Abstract:
An administrator of an enterprise storage set may be tasked with storing a large number and variety of data sets on a large number and variety of storage components. However, the manual selection of a physical schema by an administrator may be time-consuming, may generate inefficient physical schemata, and may not be easily reevaluated as the data sets and storage set change. Presented herein are techniques for automatically determining a physical schema by comparing the storage factors of each data set (e.g., data size, relationships with other data sets, and usages of the data set by users) with the storage capabilities of the storage components, selecting a suitable storage component, and implementing the storage of the data set on the storage component. An embodiment of these techniques may thereby achieve an automated identification of a physical schema with improved efficiency and flexibility of the physical schema while conserving administrative resources.

Description:
BACKGROUND 
     Within the field of computing, many scenarios involve two or more data sets that are to be stored among two or more storage components. For example, administrators of an enterprise information technology environment for a large company may be tasked with configuring a storage set, comprising a set of storage components to store a large number of data sets on behalf of a large number of users. This task may be complicated, e.g., by the large variety of storage components comprising the storage set (e.g., database servers, network file systems, archival systems and data warehouses, and cloud storage services), the properties and features of the different storage components (e.g., total and available storage capacity, bandwidth and throughput, querying capabilities, and security), and the variety and properties of the data sets (e.g., data set size, relationships with other data sets, and uses of the data sets by the users of the enterprise organization). In many contemporary scenarios, the administrator may first examine the data sets and determine a conceptual and/or logical schema, and may consider the relationships and usages of the data sets, and may then have to select a physical schema for the storage of the data sets, e.g., by choosing a storage component having suitable features matching the properties of the data set, provisioning storage on the storage component, and initiating the storage of the data set by the storage component. The administrator may then have to implement the storage selection, e.g., by provisioning storage on the selected storage components, configuring devices and software processes to utilize the provisioned storage component, storing the data onto the storage component, and instructing other users regarding the storage component. Additionally, the administrator may have to reevaluate of the selections of storage components for data sets as the number and details of the data sets and/or the storage set change. 
     SUMMARY 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key factors or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. 
     While an administrator may adequately select storage components to match various data sets, a manual selection of storage components may have some disadvantages. As a first example, the selection of a physical schema may consume the attention and resources of the administrator, and may involve a significant amount of administrative attention of the administrator to design, implement, and maintain. As a second example, a manual design of a physical schema may be inefficient in several respects (e.g., failing to utilize the storage capacities and features of the storage set with high efficiency), particularly as the number, variety, and complexity of storage components and/or data sets increases. As a third example, an administrator may be reluctant to reevaluate a manually designed physical schema, e.g., in view of changes in the data sets and/or the storage set, including an addition or removal of storage components. 
     Presented herein are techniques for automatically selecting storage components of a storage set in order to store a potentially large number and variety of data sets. In accordance with these techniques, for each storage component, a set of storage capabilities may be identified (e.g., available storage capacity, bandwidth, a capability of executing queries, remote accessibility, and a security level), and for each data set, a set of storage factors may be identified (e.g., the estimated size of the data set, the structure and content of the data set, whether or not users are likely to execute simple or complex queries against the data set, whether or not users are likely to access the data set remotely, and a sensitivity level of the data comprising the data set). When presented with a particular data set, an embodiment of these techniques may compare the storage capabilities of the storage components with the storage factors of the data set, automatically select a suitable storage component, provision space on the storage component to store the data set, and initiate the storage of the data set in that storage component. In this manner, a physical schema for storing the data sets within the storage set may be automatically generated, thereby conserving the attention of administrators, achieving a highly efficient and suitable physical schema, and enabling a reevaluation and reconfiguration of the physical schema as the data sets and/or storage set change. 
     To the accomplishment of the foregoing and related ends, the following description and annexed drawings set forth certain illustrative aspects and implementations. These are indicative of but a few of the various ways in which one or more aspects may be employed. Other aspects, advantages, and novel features of the disclosure will become apparent from the following detailed description when considered in conjunction with the annexed drawings. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a flow chart illustrating an exemplary scenario featuring a physical schema selected by a user to store data sets on storage components of a storage set. 
         FIG. 2  is a flow chart illustrating an exemplary scenario featuring an automated generation of a physical schema to store data sets on storage components of a storage set. 
         FIG. 3  is a flow chart illustrating an exemplary method of storing a data set in a storage set. 
         FIG. 4  is a flow chart illustrating an exemplary method of storing a data set in a storage set. 
         FIG. 5  is an illustration of an exemplary computer-readable medium comprising processor-executable instructions configured to embody one or more of the provisions set forth herein. 
         FIG. 6  illustrates an exemplary computing environment wherein one or more of the provisions set forth herein may be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     The claimed subject matter is now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the claimed subject matter. It may be evident, however, that the claimed subject matter may be practiced without these specific details. In other instances, structures and devices are shown in block diagram form in order to facilitate describing the claimed subject matter. 
     Within the field of computing, many scenarios involve the storage of data within a storage set comprising one or more storage components, such as a set of one or more hard disk drives, solid-state storage devices, tape backup drives, memory caches, network-attached storage devices, virtual storage devices, cloud storage services, or a combination thereof. On such storage components, many types of data may be stored in many types of storage systems, such as various types of file systems and database systems. Particularly in enterprise scenarios, these storage sets may scale up to include very large amounts of storage (possibly reaching many petabytes or exabytes) and very large numbers of storage components. Each storage component may have various storage capabilities, including physical properties, such as capacity, throughput, and reliability (e.g., mean time between failures (MTBF) and the ease of recovering from data failure), and usage properties, such as security (e.g., who administrates and may physically access the storage component) and backup (e.g., how often data archiving of the storage component is performed). Additionally, such storage capabilities may be Boolean or gradient in nature (e.g., whether or not a particular storage component provides support for queries such as Structured Query Language (SQL), vs. the per-megabyte cost of acquiring and expanding each storage component). 
     These storage devices may be used to store many types of data, including files comprising an operating environment of a computer, personal documents, media libraries, records in one or more database tables, and email mailboxes comprising sets of email messages. Each data set may have particular storage factors, such as a data size, access patterns (e.g., sequential, streaming, or random access, large or small numbers of individual units such as files, concurrent access by several users, and support for queries, and minimum acceptable access rates, such as minimum rates for streaming media objects without interruption), security (e.g., security considerations for where the data may be stored, who may access the data, the types of credentials to be presented before granting access, and the sensitivity of the data), and accessibility (e.g., whether the data is to be restricted to local access over a local area network or ubiquitously available). Again, such storage factors may be Boolean or gradient in nature (e.g., whether a particular data set involves queries, and an estimate of the advantage of providing higher throughout for a particular data set). 
     Within such complex scenarios, the task of administrating the storage set becomes nontrivial, and administrators may have to spend considerable effort and resources in acquiring, configuring, testing, and maintaining such storage components, as well as identifying, provisioning, and backing up sets of data stored on each component. In particular, the task of selecting a storage component for a particular data set may be complex. This task often involves identifying the various storage factors for a data set and matching the storage factors with a storage component featuring storage characteristics that match the storage factors of the data set. Once a data component has been selected, the administrator may have to provision a sufficient amount of storage on the storage component (e.g., creating a storage volume on the storage component), record the selection in a storage catalog, and configure devices and instruct individuals in accessing the provisioned storage. 
       FIG. 1  presents an exemplary scenario  10  featuring a user  20  tasked with allocating storage for a data set group  12 , comprising a number of data sets  14 , to a storage set  16  comprising a number of storage components  18 . For a scenario such as a research hospital, the data sets  14  may include, e.g., one or more databases; one or more email mailboxes; a set of medical records; and a set of research data. Moreover, each data set  14  may have various storage factors that may influence the selection of a storage component  18 . For example, the databases may have to be stored on a storage component that supports transactional capabilities; a set of email mailboxes may have to be stored on a storage component that supports vastly concurrent access by a large number of users; the medical records may have to be stored on a highly secured server in order to satisfy various legal and regulatory provisions; and the research data may have to be stored on a storage component supporting very large amounts of data (e.g., terabyte-size data sets). Additionally, each storage component  18  in the storage set  16  may have various storage capabilities, including features and limitations, to be considered when provisioning storage thereupon for any particular data set  14 . For example, the database servers may provide support for transactional accesses, but may involve comparatively complicated administration, and may not be remotely accessible. The network file system may offer very large storage capabilities to many users, but may not support transactional capabilities. The cloud storage service may provide ubiquitous access to data, but may offer comparatively limited throughout over the internet. A data warehouse may offer very copious storage, but may not be highly secured. 
     Faced with such storage factors of the data sets  14  and such storage capabilities of such storage components  18 , the user  20  may be presented with the task of selecting a suitable storage component  18  for each data set  14 . The user  20  may therefore evaluate various properties of the storage selection, such as the entity relationships  22  (e.g., relationships of data in a first data set  14  with data in a second data set  14 ), usage mappings  24  (e.g., considerations of the usage patterns of each data set  14 , such as the locations of users and processes that will access the data set  14  and the access patterns of such usage), and logical schema  26  (e.g., the components of each data set  14 , such as the sizes, types, and interrelationships of files within a file system or tables in a relational database). 
     As a result of these considerations, the user  20  may generate a physical schema  28 , comprising a mapping of the data sets  14  to the storage components  18 , based on the matching of storage factors of the data sets  14  with the storage capabilities of the storage components  18 . The user  20  may then have to implement the selected physical schema  28  by provisioning storage on the storage component  18  (e.g., creating a logical volume and setting up access parameters), recording the provisioning in a storage catalog, configuring devices and software processes to use the provisioned storage, and notifying and instructing other users  20  in the details of the allocated storage. Additionally, the user  20  may have to perform various maintenance tasks, such as archiving or backing up the storage components  18 , periodically testing the integrity of the storage components  18 , upgrading storage components  18  to provide more capacity, and replacing malfunctioning hardware. 
     It may be appreciated that the exemplary scenario  10  of  FIG. 1  involves a significant amount of effort on the part of the user  20 , particularly as the number and variety of data sets  14  and the number and variety of storage components  18  increase. The process of matching data sets  14  to suitable storage components  18  and implementing such selections may involve large amounts of skill and resources, and inefficient decisions may result in wasted resources, data loss, and inadequate performance (e.g., slow network transfer rates and inadequate storage capacity). Moreover, once a physical schema  28  has been selected, the reevaluation of the physical schema  28  may present a daunting challenge. For example, reevaluating reallocating storage in view of changes to the data set group  12  and the storage set  18  (e.g., the addition of new data sets  14  and/or new storage components  18 ) may comprise a difficult and resource-intensive process, particularly for a large number of data sets  14  and storage components  18 . Even if this process is desirable, the resources involved in evaluating the physical schema  28 , reconfiguring storage components  18 , moving data among storage components  18 , reconfiguring devices and software processes, and reinstructing other users  20  may simply not be cost-effective. As a result, inefficiencies may arise in the physical schema  28  that reduce the performance, capabilities, and usage of the storage set  16 . 
     Presented herein are techniques for facilitating, reducing, or eliminating the selection of a physical schema  28  by a user  20 . In accordance with the techniques presented herein, an automated matching process may be devised to, for each data set  14  having particular storage factors, automatically select a storage component  18  presenting suitable storage characteristics. In general, the problem of selecting the physical schema  28  may be viewed as a best-fit problem, wherein elements having different sizes, shapes, and properties are to be arranged in one or more storage containers having various properties. Accordingly, the process of matching data sets  14  to storage components  18  may be automatically resolved using various best-fit techniques. The matchings may be recorded in a storage catalog, which may comprise a storage catalog describing the storage set  16 , the data sets  14  stored on each storage set  16 , and the rationale for choosing such selections. Therefore, and according to the techniques presented herein, an automated matching of data sets  14  with storage components  18  may be performed to achieve a more suitable physical schema  28  in a more efficient manner than may be selected by a user  20 , resulting in improved throughput, greater available capacity, and/or reduced costs. Moreover, the selection may be automatically implemented by provisioning storage on each storage component  18  and configuring devices and software processes to use the provisioned storage (e.g., automatically configuring network mappings on computers to point to correct volumes). As still further advantages, an automated process may be able to identify the storage capabilities of the storage components  18  (e.g., identifying the throughput of each storage component  18  through bandwidth testing), and to reevaluate the physical schema  28  in view of changes in the data set group  12  and the storage set  16  (e.g., identifying changes to the physical schema  28  that may present various improvements in the performance of the storage set  16 , and even implementing such changes). Indeed, once a user  20  creates a representation of the data factors of the data sets  14 , the automated process may be able to handle the entire process of choosing, implementing, and continuously reevaluating the physical schema  28 , thereby achieving significant improvements in the performance of the storage set  16  and at much lower administrative costs. 
       FIG. 2  presents an exemplary scenario  30  featuring an automated mapping of a data set group  12  comprising various data sets  14  to a storage set  16  comprising various storage components  18 . In this exemplary scenario  30 , the data sets  14  may be identified as having various storage factors  32 . Such storage factors  32  may be based on various entity relationships  22  (e.g., a second database that relationally depends upon a first database); various usage mappings  24  (e.g., a particular database may be often used by an application executing on a particular server, and a set of email mailboxes may have to be remotely accessible via the internet); and various logical schema  26  (e.g., research data may comprise a small set of very large files, while an email mailbox may comprises a very large set of small files). Many other considerations may also be included in the storage factors  32  for various data sets  14 . For example, a set of medical records may be highly sensitive (e.g., subject to various privacy regulations, such as Heath Information Portability and Accountability Act (HIPAA), and may therefore have to be stored on a highly secured server with restricted physical access). Additionally, the storage components  18  may feature various storage capabilities  34 , such as total and available capacity, an access rate (e.g., local-area and wide-area throughput measured in both upload and download capacity), remote access, query capabilities, and security considerations. In view of these storage factors  32  and storage capabilities  34 , an automated matching may be performed to generate a physical schema  28  identifying, for each data set  14 , one or more storage components  18  wherein the data set  14  is to be stored. The matchings may be recorded in a storage catalog  36 , which may comprise a storage catalog describing the storage set  16 , the data sets  14  stored on each storage set  16 , and the rationale for choosing such selections. Additionally, an automated process may, after selecting the physical schema  28 , automatically implement the physical schema  28  on the storage components  18  and/or data sets  14 , e.g., by configuring devices, acquiring services, and storing or relocating data sets  14  there among. 
       FIG. 3  presents a first embodiment of these techniques, illustrated as an exemplary method  40  of storing a data set  14  having at least one storage factor  32  in a storage set  16  comprising at least two storage components  18  respectively having at least one storage capability  34 . The exemplary method  40  may be implemented, e.g., as a set of software instructions stored in a memory component (e.g., a system memory circuit, a platter of a hard disk drive, a solid state storage device, or a magnetic or optical disc) of a device having a processor, that, when executed by the processor of the device, cause the processor to perform the techniques presented herein. The exemplary method  40  begins at  42  and involves executing  44  the instructions on the processor. More specifically, the instructions are configured to identify  46  at least one storage factor  32  of the data set  14 . The instructions are also configured to, among the storage components  18 , select  48  a selected storage component  18  having storage capabilities  34  matching the storage factors  32  of the data set  14 . The instructions are also configured to associate  50  the data set  14  with the selected storage component  18  in the storage catalog  36 , and store  52  the data set  14  in the selected storage component  18 . In this manner, the exemplary method  40  performs and implemented an automated selection of storage of various data sets  14  on the storage components  18  of the storage set  16 , and so ends at  54 . 
       FIG. 4  presents a second embodiment of these techniques, illustrated as an exemplary method  60  of fulfilling a request to access a data set  14  stored in a storage set  16 . The exemplary method  60  may be implemented, e.g., as a set of software instructions stored in a memory component (e.g., a system memory circuit, a platter of a hard disk drive, a solid state storage device, or a magnetic or optical disc) of a device having a processor, that, when executed by the processor of the device, cause the processor to perform the techniques presented herein. The exemplary method  60  begins at  62  and involves executing  64  the instructions on the processor. More specifically, the instructions are configured to, using the storage catalog  36 , identify  66  a selected storage component  18  that is storing the data set  14 . The instructions are also configured to fulfill  68  the request by accessing the data set  14  in the selected storage component  18  according to the request. Having achieved the retrieval and provision of the data set  14  from the storage set  16  in response to the request, the exemplary method  60  ends at  70 . 
     Still another embodiment involves a computer-readable medium comprising processor-executable instructions configured to apply the techniques presented herein. Such computer-readable media may include, e.g., computer-readable storage media involving a tangible device, such as a memory semiconductor (e.g., a semiconductor utilizing static random access memory (SRAM), dynamic random access memory (DRAM), and/or synchronous dynamic random access memory (SDRAM) technologies), a platter of a hard disk drive, a flash memory device, or a magnetic or optical disc (such as a CD-R, DVD-R, or floppy disc), encoding a set of computer-readable instructions that, when executed by a processor of a device, cause the device to implement the techniques presented herein. Such computer-readable media may also include (as a class of technologies that are distinct from computer-readable storage media) various types of communications media, such as a signal that may be propagated through various physical phenomena (e.g., an electromagnetic signal, a sound wave signal, or an optical signal) and in various wired scenarios (e.g., via an Ethernet or fiber optic cable) and/or wireless scenarios (e.g., a wireless local area network (WLAN) such as WiFi, a personal area network (PAN) such as Bluetooth, or a cellular or radio network), and which encodes a set of computer-readable instructions that, when executed by a processor of a device, cause the device to implement the techniques presented herein. 
     An exemplary computer-readable medium that may be devised in these ways is illustrated in  FIG. 5 , wherein the implementation  80  comprises a computer-readable medium  82  (e.g., a CD-R, DVD-R, or a platter of a hard disk drive), on which is encoded computer-readable data  84 . This computer-readable data  84  in turn comprises a set of computer instructions  86  configured to operate according to the principles set forth herein. In one such embodiment, the processor-executable instructions  86  may be configured to perform a method of storing a data set in a storage set, such as the exemplary method  40  of  FIG. 3 . In another such embodiment, the processor-executable instructions  86  may be configured to implement a system for storing a data set in a storage set, such as the exemplary method  60  of  FIG. 4 . Some embodiments of this computer-readable medium may comprise a non-transitory computer-readable storage medium (e.g., a hard disk drive, an optical disc, or a flash memory device) that is configured to store processor-executable instructions configured in this manner. Many such computer-readable media may be devised by those of ordinary skill in the art that are configured to operate in accordance with the techniques presented herein. 
     The techniques discussed herein may be devised with variations in many aspects, and some variations may present additional advantages and/or reduce disadvantages with respect to other variations of these and other techniques. Moreover, some variations may be implemented in combination, and some combinations may feature additional advantages and/or reduced disadvantages through synergistic cooperation. The variations may be incorporated in various embodiments (e.g., the exemplary method  40  of  FIG. 3  and the exemplary method  60  of  FIG. 4 ) to confer individual and/or synergistic advantages upon such embodiments. 
     A first aspect that may vary among embodiments of these techniques relates to the scenarios wherein such techniques may be utilized. As a first variation, these techniques may be utilized to store a large variety of data sets  14 , including files comprising an operating environment of a computer, personal documents, media libraries, records in one or more database tables, and email mailboxes comprising sets of email messages. Additionally, such data sets  14  may comprise a wide variety of storage factors  16  selected from a storage factor set, such as a data set size factor (e.g., the total current or predicted size of the data set  14 ); a data set type factor (e.g., the type of data stored in the data set); a data set querying factor (e.g., whether or not queries of various types are to be applied to the data set  14 ); a data set access rate factor (e.g., the minimum acceptable access rate of the data set  14 ); and a data set security factor (e.g., the minimum degree and nature of security to be applied to the data set  14 ). 
     As a second variation of this first aspect, these techniques may be utilized to store such data sets  14  on a wide variety of storage components  18  (e.g., hard disk drives, solid-state storage systems, high-performance memory circuits, cloud storage services, and tape archives). Such storage components  18  may feature various types of file systems (e.g., disk file systems and network file systems) and/or protocols (e.g., a File Transfer Protocol (FTP), Server Message Block (SMB), Hypertext Transfer Protocol (HTTP), and Web-Based Distributed Authoring and Versioning (WebDAV)). Such storage components  18  may also comprise database servers configured to store various types of relational and/or non-relational databases comprising various types of database objects, such as records, tables, relationships, and stored procedures, and email servers configured to store email messages in email mailboxes. These storage components  18  may also feature various types of storage capabilities  34  selected from a storage capability set, including various types of features, such as a storage component type factor (e.g., the type of the storage component  18 ); a storage capacity factor (e.g., the total, available, or achievable storage capacity of the storage component  18 ); a storage access rate factor (e.g., the achievable rates of upload and download access and latency to the storage component  18 ); a storage querying interface factor (e.g., whether or not the storage component  18  supports various types of queries); and a storage security factor (e.g., the degree and details of security of the storage component  18 , such as encryption and user authentication.) The details of such storage capabilities  34  may include factors other than the device (e.g., the storage access rate factor may be limited to the achievable throughput of the network connecting the storage component  18  to an end user, and the storage security factor may include social policies, such as which individuals of an organization are permitted physical access to the storage component  18 ). Those of ordinary skill in the art may devise many types of data sets  14  and storage components  18  to which the techniques presented herein may be applied. 
     A second aspect that may vary among embodiments of these techniques relates to the manner of identifying the storage capabilities  34  of a particular storage component  16 . As a first variation, a user  20 , such as an administrator, may specify to an embodiment the storage capabilities of the storage component  16 . As a second variation, a storage component  16  may indicate to an embodiment the storage capabilities of the storage component  16 . For example, the storage component  16  may be able to report its storage capabilities  34 , such as a set of supported protocols. As a third variation, an embodiment may identify the storage capabilities  34  of a storage component  16  through detection or monitoring. For example, an embodiment may perform various throughput tests on a storage component to determine its practical sustainable throughput rate within the computing environment. Alternatively, an embodiment may have access to a storage log comprising storage events involving at least one storage component (e.g., records of data transfers performed over a network), and may be able to evaluate the storage log to identify the storage capabilities  34  of the storage components  18 . Those of ordinary skill in the art may devise many ways of identifying the storage capabilities  34  of various storage components  18  in accordance with the techniques presented herein. 
     A third aspect that may vary among embodiments of these techniques involves the manner of selecting a storage component  18  to store any particular data set  14 . As a first variation, the matching may involve many types of heuristics that compare the storage factors  32  of the data set  14  with the storage capabilities  34  of respective storage components  18  to make a suitable match. As a first example, the data set  14  may comprise a first entity that has a relationship with a second entity stored in a second data set (e.g., a dependency between software objects, a resource embedded in a document or application, a data-driven software application and a corresponding data set, or an interrelationship between relational database tables). An embodiment of these techniques may be able to identify the entity relationship between these entities, and may utilize this relationship as a storage factor  32  of the storage set  16 , such as a heuristic specifying a storing together of the data sets  14  having the entity relationship on the same storage component  18 . As a second example, a data set  14  may have a particular usage mapping, such as a set of users or software processes that utilize the data set  14  or an access pattern describing the manner in which the data set  14  may be accessed. The device may therefore utilize this usage mapping as a storage factor  32  of the storage set  16 , such as a heuristic specifying a selection of a storage device  18  that not only meets the minimum criteria of the data set  14 , but that facilitates the usage mapping, e.g., by facilitating the usage mappings. As a third examine, a data set  14  may have a particular logical schema. For example, the logical schema may define that a data set  14  comprises two or more data set components, such as a file system having subsets of files hierarchically organized into folders or a database comprising a set of tables. The device may therefore utilize this logical schema as a storage factor  32  of the storage set  16 , such as a heuristic specifying that a first storage component is to be stored on a first storage component  18 , while a second storage component is to be stored on a second storage component  18  that is different from the first storage component. 
     As a second variation of this third aspect, rather than choosing storage components  18  on an ad hoc basis for each data set  14 , an embodiment may holistically evaluate the data set group  12  and the storage set  16  in order to identify a physical schema  28 . As one such example, the embodiment may invoke a best-fit selection heuristic to choose the physical schema  28 . For example, the best-fit selection heuristic may first select storage components  18  for the data sets  14  having large or specialized storage factors  32  (e.g., very large data sets  14 , data sets  14  involving high concurrency or having usage mappings involving high access rates, or highly sensitive data sets  14 ), and may then select storage components  18  for the data sets  14  having smaller and more generalized storage factors  32  (e.g., small data sets  14  that are accessed in general ways and that may be placed anywhere). Those of ordinary skill in the art may devise many ways of selecting storage components  18  for data sets  14  in accordance with the techniques presented herein. 
     A fourth aspect that may vary among embodiments of these techniques involves the manner of implementing a physical schema  28  selected for a data set group  12  and a storage set  16 . As a first variation, an embodiment may automatically implement the physical schema  28 , e.g., by automatically provisioning storage on the storage components  18  for assigned data sets  14  and automatically configuring devices and software processes to utilize the provisioned storage component  18 . Alternatively, the embodiment may inform one or more users  20  (e.g., administrators) of the physical schema  28  (e.g., by providing the storage catalog  36  to the user  20 ), and possibly with instructions for implementing the physical schema  28  on the storage components  18 . 
     As a second variation, an embodiment of these techniques may participate in the implementation, e.g., by persistently connecting data consumers (e.g., data-driven applications) to data components  18  wherein the data sets  14  are stored. As a first such example, an embodiment may store different data set components of a data set  14  on different storage components  18 , or may store two data sets  14  having a relationship on two different storage components  18 . When a user  20  or application presents a request (such as a query) specifying the data set  14 , the embodiment may represent the data set  14  as a unified data set  14 , e.g., by contacting the first storage component  14  and the second storage component  14 , accessing the data set  18  stored on each storage component  14  (e.g., retrieving a first data subset from the first storage component  14  and a second data subset from the second storage component  14 ), and aggregating the responses or data to present the user  20  or application with a combined result set (e.g., a single query response or a single data set  18 ). Those of ordinary skill in the art may devise many ways of involving an embodiment in these techniques in the implementation of the automatically selected physical schema  28  in accordance with the techniques presented herein. 
     A fifth aspect that may vary among embodiments of these techniques involves the updating of the storage catalog  36  to reflect changes in the data set group  12  and/or the storage set  16 . As a first example, upon receiving a notification of an added storage component  18 , an embodiment may detect at least one storage capability  34  of the added storage component  18 , and may represent the added storage component  18  in the storage catalog  36 . Conversely, upon receiving a notification of a removed storage component  18 , an embodiment may remove the removed storage component  18  from the storage catalog  36  (optionally relocating any data sets  14  stored therein to other storage components  18 ). 
     As a second variation of this fifth aspect, an embodiment may occasionally reevaluate the storage catalog  36  to identify potential improvements. For example, after storing a data set  14  on a storage component  18 , an embodiment may compare the storage capabilities  32  of the selected storage component  18  for storing the data set  14  with the storage capabilities  32  of other storage components  18  of the storage set  16 , and upon identifying a second storage component  18  having storage capabilities  34  having a higher match with the storage factors  32  of the data set than the storage capabilities  34  of the selected storage component  18 , may relocate the data set  14  to the second storage component  18  and update the storage catalog  36  to associate the data set  14  with the second storage component  18 . This reevaluation may be performed occasionally (e.g., periodically or upon detecting a change to the data set group  12  and/or the storage set  16 ) in order to identify potential improvements therein that may improve the available capacities or performance of the storage set  16 . Those of ordinary skill in the art may devise many ways of configuring an embodiment of these techniques to update the data catalog  36  in accordance with the techniques presented herein. 
     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. 
     As used in this application, the terms “component,” “module,” “system”, “interface”, and the like are generally intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a controller and the controller can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. 
     Furthermore, the claimed subject matter may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier, or media. Of course, those skilled in the art will recognize many modifications may be made to this configuration without departing from the scope or spirit of the claimed subject matter. 
       FIG. 6  and the following discussion provide a brief, general description of a suitable computing environment to implement embodiments of one or more of the provisions set forth herein. The operating environment of  FIG. 6  is only one example of a suitable operating environment and is not intended to suggest any limitation as to the scope of use or functionality of the operating environment. Example computing devices include, but are not limited to, personal computers, server computers, hand-held or laptop devices, mobile devices (such as mobile phones, Personal Digital Assistants (PDAs), media players, and the like), multiprocessor systems, consumer electronics, mini computers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. 
     Although not required, embodiments are described in the general context of “computer readable instructions” being executed by one or more computing devices. Computer readable instructions may be distributed via computer readable media (discussed below). Computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), data structures, and the like, that perform particular tasks or implement particular abstract data types. Typically, the functionality of the computer readable instructions may be combined or distributed as desired in various environments. 
       FIG. 6  illustrates an example of a system  90  comprising a computing device  92  configured to implement one or more embodiments provided herein. In one configuration, computing device  92  includes at least one processing unit  96  and memory  98 . Depending on the exact configuration and type of computing device, memory  98  may be volatile (such as RAM, for example), non-volatile (such as ROM, flash memory, etc., for example) or some combination of the two. This configuration is illustrated in  FIG. 6  by dashed line  94 . 
     In other embodiments, device  92  may include additional features and/or functionality. For example, device  92  may also include additional storage (e.g., removable and/or non-removable) including, but not limited to, magnetic storage, optical storage, and the like. Such additional storage is illustrated in  FIG. 6  by storage  100 . In one embodiment, computer readable instructions to implement one or more embodiments provided herein may be in storage  100 . Storage  100  may also store other computer readable instructions to implement an operating system, an application program, and the like. Computer readable instructions may be loaded in memory  98  for execution by processing unit  96 , for example. 
     The term “computer readable media” as used herein includes computer storage media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions or other data. Memory  98  and storage  100  are examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVDs) or other optical 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 be accessed by device  92 . Any such computer storage media may be part of device  92 . 
     Device  92  may also include communication connection(s)  106  that allows device  92  to communicate with other devices. Communication connection(s)  106  may include, but is not limited to, a modem, a Network Interface Card (NIC), an integrated network interface, a radio frequency transmitter/receiver, an infrared port, a USB connection, or other interfaces for connecting computing device  92  to other computing devices. Communication connection(s)  106  may include a wired connection or a wireless connection. Communication connection(s)  106  may transmit and/or receive communication media. 
     The term “computer readable media” may include communication media. Communication media typically embodies computer readable instructions 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” may include a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. 
     Device  92  may include input device(s)  104  such as keyboard, mouse, pen, voice input device, touch input device, infrared cameras, video input devices, and/or any other input device. Output device(s)  102  such as one or more displays, speakers, printers, and/or any other output device may also be included in device  92 . Input device(s)  104  and output device(s)  102  may be connected to device  92  via a wired connection, wireless connection, or any combination thereof. In one embodiment, an input device or an output device from another computing device may be used as input device(s)  104  or output device(s)  102  for computing device  92 . 
     Components of computing device  92  may be connected by various interconnects, such as a bus. Such interconnects may include a Peripheral Component Interconnect (PCI), such as PCI Express, a Universal Serial Bus (USB), firewire (IEEE 1394), an optical bus structure, and the like. In another embodiment, components of computing device  92  may be interconnected by a network. For example, memory  98  may be comprised of multiple physical memory units located in different physical locations interconnected by a network. 
     Those skilled in the art will realize that storage devices utilized to store computer readable instructions may be distributed across a network. For example, a computing device  110  accessible via network  108  may store computer readable instructions to implement one or more embodiments provided herein. Computing device  92  may access computing device  110  and download a part or all of the computer readable instructions for execution. Alternatively, computing device  92  may download pieces of the computer readable instructions, as needed, or some instructions may be executed at computing device  92  and some at computing device  110 . 
     Various operations of embodiments are provided herein. In one embodiment, one or more of the operations described may constitute computer readable instructions stored on one or more computer readable media, which if executed by a computing device, will cause the computing device to perform the operations described. The order in which some or all of the operations are described should not be construed as to imply that these operations are necessarily order dependent. Alternative ordering will be appreciated by one skilled in the art having the benefit of this description. Further, it will be understood that not all operations are necessarily present in each embodiment provided herein. 
     Moreover, the word “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as advantageous over other aspects or designs. Rather, use of the word exemplary is intended to present concepts in a concrete fashion. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. In addition, the articles “a” and “an” as used in this application and the appended claims may generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. 
     Also, although the disclosure has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art based upon a reading and understanding of this specification and the annexed drawings. The disclosure includes all such modifications and alterations and is limited only by the scope of the following claims. In particular regard to the various functions performed by the above described components (e.g., elements, resources, etc.), the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure which performs the function in the herein illustrated exemplary implementations of the disclosure. In addition, while a particular feature of the disclosure may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. Furthermore, to the extent that the terms “includes”, “having”, “has”, “with”, or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising.”