Abstract: A method, system, and apparatus to divide a computing job into micro-jobs and allocate the execution of the micro-jobs to times when needed resources comply with one or more idleness criteria is provided. The micro-jobs are executed on an ongoing basis, but only when the resources needed by the micro-jobs are not needed by other jobs. A software program utilizing this methodology may be run at all times while the computer is powered up without impacting the performance of other software programs running on the same computer system.

Abstract: Resource-based scheduling of computer jobs is disclosed. A computer job is scheduled based on utilization of a resource and a utilization criterion that the computer job has pertaining to the resource, in accordance with an embodiment of the present invention.

Abstract: A method, system, and apparatus to divide a computing job into micro-jobs and allocate the execution of the micro-jobs to times when needed resources comply with one or more idleness criteria is provided. The micro-jobs are executed on an ongoing basis, but only when the resources needed by the micro-jobs are not needed by other jobs. A software program utilizing this methodology may be run at all times while the computer is powered up without impacting the performance of other software programs running on the same computer system.

Abstract: A method, system, and apparatus to divide a computing job into micro-jobs and allocate the execution of the micro-jobs to times when needed resources comply with one or more idleness criteria is provided. The micro-jobs are executed on an ongoing basis, but only when the resources needed by the micro-jobs are not needed by other jobs. A software program utilizing this methodology may be run at all times while the computer is powered up without impacting the performance of other software programs running on the same computer system.

Abstract: A system and method for efficient data storage, which reduces data entropy of data on a data storage device. The technique organizes related data entities (such as files on a hard drive) in close physical proximity and in a predictive or ordered sequence, to reduce the amount of time and effort (mechanical, computational, or otherwise) a storage/retrieval device needs to expend locating each data related entity as it processes a sequence of requests. In addition, the data may be organized and stored according to a file directory index structure, whereby file fragmentation is reduced.

Abstract: A system and method for efficient data storage, which reduces data entropy of data on a data storage device. The technique organizes related data entities (such as files on a hard drive) in close physical proximity and in a predictive or ordered sequence, to reduce the amount of time and effort (mechanical, computational, or otherwise) a storage/retrieval device needs to expend locating each related data entity as it processes a sequence of requests. In addition, the data may be organized and stored according to a file directory index structure, whereby file fragmentation is reduced.

Abstract: A method, system, and apparatus for improving performance when retrieving data from one or more storage media. Files to be stored on the one or more storage media are classified into a ranking of different sets. Differences in retrieval value of different regions of the one or more storage media are exploited by selecting which files to store in which regions. For example, files that have a higher classification are stored in regions with faster retrieval values. The files can be classified based on frequency of access. Thus, files that are more frequently accessed are stored in regions that have a faster retrieval value. The files can be classified by another measure such as priority. For example, the classification for some or all of the files can be based on user-assigned priority. The classification may be based on events or data grouping.

Abstract: A method, system, and apparatus for improving performance when retrieving data from one or more storage media. Files to be stored on the one or more storage media are classified into a ranking of different sets. Differences in retrieval value of different regions of the one or more storage media are exploited by selecting which files to store in which regions. For example, files that have a higher classification are stored in regions with faster retrieval values. The files can be classified based on frequency of access. Thus, files that are more frequently accessed are stored in regions that have a faster retrieval value. The files can be classified by another measure such as priority. For example, the classification for some or all of the files can be based on user-assigned priority. The classification may be based on events or data grouping.

Abstract: A method, system, and apparatus for improving performance when retrieving data from one or more storage media. Files to be stored on the one or more storage media are classified into a ranking of different sets. Differences in retrieval value of different regions of the one or more storage media are exploited by selecting which files to store in which regions. For example, files that have a higher classification are stored in regions with faster retrieval values. The files can be classified based on frequency of access. Thus, files that are more frequently accessed are stored in regions that have a faster retrieval value. The files can be classified by another measure such as priority. For example, the classification for some or all of the files can be based on user-assigned priority. The classification may be based on events or data grouping.

Abstract: A method, system, and apparatus for improving performance when retrieving data from one or more storage media. Files to be stored on the one or more storage media are classified into a ranking of different sets. Differences in retrieval value of different regions of the one or more storage media are exploited by selecting which files to store in which regions. For example, files that have a higher classification are stored in regions with faster retrieval values. The files can be classified based on frequency of access. Thus, files that are more frequently accessed are stored in regions that have a faster retrieval value. The files can be classified by another measure such as priority. For example, the classification for some or all of the files can be based on user-assigned priority. The classification may be based on events or data grouping.

Abstract: Computer micro-jobs are disclosed. A computer job is divided into micro-jobs. In one embodiment, the micro-jobs have a size that allows a particular micro-job to complete within an allotted time for which the particular micro-job owns a resource used to execute the micro-job. In one embodiment, the allotted time is a quantum. In one embodiment, an entire computer job is divided into micro-jobs and the computer job is then executed micro-job by micro-job until the entire computer job is complete. Each of the micro-jobs may complete its execution within its quantum, in one embodiment. In one embodiment, the execution of the micro-jobs is allocated to times when needed resources comply with one or more idleness criteria. A software program executed with micro-jobs may be run at all times while the computer is powered up without impacting the performance of other software programs running on the same computer system.

Abstract: A method, system, and apparatus to divide a computing job into micro-jobs and allocate the execution of the micro-jobs to times when needed resources comply with one or more idleness criteria is provided. The micro-jobs are executed on an ongoing basis, but only when the resources needed by the micro-jobs are not needed by other jobs. A software program utilizing this methodology may be run at all times while the computer is powered up without impacting the performance of other software programs running on the same computer system.

Abstract: A method, system, and apparatus to divide a computing job into micro-jobs and allocate the execution of the micro-jobs to times when needed resources comply with one or more idleness criteria is provided. The micro-jobs are executed on an ongoing basis, but only when the resources needed by the micro-jobs are not needed by other jobs. A software program utilizing this methodology may be run at all times while the computer is powered up without impacting the performance of other software programs running on the same computer system.

Abstract: A system and method for efficient data storage, which reduces data entropy of data on a data storage device. The technique organizes related data entities (such as files on a hard drive) in close physical proximity and in a predictive or ordered sequence, to reduce the amount of time and effort (mechanical, computational, or otherwise) a storage/retrieval device needs to expend locating each data entity as it processes a sequence of requests. For example, the data may be organized and stored according to a file directory index structure, whereby data and free space fragmentation is also reduced.

Abstract: Computer micro-jobs are disclosed. A computer job is divided into micro-jobs. In one embodiment, the micro-jobs have a size that allows a particular micro-job to complete within an allotted time for which the particular micro-job owns a resource used to execute the micro-job. In one embodiment, the allotted time is a quantum. In one embodiment, an entire computer job is divided into micro-jobs and the computer job is then executed micro-job by micro-job until the entire computer job is complete. Each of the micro-jobs may complete its execution within its quantum, in one embodiment. In one embodiment, the execution of the micro-jobs is allocated to times when needed resources comply with one or more idleness criteria. A software program executed with micro-jobs may be run at all times while the computer is powered up without impacting the performance of other software programs running on the same computer system.

Abstract: A system and method for efficient data storage, which reduces data entropy of data on a data storage device. The technique organizes related data entities (such as files on a hard drive) in close physical proximity and in a predictive or ordered sequence, to reduce the amount of time and effort (mechanical, computational, or otherwise) a storage/retrieval device needs to expend locating each data entity as it processes a sequence of requests. For example, the data may be organized and stored according to a file directory index structure, whereby data and free space fragmentation is also reduced.

Abstract: A method for optimizing a solid state drive is described. The method involves determining whether a free space fragment on the SSD is smaller than the threshold fragment size. If the free space fragment on the SSD is smaller than the threshold fragment size, eliminating the free space fragment. If the free space fragment on the SSD is not smaller than the threshold fragment size, retaining the free space fragment for storing data. Elimination of the free space fragments smaller than the threshold fragment size results in a fewer number of free space fragments being used when writing to the SSD, allowing for improved SSD performance.

Abstract: Computer micro-jobs are disclosed. A computer job is divided into micro-jobs. In one embodiment, the micro-jobs have a size that allows a particular micro-job to complete within an allotted time for which the particular micro-job owns a resource used to execute the micro-job. In one embodiment, the allotted time is a quantum. In one embodiment, an entire computer job is divided into micro-jobs and the computer job is then executed micro-job by micro-job until the entire computer job is complete. Each of the micro-jobs may complete its execution within its quantum, in one embodiment. In one embodiment, the execution of the micro-jobs is allocated to times when needed resources comply with one or more idleness criteria. A software program executed with micro-jobs may be run at all times while the computer is powered up without impacting the performance of other software programs running on the same computer system.

Abstract: A method, system, and apparatus for improving performance when retrieving data from one or more storage media. Files to be stored on the one or more storage media are classified into a ranking of different sets. Differences in retrieval value of different regions of the one or more storage media are exploited by selecting which files to store in which regions. For example, files that have a higher classification are stored in regions with faster retrieval values. The files can be classified based on frequency of access. Thus, files that are more frequently accessed are stored in regions that have a faster retrieval value. The files can be classified by another measure such as priority. For example, the classification for some or all of the files can be based on user-assigned priority. The classification may be based on events or data grouping.

Abstract: A method, system, and apparatus for improving performance when retrieving data from one or more storage media. Files to be stored on the one or more storage media are classified into a ranking of different sets. Differences in retrieval value of different regions of the one or more storage media are exploited by selecting which files to store in which regions. For example, files that have a higher classification are stored in regions with faster retrieval values. The files can be classified based on frequency of access. Thus, files that are more frequently accessed are stored in regions that have a faster retrieval value. The files can be classified by another measure such as priority. For example, the classification for some or all of the files can be based on user-assigned priority. The classification may be based on events or data grouping.