BRIEF DESCRIPTION OF THE DRAWINGS
For a more complete understanding of the present invention, the needs satisfied thereby, and the features and technical advantages thereof, reference now is made to the following descriptions taken in connection with the accompanying drawings.
FIG. 1 is a schematic of a system for improving uniformity of assets within an entity, according to an embodiment of the present invention.
FIG. 2 is an exemplary schematic of two different clusters of assets within an entity, and an exemplary deviation of one of the cluster of assets from the center of the cluster, according to an embodiment of the present invention.
FIG. 3 is a flow chart of a method for improving uniformity of assets within an entity, according to an embodiment of the present invention.
DETAILED DESCRIPTION OF EMBODIMENTS
Embodiments of the present invention and their features and technical advantages may be understood by referring to FIGS. 1-3, like numerals being used for like corresponding parts in the various drawings.
Referring to FIG. 1, a system 100 for improving uniformity of assets within an entity, according to an embodiment of the present invention, is depicted. System 100 may comprise a database 104, a processing arrangement 102 coupled to database 104, and a client-side arrangement 106 coupled to processing arrangement 102.
Database 104 may store data associated with a plurality of different assets, e.g., a laptop computer, a desktop computer, a server, or the like, and combinations thereof, of a particular entity, e.g., a corporation, a partnership, or the like, which is a client of an operator of system 100. The data associated with each of the assets may comprise information associated with different features of the asset. For example, such features may be a vendor of the asset, an operating system of the asset, an age of the asset, a speed of the asset, an amount of memory associated with the asset, a number of computer processing units associated with the asset, software installed on the asset, or the like, and combinations thereof.
Referring to FIGS. 1 and 2, processing arrangement 102 may be configured to retrieve the data from database 104, and to group the assets into one or more clusters of assets based on the data associated with the assets. For example, referring to FIG. 2, processing arrangement 102 may group the assets into a first cluster of assets 202 comprising assets 202a-202j, and a second cluster of assets 204 comprising assets 204a-204f. Although FIG. 2 depicts a pair of clusters, those of ordinary skill in the art readily will understand that processing arrangement 102 may group the assets into any number of cluster of assets, and that each cluster of assets may comprise any number of assets. For example, the number of clusters of assets may depend on the number of different types of assets associated with the particular entity, and the degree of similarities and difference between each of the assets within each of the different types of assets. In this embodiment of the present invention, processing arrangement 104 grouped the assets into first cluster of assets 202 and second cluster of assets 204 based on the data associated with the assets. Specifically, although the features of assets 202a-202j are different, the features of assets 202a-202j were similar enough to group them together in first cluster of assets 202, and the features of assets 204a-204f were similar enough to group them together in second cluster of assets 204. Moreover, when each of the assets are plotted within the cluster, the number dimensions of the plot may correspond to the number of features which were considered, e.g., if four features are considered then the plot of the clusters may be a four-dimensional plot. Nevertheless, those of ordinary skill in the art readily will understand that the plot of the assets may plot having an number of dimensions. Each of first cluster of assets 202 and second cluster of assets 204 has a center point which may correspond to a hypothetical standard metric associated with the assets included in the cluster of assets. For example, as shown in FIG. 2, the hypothetical standard metric may correspond to a Z-score which represents a deviation from the norm of the cluster of assets. Alternatively, a curved-line fitting method, such as a linear regression method, may be employed to predict/determine a desired or an ideal hypothetical standard metric for the cluster of assets. In this embodiment of the present invention, because workload-configuration models may change from year to year, and thus, the desired or ideal hypothetical standard metric may change over time, the curved-line fitting model may made to be a time-dependent model by incorporating a time/date related variable into the model. Moreover, processing arrangement 102 may be configured to determine a degree of difference, e.g., a deviation from the hypothetical standard metric, between each of the assets included in the cluster of assets and the hypothetical standard metric.
In operation, client-side arrangement 106, e.g., a processing arrangement associated with the particular entity or an individual or group of individuals associated with the particular entity, may select one or more of the assets of each cluster and to alter one or more of the features of the selected assets. For example, client-side arrangement 106 may select those assets of each cluster which have the greatest degree of difference with respect to the hypothetical standard metric. Client-side arrangement 106 then may alter the features of the selected assets, such that the degree of difference between the selected assets and the hypothetical standard metric decreases, which results in the assets within the cluster being more uniform with respect to each other. Alternatively, if the degree of difference between a selected asset and the hypothetical standard metric is sufficiently large, the selected asset may be removed from the assets associated with the particular entity. Thus, by grouping the assets of the particular entity into clusters of assets, and determining the degree of difference between the assets within the cluster and the hypothetical standard metric, assets of similar types may be made more uniform with respect to each other, which makes it less difficult, time consuming, and expensive for the information technology department to manage the assets.
Referring to FIG. 3, a method for improving uniformity of assets within an entity, according to an embodiment of the present invention is depicted. In step 302, data associated with the assets may be retrieved, e.g., from database 104, and in step 304 the assets may be grouped into one or more clusters based on the data associated the assets. In step 306, the degree of difference between each asset and the hypothetical standard metric may be determined. Moreover, in step 308, one or more assets may be selected based on the degree of difference between the selected asset and the hypothetical standard metric, and in step 310 one or more features of the selected asset may be altered to decrease the degree of difference between the selected asset and the hypothetical standard metric.
While the invention has been described in connection with exemplary embodiments, it will be understood by those skilled in the art that other variations and modifications of the exemplary embodiments described above may be made without departing from the scope of the invention. Other embodiments will be apparent to those skilled in the art from a consideration of the specification or practice of the invention disclosed herein. It is intended that the specification and the described examples are considered merely as exemplary of the invention, with the true scope of the invention being indicated by the flowing claims.
Patent Application
20080082350
