controlled, within parameters specified by the output scheduler for the endpoint or downstream collector, by the input queue for the upstream collector. Scheduling is thus based primarily on the portion of the data transfer mechanism mostly likely to comprise a bottle-neck, the upstream collector, but accommodates large numbers of fully parallel data generation endpoints as well as nondeterministic endpoint availability. 
     The above as well as additional objectives, features, and advantages of the present invention will become apparent in the following detailed written description. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The novel features believed characteristic of the invention are set forth in the appended claims. The invention itself however, as well as a preferred mode of use, further objects and advantages thereof, will best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings, wherein: 
     FIGS. 1A-1B depict diagrams of a distributed data collection mechanism in accordance with a preferred embodiment of the present invention; 
     FIGS. 2A-2B are diagrams of portions of the distributed data collection mechanism relevant to different phases of the data collection process in accordance with a preferred embodiment of the present invention; 
     FIG. 3 is a diagram of components of a distributed data collection mechanism employed in a collection process in accordance with a preferred embodiment of the present invention; 
     FIGS. 4A-4B are diagrams of a collector and a collector scheduler in accordance with a preferred embodiment of the present invention; 
     FIG. 5A depicts a high level flow chart for a process of employing a collector scheduler within a distributed collection process in accordance with a preferred embodiment of the present invention; and 
     FIG. 5B is a diagram of a distributed data collection mechanism in accordance with the known art. 
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     With reference now to the figures, and in particular with reference to FIGS. 1A and 1B, diagrams of a distributed data collection mechanism in accordance with a preferred embodiment of the present invention are depicted. Distributed data collection mechanism  102  is implemented within a network of data processing systems including endpoints (“E”)  104  coupled via gateways (“G”)  106  to collectors  108   a - 108   n.    
     The network of data processing systems in which distributed data collection mechanism  102  is implemented may be either homogeneous or heterogeneous, and may form a local area network (LAN) or include two or more LANs connected to form a wide area network (WAN) or global area network. The network of data processing systems preferably includes an environment for running distributed applications, such as Tivoli Management Environment (TME) available from Tivoli Systems, Inc. of Austin, Tex. 
     Endpoints  104 , which may also be referred to as “sources,” are the systems from which data is to be collected. Gateways  106  are systems which facilitate communications between endpoints  104  and collectors  108   a - 108   n  and/or routing manager  110 . Recipients are objects or processes that receive collected data, and may be collectors  108   a - 108   n  or relational interface modules (“RIMs”)  112 . 
     Collectors  108   a - 108   n  are objects or processes which perform the task of data collection from a fixed set of endpoints  104 . The primary objective for collectors  108   a - 108   n  is to collect data from all corresponding endpoints  104  assigned to route data to the respective collector  108   a - 108   n , and store the received data in a persistent depot until another collector or the ultimate recipient is ready to receive the collected data. Data is collected based on certain characteristics such as priority, availability of the source (endpoint  104  or collector  108   a - 108   n ), and bandwidth usage. A collector  108   a - 108   n  may collect data from endpoints  104  or from another collector (e.g., collectors  108   a  and  108   n  in the example depicted). A collector  108   a - 108   n  may transfer data to another collector or to the recipient of the collection. 
     Routing manager  110  is a centralized module which manages collectors  108   a - 108   n  and the routes from endpoints  104  through collectors  108   a - 108   n  to the recipients of the collection, relational interface modules (“RIMs”)  112  and relational database management system (“RDBMS”)  114 . The primary function of routing manager  110  is to manage the deployment of collectors  108   a - 108   n  and maintain the routes from endpoints  104  through collectors  108   a - 108   n  to the recipients of the collection. Each routing manager  110  will service several collectors  108   a - 108   n , although for scalability an enterprise may include several routing managers  110 . FIG. 1B illustrates one circumstance under which multiple routing managers would be desirable: where the data collection includes networks spanning multiple continents. 
     Collectors  108   a - 108   n , routing manager  110 , RIMs  112  and RDBMS  114  may all run on a single system or may be distributed among a plurality of systems. Although the exemplary embodiment depicts a one-to-one correspondence between collectors  108   a - 108   n  and RIMs  112 , multiple collectors  108   a - 108   n  may be coupled to a single RIM  112 , and a single collector  108   a - 108   n  may be coupled to multiple RIMs  112  for separate databases. Endpoints  104  may be continuously coupled to the network hosting distributed data collection mechanism  102 , or they may be mobile systems only occasionally connected to the network. Endpoints  104  may be continuously powered on or may have periods during which they are turned off. 
     Referring to FIGS. 2A and 2B, diagrams of portions of the distributed data collection mechanism relevant to different phases of the data collection process in accordance with a preferred embodiment of the present invention are illustrated. In the present invention, the “scan” phase of the data collection process is decoupled from the gathering of return results. FIG. 2A illustrates the “scan” phase of the data collection process. In the present invention, each endpoint  104   a - 104   n  includes an autonomous scanner, allowing scans to proceed fully parallel. During the “scan” phase, a central “scan initiator” module  202 , which may be part of the routing manager, merely performs a profile push to the endpoints  104   a - 104   n  without waiting for return data. Some status data may be returned by endpoints  104   a - 104   n  to central module  202 , but the data to be collected is not returned to central module  202  in response to the profile push. 
     Instead, as illustrated in FIG. 2B, “return” or collection data (the data being collected) is asynchronously uploaded by individual endpoints  104   a - 104   n  to a collection network  204 , consisting of collectors associated with endpoints  104   a - 104   n , which routes the data to RIM  112 . Transfer of the collection data is thus initiated by the endpoints  104   a - 104   n  rather than a centralized module  202 . Stated differently, the actual data collection is initiated from the bottom up rather than from the top down. 
     The profile pushes from central module  202  are infrequent, needed only to set or change the scanner configuration at endpoints  104   a - 104   n . Scanners may be set to run automatically on a daily/weekly schedule or on some other schedule, or in response to an event (including a synchronous scan request from the user), or on boot-up. Scan data is subsequently queued for asynchronous collection. 
     In its simplest form, collection network  204  may simply be an upcall-collector at each gateway  106  within the distributed data collection mechanism  102 , with uploads from endpoints  104   a - 104   n  routed from the gateway  106  directly to the RIM  112 . However, this implementation may result in a potentially large load on gateway  106 . For a gateway  106  servicing 1,000 endpoints  104   a - 104   n , each generating 50K of collection data, the total load on gateway  106  will be 50MB. Data collection in this case will be competing with data distributions on gateway  106  in which data is flowing downstream. 
     Another drawback with this implementation is that gateways are not generally RIM hosts, which means that unless a custom RIM method is implemented, data flowing from gateway  106  to RIM  112  will be based on CORBA method parameters. Furthermore, wire-transfer overhead is potentially added to the RIM write, in addition to RIM latency. This implementation also provided less control over return data path and less coordination between RIM writers. 
     A more scalable solution is provided where collection network  204  is implemented with a configurable topology, using collectors nodes instead of simple upcall-collectors as basic elements. Collector nodes can be connected together to form a collection network topology, and can provide additional functionality such as depoting (caching), bandwidth control, and transfer scheduling. Deployment of collector nodes is controlled by the customer, so that the user may choose a simple topology if that is sufficient or, for larger deployments, add additional managed collector nodes to the topology to scale up the amount of data which can be buffered and improve response time visible to endpoints  104   a - 104   n  for queuing collection requests. Since collector nodes are not hard coded to gateways, application-specific topology customization becomes possible for, e.g., strategic placement of destination collectors on the RIM host for inventory so that wire-transfer overhead in the RIM object call is eliminated. 
     Each collector within collection network  204  has a list of endpoints  104   a - 104   n  and other collectors with which it may communicate, with the list being maintained by the routing manager. In uploads to a higher collector, each collector behaves substantially like endpoints  104   a - 104   n.    
     With reference now to FIG. 3, a diagram of components of a distributed data collection mechanism employed in a collection process in accordance with a preferred embodiment of the present invention is depicted. A “collection,” or an upload of return/collection data from a source towards a recipient, is initiated by endpoint  104   n . Whenever endpoint  104   n  wishes to initiate a collection, endpoint  104   n  contacts the routing manager  110  to which it is assigned to determine the nearest available collector  108   n . Routing manager  110  can determine the location of the nearest available collector  108   n , which may take into account the priority of the collection, the utilization of the network by other processes, the availability of endpoint  104   n.    
     Endpoint  104   n  then initiates a collection by transmitting to the collector  108   n  a Collection Table of Contents (CTOC), a data structure including information about the collection such as source, recipient, priority, time window, and collection identifier (once assigned). The first time a CTOC is submitted to a collector  108   n , the CTOC will receive a unique collection identifier utilized to track the progress of the collection in the network. 
     Upon receiving the CTOC from endpoint  104 n, the collector  108   n  will queue the CTOC for handling. When collector  108   n  is ready to receive the collection data (based on factors such as current time, current load, available depot space, and available threads), collector  108   n  initiates and upload by informing endpoint  104   n  that it (collector  108   n ) is ready. Upon receipt of this ready message, endpoint  104   n  begins transmitting the collection data to collector  108   n  in small packets, which collector  108   n  stores in persistent storage (depot  302 ). 
     Once the collection data received from endpoint  104   n  has all been stored by collector  108   n , collector  108   n  sends a message containing the CTOC to either another collector or the recipient. The collection data remains within collector  108   n  until requested by a higher level collector or the recipient. Once the upstream collector or recipient is ready to receive the collection data, collector  108   n  transfers the collection data in the same manner in which it collected the data from endpoint  104   n  or a downstream collector. 
     If a collection is broken off in the middle of receiving the collection data, collector  108   n  attempts to receive the remaining data after a short duration, employing a checkpoint restart from the point at which the collection was interrupted. The sender of the data does not delete any part of the collection data until the entire collection data has been successfully transferred upstream. 
     Upload of collection data to collectors may be managed to control network loading, and may cooperate with other distributed applications to conserve bandwidth utilization while optimizing the amount of bandwidth employed for collection. If there exists a critical section of the network where collection needs to be controlled, a collector may be placed at the end where data is being received. That collector may then be “turned off” or prohibited from transmitting a ready message to an endpoint or downstream collector during certain blackout periods to disable collection. The network may thus impose restrictions on when data may be transferred across certain critical network facilities in order to conserve bandwidth usage. 
     Referring to FIGS. 4A and 4B, diagrams of a collector and a collector scheduler in accordance with a preferred embodiment of the present invention is illustrated. The collector, shown in FIG. 4A, is a fundamental element of the distributed data collection service of the present invention, and is responsible for storing and forwarding collected data towards the eventual destination. The collector is a mid-level management object having one instance per host, and providing priority-based queuing of collection requests, depoting of collection data, a crash recovery mechanism for collection data transfers, and multi-threaded transfer scheduling of collection requests in the queues. 
     Collectors may be connected into a collection network and deployed into a distributed hierarchical network topology to implement a distributed data collection service, a distribution which may be performed either manually or through some network-loading-based algorithm. Collectors in a collection network have topological relationships utilized to control the routing of data from an “injection” point (endpoint interface to collector) in the collection network to the final destination. Route calculations from endpoints to the recipients via particular collectors may be performed either at each collector, or on a global basis, to marshal collection data towards the eventual destination. These route calculations may be based on a static routing map or one that is dynamically evaluated based on network loading. 
     Each collector  108   n  includes a priority-based queuing mechanism which initially includes two queues: an input queue  402  to store requests for collection from downstream nodes (endpoints or lower level collectors) and an output queue  404  to hole collection requests which were spooled to upstream collectors for pickup. Queues  402  and  404  are maintained in sorted order with the primary sort key being the CTOC priority, which ranges, for example, from priority level  0  up to priority level  4 . 
     The priority level may be utilized, for instance, in differentiating between collection from mobile endpoints or other endpoints which are intermittently unavailable (e.g., shut down) and collection from continuously available endpoints. A highest level priority may be assigned to CTOCs originating from mobile endpoints to ensure timely collection of the data while the endpoint is available. Lower level priorities may be assigned to collection data from endpoints which are continuously available. 
     For mobile and intermittently connected endpoints, the activation time and activation duration information may be utilized to inform collectors of the mobile status of the endpoints, and the expected times that a connection would be available for data pickup. These mobile endpoints would also set a high priority on the collection request, since they have a limited availability window on the network. The collector handling the request would then use the priority to schedule this data pickup ahead of other normal endpoints occurring at the same time. 
     The secondary sort key within a given priority level is the CTOC&#39;s activation time—that is, the time at which the node is available for servicing data transfer requests. The tertiary sort key within a given priority level and a given pickup time is the activation duration for which the node is available to service data transfer requests. Alternatively, additional sorting beyond the primary sort may be applied by providing function hooks. 
     Queues  402  and  404  essentially store CTOC elements, and can be checkpointed and restarted from a disk file to allow crash recovery of the collection service and the user data for which transfer was in progress. Checkpoint-restart of queues  402  and  404  utilizes Interface Definition Language (IDL) serialization to write and read ASN.1/BER streams to and from the disk. Queues  402  and  404  are preferably thread-safe implementations, as is a retry queue (not shown) if needed. 
     The data representations employed by collector  108   n  are implementing in accordance with the key-value pairs approach to store and retrieve data by name, and with backwards binary compatibility from an IDL standpoint. The primary and essential data representations employed are the CTOC  406  and the data pack  408 . These data representations possess persistence properties as well as the ability to cross system and process boundaries, and may be implemented in IDL or EIDL. 
     CTOC  406  forms the “header” portion of collection data, which is utilized by both data-generation endpoints and intermediate collector nodes in the collection network to inform upstream nodes of the availability of the data for pickup. CTOC  406  is employed by endpoints and collectors to negotiate data transfer requests. 
     CTOC  406  contains a unique identifier  410  for system-wide tracking of the progress of the collection data through the collection network, assigned when the CTOC is first submitted by the data-generation endpoint. CTOC  406  also contains a priority  412  associated with the collection data, which may be from level  0  up to level  4  in the exemplary embodiment. 
     CTOC  406  also includes source and destination object identifiers  414  and  416  for routing the collection data, and a DataPack component  418  which describes the actual data by, e.g., size and location of files, data compression information, if any, and data encryption information, if any. CTOC  406  also contains an activation time window  420 , a “window of opportunity” when the node is available for servicing data transfer request. 
     Activation time window  420  is encoded in two fields: an activation time field  420   a  which specifies the earliest time at which the node is available to service data transfer requests; and an activation duration field  420   b  which places a limit on how long the node will be available to service data transfer requests, starting from the activation time. The activation time window  420  may be employed both for bandwidth utilization control on the collection network and for handling endpoints, such as mobile systems, which are only intermittently connected to the collection network. 
     The other essential data representation employed by collector  108   n  is data pack  408 , which is the “data” part of the collection dataset and which, together with constituent data segments  422 , contains an encoded form of the actual collection data for pickup and delivery. Data pack  408  represents the atomic unit for collection data. A data pack  408  may contain multiple data segments  422  each corresponding to a file and containing, for example, the file path, the file name, and the file attributes such as compression flags, checksums, and encryption information. Data packs  408  and data segments  422  are designed to model arbitrary collections of data, file-based or otherwise. 
     Collector  108   n  also includes or employs other components. The persistent storage or “depot”  302  accessible to collector  108   n  provides intermediate staging of the collection data while in transit, which offers two benefits: first, the data transmission cycle may be spread out more evenly, allowing better bandwidth utilization management of the collection network as well as reducing the instantaneous loading on the end application (the recipient) and thereby increasing scalability; and, second, reliability is increased since the data can now take advantage of crash recovery mechanisms available in the collection service. 
     Collection data in the form of data packs  408  and data segments  422  are stored on disk within depot  302 . Depot  302  maintains an indexed archive of data packs  408  on disk, indexed utilizing the CTOC identifier  410  for the collection data. Depot  302  also implements thread-safety and crash-recovery mechanisms. 
     Collector  108   n  also includes a scheduler  424 , an active agent which manages the queues  402  and  404  and depot  302 . Scheduler  424  services CTOCs  406  from input queue  402 , stores and retrieves collection data from depot  302 , and propagates collection data upstream to the next collector node. 
     Scheduler  424  is a multi-threaded implementation which employs socket-based Inter-Object Message (IOM) channels for actual transmission of collection data, with network-bandwidth control mechanisms to regulate flow. Scheduler  424  also employs IOM to transfer data from downstream nodes to the local depot  302 . Locally resident CTOCs  406  are then routed by scheduler  424  to the next upstream collector and placed in output queue  404  until the collection data is picked up. 
     Collector  108   n  also includes a router  426  which consults the topology management daemon (routing manager) and performs calculations based on the source and recipient identifiers  414  and  416  necessary to determine the next upstream collector node within the collection network for the CTOC. The collection or routing manager  110  maintains a graph-based representation of the collection network, in which collectors are modelled as nodes in the graph and permitted connections are represented by weighted edges. This representation is employed to calculate the optimum path for data and in each hop calculation. Multiple collection managers may exist in the enterprise, with one collection manager per region for multiple regions. Data crossing regions boundaries will do so along WAN-entry points which may be specified to the collection managers. Router  426  may cache frequently utilized routes to minimize network traffic. 
     Router  426  also optimizes transmission of the collection data to the next upstream node for bandwidth utilization by controlling the activation time and duration fields  420   a - 420   b  in the CTOC  406 . An alternative, which may be implemented either in lieu of or in addition to the activation time window, is to provide more coarse grain control by specifying, for each collector, a list of endpoints from which that collector may NOT collect data, with each entry specifying either an individual node or a group of nodes. When a CTOC arrives from a node specified within the list, the collector defers collection indefinitely. Any external scheduling mechanism may then be utilized with this mechanism to control, in coarse grain fashion, which of the nodes is to be blocked or blacked out. At the extremes, none or all of the nodes may be blocked. 
     Finally, collector  108   n  includes a customization and control module  428  to support status monitoring and the ability to stop any particular collector in the collection network and drain its queues. Also provided are mechanisms which help construct a topology view of the collection network in a graphical fashion, with state and control information for individual collectors. This feature is utilized by administrators to monitor and fine-tune the collection network. Additional hooks may be provided within collector  108   n  to permit other distributed applications to utilize collection services. 
     The logical piece identified as scheduler  424 , shown in greater detail with other relevant portions of collector  108   n  in FIG. 4B, is actually two distinct pieces which share functionality but have unique qualities: input scheduler  430  and output scheduler  432 . Input scheduler  430  handles the requests for data transfer by endpoints or downstream collectors through successful storage of the collection data within the collector&#39;s depot. Output scheduler  432  handles transfer of the collection data from the collector&#39;s depot to the upstream collector. 
     On the input side, an input request handler  434  associated with collector  108   n  provides an IDL method which will accept upcalls from endpoints or method calls from a downstream collector. The CTOC received from the endpoint or downstream collector by input request handler  434  is placed in input queue  402 . If input scheduler  430  has not yet been initiated, the placement of a CTOC within input queue  402  will start up input scheduler  430 . 
     Input scheduler  430  controls the flow of CTOCs from input queue  402  to output queue  404 . Input scheduler  430  is a thread which gets initiated after input request handler  434  places the first CTOC within input queue  402 . In operation, input scheduler  434  finds the next waiting CTOC to be scheduled and, if a worker thread is available, will set the CTOC to the working state within input queue  402  and spawn a scheduler worker thread  436  to call the application programming interfaces (APIs) of input handler  438  with that CTOC. Input handler  438  will then transfer the data pack(s) and data segement(s) associated with the CTOC into the collector&#39;s depot  302 . Input handler  438  is a routine for getting the IOM key and sending the key to the lower collector object to open the IOM channel. Input handler  438  is implemented with a set of APIs which input scheduler  430  and depot  302  can call to initiate data transfer. 
     When the data transfer to depot  302  is finished, the worker thread  436  will return a success or error condition to the calling method of input scheduler  430 . On error, the CTOC is placed back into the input queue  402  with an increased retry count. On success, the input scheduler puts the CTOC in output queue  404  and removes that CTOC from input queue  402 . 
     Output scheduler  432  controls the flow of the CTOC from output queue  404  to the next (upstream) collector. Output scheduler  432  is a thread which is initiated after input scheduler  430  places the first CTOC entry in output queue  404 . In operation, output scheduler  432  finds the next waiting CTOC within output queue  404  and uses input method caller  440  to call the upstream collector or method to inform it that a CTOC is ready to be sent, setting the state of the CTOC within output queue  404  to working. Output scheduler  432  then goes back into the loop waiting for another ready CTOC to be placed in output queue  404 . Unlike input scheduler  430 , however, output scheduler  432  will never remove a CTOC from output queue  404  and generate an error based on the retry count, or else the collector  108   n  will permanently lose data. 
     Input method caller  440  determines whether the next upstream collector is identified in local memory and, if not, contacts the router to get the next upstream collector object identifier (OID). Input method caller  440  then contacts the upstream collector to inform it that collector  108   n  is ready to send data upstream. 
     Output scheduler  432  includes a get_data method called by the upstream collector when the upstream collector is ready for the collection data. Output scheduler  432  must check for available thread and spawn a new worker thread for itself, if there are some available, and return success or failure. The worker thread utilizes APIs in output handler  442  to establish the IOM channel and transfer data from depot  302  in collector  108   n  to the upstream collector. After completion of the collection data transfer, output scheduler  432  calls output queue  404  and removes the CTOC if the transfer was successful. If the collection data transfer failed, the retry count within the CTOC gets increase and the CTOC is placed back into the waiting state in output queue  404 . 
     With reference now to FIGS. 5A and 5B, high level flow charts for processes of employing a collector scheduler within a distributed collection process in accordance with a preferred embodiment of the present invention are depicted. FIG. 5A depicts a process employed by the input scheduler. For simplicity, a single threaded process is illustrated, although the input scheduler preferably employs a multi-threaded process. 
     The process of FIG. 5A begins with step  502 , which depicts a CTOC being placed in the input queue after being received from an endpoint or downstream collector. For multi-threaded implementations, the CTOC is placed in the input queue in the “waiting” state, indicating that the CTOC is awaiting service, as opposed to the “working” state, which indicates that the corresponding collection data is being transferred. The process then passes to step  504 , which illustrates a determination of whether the input scheduler has previously been started. If not, the process proceeds to step  506 , which depicts starting the input scheduler. 
     If the input scheduler had been previously started, or once the input scheduler is started, the process proceeds from step  504  or  506  to step  508 , which illustrates sorting the (waiting) CTOCs within the input queue. Although the sorting may actually be performed by the input queue rather than the input scheduler, the sorting comprises part of the scheduling process since the sort order determines which CTOC is “next.” CTOCs within the input queue are preferably sorted first by priority, and second by activation time window. 
     The process next passes to step  510 , which depicts the input scheduler getting the next CTOC and initiating transfer of the corresponding collection data. For multi-threaded processes, data collection transfers may be performed in parallel up to a number of available worker threads, as long as CTOCs remaining waiting in the input queue. The transferred collection data is stored within the depot for the collector. 
     The process then passes to step  512 , which illustrates a determination of whether the collection data transfer was successfully completed. If so, the process proceeds to step  514 , which depicts removing the corresponding CTOC from the input queue and placing it within the output queue. If the collection data transfer was not successfully completed, however, the process proceeds instead to step  516 , which illustrates incrementing a retry count within the CTOC and placing the CTOC back into the input queue (in the waiting state, for multi-threaded implementations). Transfer of the associated collection data will then be reattempted. 
     From either of steps  514  or  516 , the process passes to step  518 , which illustrates a determination of whether there are any other (waiting) CTOCs remaining within the input queue. If so (i.e., the input queue is not empty), the process returns to step  508  and the data collection process is repeated with the new CTOC. If not, however, the process proceeds instead to step  520 , which illustrates the process becoming idle until another CTOC is place in the input queue. 
     FIG. 5B depicts the process employed by the output scheduler, which is also depicted as a single-threaded process for simplicity. The process of FIG. 5B begins with step  522 , which depicts a CTOC being placed in the output queue by the input scheduler. Again, for multi-threaded implementations, the CTOC is placed in the input queue in the “waiting” state. The process then passes to step  524 , which illustrates a determination of whether the output scheduler has previously been started. If not, the process proceeds to step  526 , which depicts starting the output scheduler. 
     If the output scheduler had been previously started, or once the output scheduler is started, the process proceeds from step  524  or  526  to step  528 , which illustrates sorting the (waiting) CTOCs within the output queue. This sorting also comprises part of the scheduling process although it may actually be performed by the output queue rather than the output scheduler, since the sort order determines which CTOC is “next.” CTOCs within the output queue are also preferably sorted primarily based upon priority and secondarily based upon activation time window. 
     The process next passes to step  530 , which depicts the output scheduler getting the next CTOC and transmitting it to the upstream collector. For multi-threaded processes, several CTOCs may be transmitted to upstream collectors in parallel, up to a number of available worker threads. The process then passes to step  532 , which illustrates a determination of whether there are any other (waiting) CTOCs remaining within the output queue. If so (i.e., the output queue is not empty), the process returns to step  528  and the the next CTOC is transmitted upstream. If not, however, the process proceeds instead to step  534 , which illustrates the process becoming idle until another CTOC is place in the output queue. 
     In the present invention, scheduling of data collection for a collector within a distributed data collection process is performed based on local conditions, without direct global control or management. Global management of the distributed data collection process is only achieved indirectly by profile pushes to autonomous scanners within the endpoints. By not attempting global scheduling, the scheduling task is simplified. 
     Scheduling is also bifuracted for the input queue and output queue of a collector. This allows simpler logic to be implemented for the scheduler and permits the scheduling processes to run independent for both queues. For example, the input scheduler may be stopped in response to, say, a disk full error condition, while the output scheduler is permitted to continue uninterrupted. Scheduling is even further localized since the input and output schedulers schedule data transfers or CTOCs based only the contents of the input or output queues, respectively, and the downstream or upstream loading conditions, respectively. Different functionality for the input and output scheduler may also be implemented, as in the case of the retry count exceeding a maximum limit. 
     The present invention provides an asynchronous collection mechanism in which data collection is controlled by the individual collector. Data is transferred utilizing a direct channel and is stored in depots established to hold collected data. The data collection mechanism allows checkpoint restarts, blackout windows, and bandwidth utilization control. 
     The present invention may be utilized in a distributed collection network in which the topology is configurable and may be dynamically modified at run time based on loading conditions by specifying routing for particular packets of collection data according to regional traffic on all alternative paths between the source and recipient. Data collection autonomously originates with data generation endpoints and is asynchronously timed, within an activation window specified by the endpoints, based on local conditions within an upstream collector for a next immediate hop. Scheduling of collection data hops is performed locally without global timing constraints. 
     The present invention is well-suited for data collection from mobile endpoints. The user of the mobile endpoint may trigger an autonomous scan for collection data within the mobile endpoitn by logging on to a network. The mobile system formulates a CTOC for the collection data, specifying an activation window with, perhaps, some input from the user regarding how long the user intends to remain logged onto the network. The priority for the CTOC may also be set based on whether the endpoint is mobile and/or intermittently unavailable or always on and always connected. The routing for the next data hop is optimized by a central collection routing manager for local traffic and available alternative paths to the intended recipient, but scheduling is based on local conditions without global timing controls. 
     It is important to note that while the present invention has been described in the context of a fully functional data processing system and/or network, those skilled in the art will appreciate that the mechanism of the present invention is capable of being distributed in the form of a computer usable medium of instructions in a variety of forms, and that the present invention applies equally regardless of the particular type of signal bearing medium used to actually carry out the distribution. Examples of computer usable mediums include: nonvolatile, hard-coded type mediums such as read only memories (ROMs) or erasable, electrically programmable read only memories (EEPROMs), recordable type mediums such as floppy disks, hard disk drives and CD-ROMs, and transmission type mediums such as digital and analog communication links. 
     While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.
Scheduling in a distributed data collection process is performed locally, within collectors. Scheduling of data transfers from endpoints or downstream collectors or to upstream collectors is based on local queues, without global management. Additionally, scheduling for the collector input queue, which manages data collection from endpoints or downstream collectors, is bifurcated from scheduling for the output queue, which manages notifications to upstream collector(s) regarding the availability of collection data for pickup. Such bifurcation permits simpler scheduling logic and different functional responses to similar events, and further localizes scheduling. Scheduling of collection data transfer is controlled, within parameters specified by the output scheduler for the endpoint or downstream collector, by the input queue for the upstream collector. Scheduling is thus based primarily on the portion of the data transfer mechanism mostly likely to comprise a bottle-neck, the upstream collector, but accommodates large numbers of fully parallel data generation endpoints as well as nondeterministic endpoint availability.