Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on February 25, 2025 and May 19, 2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 2-4, 8, and 10-20 objected to because of the following informalities:
Claim 2, line 10, “the count” lacks proper antecedent basis.
Claim 3, line 11, “the single weighted backlog indicator” lacks proper antecedent basis.
Claim 8, line 13, “the request” lacks proper antecedent basis and line 18, after “from”, --the-- might be needed.
Claim 10, line 12, “the customer”, line 14, “the customer”, line 16, “the customer”, and line 18, “the customer” lack proper antecedent basis.
Claim 11, line 7, “the data platform” lacks proper antecedent basis.
Claim 12, line 10, “the count” lacks proper antecedent basis.
Claim 13, line 10, “the single weighted backlog indicator” lacks proper antecedent basis.
Claim 18, lines 3-4, “the data platform” lacks proper antecedent basis.
Claim 19, line 10, “the count” lacks proper antecedent basis.
Claim 20, line 11, “the single weighted backlog indicator” lacks proper antecedent basis.
Claims 4 and 14-17 depend on objected claims and inherit the same issues as objected claims.
Appropriate correction is required. Applicant is advised to review entire claims for further needed correction.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 7-8, 11-12, 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Channapattan et al. (U.S. Patent Publication No. US 10942780 B1, hereinafter “Channapattan”) in view Yeap et al. (U.S. Patent Application Publication No. US 20200050556 A1, hereinafter “Yeap”) and of Gabrielson (U.S. Patent Publication No. US 11032392 B1).
With regard to claim 1, Channapattan discloses:
A method (“methods”, col 2, line 8) comprising:
obtaining, by processing circuitry of a data platform (“The distributed computing system 100 is an example of a system implemented as computer programs on one or more computers in one or more locations, in which the systems, components, and techniques described below can be implemented.”, col 4, lines 36-40), a generic backlog indicator (i.e. "access cost") for a plurality of workloads to execute via the data platform ("The distributed computing system 100 generates measures of resource load, e.g., resource cost 170 of one or more tasks performed by a distributed computing framework 101 in the distributed computing system 100 based on performance data about the operation of the distributed computing framework 101 and a resource cost model 169 for calculating each measure of resource cost 170.", col 4, lines 41-47, "As another example, the distributed computing system 100 can generate a measure of access cost associated with accessing one or more files on the distributed computing framework 101 based on performance data about access counts 163 of one or more files on the on the distributed computing framework 101 and a resource cost model 169 for generating measures of access cost.", col 4, lines 54-60),
obtaining, by the processing circuitry of the data platform (“The distributed computing system 100 is an example of a system implemented as computer programs on one or more computers in one or more locations, in which the systems, components, and techniques described below can be implemented.”, col 4, lines 36-40), a custom backlog indicator (i.e. "execution cost") for at least a subset of the plurality of workloads to execute via the data platform ("The distributed computing system 100 generates measures of resource load, e.g., resource cost 170 of one or more tasks performed by a distributed computing framework 101 in the distributed computing system 100 based on performance data about the operation of the distributed computing framework 101 and a resource cost model 169 for calculating each measure of resource cost 170.", col 4, lines 41-47, "For example, the distributed computing system 100 can generate a measure of execution cost of an execution job, i.e., an execution task, based on performance data about resource usage 161 and execution time 162 associated with the execution job and a resource cost model 169 for generating measures of execution cost." col 4, lines 48-53);
calculating, by the processing circuitry using a priority manager (i.e. “resource cost model 169”), a single weighted backlog indicator value (i.e. "a particular measure of combined resource cost") for each of the plurality of workloads to execute via the data platform, by applying configurable weights (i.e. "particular weights") to each of the generic backlog indicator (i.e. "execution cost") and the custom backlog indicator (i.e. "access cost") for a respective workload from the plurality of workloads ("For example, a resource cost model 169 for generating a particular measure of combined resource cost associated with one or more tasks could instruct the system 100 to combine two or more costs, e.g., all three of, the execution cost, the access cost, and the storage cost associated with the one or more tasks to generate the particular measure of combined resource cost. The two or more costs can be combined, for example, using a sum or weighted sum in which particular costs are assigned particular weights.", col 5, lines 10-18);
Channapattan does not disclose:
wherein each of the plurality of workloads specify one or more storage system maintenance operations for one or more storage systems managed by the data platform;
scheduling, by the processing circuitry using a scheduler, the plurality of workloads for execution on the data platform based on the single weighted backlog indicator value calculated for each of the plurality of workloads; and
processing, by the processing circuitry of the data platform, the plurality of workloads according to the scheduling.
Yeap discloses:
wherein each of the plurality of workloads specify one or more storage system maintenance operations for one or more storage systems managed by the data platform (“Data reclamation is one such operation. Sometimes referred to as “garbage collection” or “folding,” data reclamation is a process widely deployed in flash-memory subsystems to reclaim unused portions of memory. Data reclamation addresses the need to erase flash-memory in blocks before writing new data to it. When data stored in a memory subsystem is no longer needed (e.g., because the host system “deleted” or rewrote it), the data is not immediately deleted but rather flagged as no longer needed (e.g., “stale”). Because the stale data may be stored with other non-stale data in a portion of memory that is erased as a block, a data reclamation process occasionally moves the non-stale data to another portion of memory so that the block of memory can be erased and made available for new data. Thus, the data reclamation process preserves non-stale or “valid” data while freeing the space associated with the stale or “invalid” data.”, para [0012]);
Both the systems of Channapattan and Yeap deal with processing computing operations. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine Channapattan in view of Yeap to improve storage utilization.
Gabrielson discloses:
scheduling, by the processing circuitry using a scheduler (i.e. “load balancer 620”), the plurality of workloads for execution on the data platform based on the single weighted backlog indicator value (i.e. “priority value”) calculated for each of the plurality of workloads (“A scheduling decision may include decisions to process (without waiting) the request or throttle processing of the request (e.g., by queuing the request) or the dropping, canceling, or otherwise refusing to complete processing to the request), in some embodiments. For example, queuing the request may include placing the request at different locations within the queue according to a priority value (e.g., a weight or other value received and/or determined from the information in the request) or into one of many different queues from which requests are dispatched or otherwise performed differently (e.g., at different time intervals).”, col 18, lines 19-29, “Load balancer 620 may queue the request 612 when received. In some embodiments, a FIFO queue may be implemented. However, in other embodiments, other queuing techniques may be implemented similar to those discussed below with regard to FIG. 7.”, col 15, lines 49-53, “FIG. 7 is a high-level flowchart illustrating various methods and techniques for including information regarding prior requests in requests for scheduling subsequent requests, according to some embodiments.”, col 16, lines 47-50); and
processing, by the processing circuitry of the data platform, the plurality of workloads according to the scheduling (“For example, the request may be stored, inserted, or otherwise input into a queue according to a priority value determined for the request. In some embodiments, the request may be dropped or otherwise processed without queuing as determined by the decision. The request may be dispatched from the queue to a request processor, or in some embodiments, the request may be directly sent to a request processing component (e.g., without an dispatch component) so that requests pulled from the queue are then performed by the receiving component.” .”, col 18, lines 47-56).
Both the systems of Channapattan and Gabrielson deal with processing computing operations. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine Channapattan as modified in view of Gabrielson to optimize task scheduling.
With regard to claim 2, Channapattan as modified discloses the method of claim 1,
Channapattan does not disclose however, Yeap discloses:
wherein the one or more storage system maintenance operations include at least one of:
garbage removal operations that, responsive to determining the data platform has identified data as available for removal from the one or more storage systems managed by the data platform, remove the data from the one or more storage systems (“Data reclamation is one such operation. Sometimes referred to as “garbage collection” or “folding,” data reclamation is a process widely deployed in flash-memory subsystems to reclaim unused portions of memory. Data reclamation addresses the need to erase flash-memory in blocks before writing new data to it. When data stored in a memory subsystem is no longer needed (e.g., because the host system “deleted” or rewrote it), the data is not immediately deleted but rather flagged as no longer needed (e.g., “stale”). Because the stale data may be stored with other non-stale data in a portion of memory that is erased as a block, a data reclamation process occasionally moves the non-stale data to another portion of memory so that the block of memory can be erased and made available for new data. Thus, the data reclamation process preserves non-stale or “valid” data while freeing the space associated with the stale or “invalid” data.”, para [0012]);
Both the systems of Channapattan and Yeap deal with processing computing operations. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine Channapattan as modified in view of Yeap to improve storage utilization.
With regard to claim 7, Channapattan as modified discloses the method of claim 1.
Channapattan further discloses:
wherein the custom backlog indicator (i.e. “execution cost”) comprises a measure of utilization for a specified one or more resources (i.e. “one or more memory resources”) within the data platform affected by the data platform executing the respective workload from the plurality of workloads (“FIG. 3 uses the example of memory usage to illustrate the generation of the measure of execution cost. However, this process can be used in a similar manner for generating the measure of execution cost for other resources, including processor usage, e.g., CPU or GPU, network bandwidth, or other resources.”, col 10, lines 4-9, “The system obtains memory use information that quantifies an amount of one or more memory resources of the system used to perform a particular job (302). For example, the system can obtain information that indicates that the system has used 10 megabytes of memory of a first cluster to perform the particular job.”, col 5, lines 10-15).
With regard to claim 8, Channapattan as modified discloses the method of claim 1.
Channapattan further discloses:
wherein each respective workload from the plurality of workloads embodies at least one of:
a job specifying the one or more executable tasks to be performed via the data platform (“A task is a combination of one or more operations performed by at least one component of a computing system, such as a processing task, a storage task, or a data access task.”, col 1, lines 12-15);
With regard to claim 11, the limitations except those addressed below are rejected using the mapping from analogous claim 1.
Channapattan discloses:
A computing system comprising (“computer systems”, col 2, line 6):
a storage device (“Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.” col 2, lines 5-8); and
processing circuitry having access to the storage device and configured to (“For a system of one or more computers to be configured to perform particular operations or actions means that the system has installed on it software, firmware, hardware, or a combination of them that in operation cause the system to perform the operations or actions.”, col 2, lines 10-14):
With regard to claim 12, Channapattan as modified discloses the computing system of claim 11.
The remaining limitations are rejected using the mapping from analogous claim 2.
With regard to claim 17, Channapattan as modified discloses the computing system of claim 11.
The remaining limitations are rejected using the mapping from analogous claim 7.
With regard to claim 18, the limitations except those addressed below are rejected using the mapping from analogous claim 1.
Channapattan discloses:
A computer-readable storage medium comprising instructions that, when executed, configure processing circuitry of a computing system (“Computer readable media suitable for storing computer program instructions”, col 15, lines 27-28, “For one or more computer programs to be configured to perform particular operations 15 or actions means that the one or more programs include instructions that, when executed by data processing apparatus, cause the apparatus to perform the operations or actions.”, col 2, line 13-18) to:
With regard to claim 19, Channapattan as modified discloses computer-readable storage medium of claim 18.
The remaining limitations are rejected using the mapping from analogous claim 2.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Channapattan in view Yeap and Gabrielson as applied to claim 1 above, and further in view Chou et al. (U.S. Patent Application Publication No. US 20190155652 A1, hereinafter “Chou”) and of Hagmann et al. (U.S. Patent Publication US 9280386 B1. hereinafter “Hagmann”).
With regard to claim 9, Channapattan as modified discloses the method of claim 1.
Channapattan does not disclose:
wherein obtaining the generic backlog indicator for the plurality of workloads to execute via the data platform, comprises:
calculating the generic backlog indicator for each of the plurality of workloads based on one or more of:
a configurable urgency value for each respective workload of the plurality of workloads;
a backlog indicator value for each respective workload of the plurality of workloads representing a period of time the respective workload from the plurality of workloads has failed to initiate execution at the data platform beyond an initially scheduled execution time for the respective workload from the plurality of workloads;
a quiet time value for each respective workload of the plurality of workloads representing a period of time the respective workload from the plurality of workloads was not executing between repeated executions of the respective workload from the plurality of workloads;
a workload deadline value for each respective workload of the plurality of workloads indicating the respective workload from the plurality of workloads encountered a configurable deadline without finishing execution at the data platform;
a workload deadline count for each respective workload of the plurality of workloads indicating a number of times the respective workload from the plurality of workloads encountered a configurable deadline without finishing execution at the data platform over a configurable historical time period or a historical quantity of execution attempts for the respective workload from the plurality of workloads;
an actions emitted count for each respective workload of the plurality of workloads indicating a number of outputs from executable tasks executed to completion by the data platform as part of each respective workload of the plurality of workloads;
an aggregate emissions time on a per-workload basis, calculated for each respective workload of the plurality of workloads to indicate a total amount of processing time required to fully complete one or more executable tasks performed by the data platform as part of the respective workload from the plurality of workloads; and
storing the custom backlog indicator as calculated for each of the plurality of workloads
in unique association with the respective workload from the plurality of workloads for which the
custom backlog indicator was calculated.
Chou discloses:
wherein obtaining the generic backlog indicator for the plurality of workloads to execute via the data platform, comprises:
calculating the generic backlog indicator (i.e. “task weight score”) for each of the plurality of workloads based on one or more of:
a configurable urgency value (i.e. “priority”) for each respective workload of the plurality of workloads (“the second resource provisioning device obtains the task weight score of each task”, “The task weight score of each task is expressed as follows: task weight score=priority×amount of data, wherein the priority of each task is assigned by the user or the resource-management device.”, para [0037]);
Both the systems of Channapattan and Chou deal with generating metrics for computing tasks. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine Channapattan as modified in view of Chou to improve task scheduling.
Hagmann discloses:
storing the custom backlog indicator as calculated for each of the plurality of workloads
in unique association with the respective workload from the plurality of workloads for which the
custom backlog indicator was calculated (“For example, the metric data engine 106 can access the database 108 and analyze the metric data associated with each dictionary task instance performed on Platform A and determine the mean CPI and standard deviation of the dictionary task.”, col 7, lines 33-37, “The metric data engine 106 can store the calculated statistical data in the database and include information identifying Platform A and the task instance associated with the statistical data.”, col 7, lines 39-42, “The metric data engine 106 can also associate information to uniquely identify a task instance with the metric data associated with the task instance.”, col 4, lines 26-28).
Both the systems of Channapattan and Hagmann deal with generating metrics for computing operations. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine Channapattan as modified in view of Hagmann to reduce computational overhead by saving the generated metric for future access.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Channapattan in view Yeap and Gabrielson as applied to claim 1 above, and further in view of Hagmann et al. (U.S. Patent Publication US 9280386 B1. hereinafter “Hagmann”).
With regard to claim 10, Channapattan as modified discloses the method of claim 1.
Channapattan further discloses:
wherein obtaining the custom backlog indicator for the plurality of workloads to execute via the data platform, comprises:
calculating the custom backlog indicator for at least a subset of the plurality of workloads based on one or more of (“FIG. 3 uses the example of memory usage to illustrate the generation of the measure of execution cost. However, this process can be used in a similar manner for generating the measure of execution cost for other resources, including processor usage, e.g., CPU or GPU, network bandwidth, or other resources.”, col 10, lines 4-9):
a workload-specific total utilization value for the respective workload from the plurality of workloads to be scheduled (“The system obtains memory use information that quantifies an amount of one or more memory resources of the system used to perform a particular job (302). For example, the system can obtain information that indicates that the system has used 10 megabytes of memory of a first cluster to perform the particular job.”, col 10, lines 10-15);
Channapattan does not disclose however, Hagmann discloses:
storing the custom backlog indicator as calculated for each of the plurality of workloads
in unique association with the respective workload from the plurality of workloads for which the
custom backlog indicator was calculated (“For example, the metric data engine 106 can access the database 108 and analyze the metric data associated with each dictionary task instance performed on Platform A and determine the mean CPI and standard deviation of the dictionary task.”, col 7, lines 33-37, “The metric data engine 106 can store the calculated statistical data in the database and include information identifying Platform A and the task instance associated with the statistical data.”, col 7, lines 39-42, “The metric data engine 106 can also associate information to uniquely identify a task instance with the metric data associated with the task instance.”, col 4, lines 26-28).
Both the systems of Channapattan and Hagmann deal with generating metrics for computing operations. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine Channapattan as modified in view of Hagmann to reduce computational overhead by saving the generated metric for future access.
Allowable Subject Matter
Claims 3-6, 13-16, and 20 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yang (U.S. Patent Application Publication No. US 20190034223 A1) discloses “The task resource scheduling method and apparatus provided by the present disclosure, through determining priority levels of multiple tasks as well as set task deadline times, and determining a preset resource upper bound, based on task priority levels schedule the successive order of executing multiple tasks, and post-scheduling tasks satisfy a first condition, wherein, the first condition is that a time resource conflict does not exist for the tasks, the time resource conflict being that a section exists on a time series where more than one task overlaps, and the sum of the resources occupied in the section having the overlap is greater than the resource upper bound.” (Yang, para [0014]).
Anderson (U.S. Patent Application Publication No. US 20150172204 A1) discloses “A given tenant can assign different priorities to different applications based on the needs of the application and the importance of the application to the tenant. FIGS. 3 and 4 provided an example of different priorities being assigned to different applications running in a given enterprise.” (Anderson, para [0051]), “At step 650, the process identifies the workload's current demand and also calculates the workload's weighted priority based on the tenant priority, the workload priority and the current, or expected, demand for the workload. The weighted priorities for the workloads are stored in memory area 655.” (Anderson, para [0051]).
Zou (Chinese Patent Application No. CN 111694652 B) discloses “In one embodiment, the influence factor comprises an emergency degree, an execution value and an equalization factor, and in step S4, that is, the influence factor is used to perform real-time priority calculation on the normal task to obtain the real-time priority corresponding to each normal task comprises the following steps: S41: according to formula (2), calculating the real-time priority corresponding to each normal task: p=ds * (1-a) + wr*a formula (2) wherein p is the real-time priority, ds is the emergency degree, a is the balance factor, wr is the execution value. Specifically, the real-time priority corresponding to each normal task of formula (2) is used. Further, the emergency degree ds in the formula (2) can be calculated by the formula (3), and the execution value wr can be calculated by the formula (4)” (Zou, page 9, fourth paragraph).
Geng (Chinese Patent Application No. CN 113342497 A) discloses “the calculation formula of the preset scheduling algorithm is as follows: f=a * Jwait + (b-c * Jrun); the task to be scheduled with large f value is preferentially dispatched by the scheduler. in the preset scheduling algorithm, a, b and c are the initial scheduling parameter in the present embodiment, a is the first weight control parameter of the waiting time, the value range of a is [0, 1], c is the second weight control parameter of the execution time, b is the third weight control parameter of the execution time; the value range of c can be (the longest execution time length in the task to be scheduled).” (Geng, page 8, last paragraph-page 9, first paragraph).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SELENA SABAH NAHRA whose telephone number is (571)272-6115. The examiner can normally be reached Monday-Thursday 7:00 AM -5:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hyung Sough can be reached at (571) 272-6799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/S.S.N./Examiner, Art Unit 2192
/S. Sough/SPE, Art Unit 2192
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