DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to application filed on 5/31/2024, wherein claims 1-20 are pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4-5, 14 and 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
The following claim terms lacks antecedent basis:
Claim 4: “the first amount of resources”.
Claim 5: “the first amount of resources”.
Claim 14, “the first amount of resources”.
Claim 15: “the first amount of resources”.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 6-11, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over DeSantis (US PGPUB 2018/0129628A1), in view of Casillas et al. (EP 3529698 B1)
As for claim 1, Casillas teaches a computer-implemented method executed on data processing hardware that causes the data processing hardware to perform operations comprising:
determining a first access control band defining a first amount of computing resources of the distributed computing system (paragraph 10, “…access of users maybe based on each …users having dedicated or exclusive access to use the respective …execution capacity…” paragraph 14, “…”dedicated program execution capacity functionality …available for one …user….dedicated amount…guaranteed to be available for execution programs on behalf of the one or more users…” and paragraphs 20-21)
determining a second access control band defining a second amount of computing resources of the distributed computing system, the zone of workloads comprising a plurality of workloads associated with a plurality of different users (paragraphs 12-13 “…on-demand variable program execution capacity that is available to satisfy dynamically received requests of users to execute programs…”, “…on demand variable program execution capacity maybe distinct from ….dedicated program execution capacity …” teaching a separate bucket of capacity for a group of users (instead of single user above), to execute different programs. );
determining, based on an amount of computing resources of the distributed computing system not defined by the first access control band or the second access control band, a third access control band defining a third amount of computing resources of the distributed computing system (paragraph 9, “…excess or otherwise unused program execution capacity ….maybe made available to execute programs on behalf of users on a temporary or non-guaranteed basis….until a time that the program execution capacity is desired for other purposes …” paragraph 13, “…the on-demand…execution capacity maybe distinct from …dedicated program execution capacity and …from the computing nodes used to provide excess program execution capacity….” Teaching this 3rd band is separate and distinct from the other two);
receiving, from the one or more workloads, a request for a particular amount of computing resources of the distributed computing system (paragraph 53, “…request for immediate execution…previously received program execution request that scheduled or otherwise reserve the then-future execution….requests maybe received in various ways, such as directly from a user…or from an executing program of a user that automatically initiates the execution of one or more other programs or other instances…include various information to be used…..such as…resource allocation…geographical and/or logical location for execution…”) ;
based on the requested particular amount of computing resources, determining one or more access control bands from the first access control band, the second access control band, and the third access control band (paragraph 54, “…after receiving a request to execute one or more instances….PES may determine one or more computing nodes to use in executing the program instances….” Paragraph 55, “which computing nodes to use for execution …maybe made in a variety of ways….based on any preferences and/or requirements specified in the request or otherwise specified for….or associated user…” paragraphs 65-70 teaching allocation of resources to the above different bands based on the request (type of request, amount, timing, etc.); and
allocating at least a portion of the requested particular amount of computing resources to the one or more workloads based on the determined one or more access control bands (paragraphs 12, 19, 26, 47-48, and 76-79 teaching various embodiments of allocation of resources in full or in part based on variety of factors.).
While DeSantis teaches tracking historical computing usage and using it for capacity decisions and contemplates demand forecast governing cross-tier sharing (paragraphs 24 and 35). DeSantis does not explicitly teach determining of capacity commitments from historical computing usage.
However, Casillas teaches a known method of computer resource allocation in a distributed network computing environment including determining, based on a first historical computing usage of a distributed computing system by one or more workloads associated with a user, a first access control band defining (paragraph 46 and 63);
determining, based on a second historical computing usage of the distributed computing system by a zone of workloads, (paragraph 46 and 63). This known technique is applicable to the system of DeSantis as they both share characteristics and capabilities, namely, they are directed to computer resource allocation in a networked computing environment based on variety of allocation bands.
One of ordinary skill in the art before the effective filing date of the application would have recognized that applying the known technique of Casillas would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Casillas to the teachings of DeSantis would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such resource allocation features into similar systems. Further, applying use of historical computing usage data by singular workloads and plurality of workloads of multiple users to determine resource allocation band for singular user workload and plurality of workloads of multiple users to determine shared resource allocation band to DeSantis with determining resource allocation band for singular workload/user and determining shared resource allocation bands accordingly, would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more improved resource utilization efficiency across a range of tenants. (Casillas, paragraph 6).
As for claim 6, Casillas also teaches the second historical computing usage of the distributed computing system comprises an aggregate of an average computing usage by each respective workload of the zone of workloads (paragraph 49). Rationale to combine same as claim 1 above.
As for claim 7, DeSantis teaches the first historical computing usage of the distributed computing system comprises at least one of: a peak-to-average usage ratio by the one or more workloads; a size of the one or more workloads; a family indicator of the one or more workloads; or a growth rate of the one or more workloads (paragraphs 11, 53, 50, and 24-25).
Casillas also teaches the same (paragraph 37, 46). Rationale to combine same as claim 1 above.
As for claim 8, DeSantis also teaches the first access control band is associated with a first likelihood that the first amount of computing resources will be available for allocation to the one or more workloads associated with the user; the second access control band is associated with a second likelihood that the second amount of computing resources will be available for allocation to the one or more workloads associated with the user, the second likelihood different from the first likelihood and the third access control band is associated with a third likelihood that the third amount of computing resources will be available for allocation to the one or more workloads associated with the user, the third likelihood different from the first likelihood and the second likelihood (paragraphs 9, 12, 14, 57, “varying degrees of availability, such as guaranteed availability …variable availability….” teaches dedicated guaranteed availability for a first tier, on demand is next level down where it’s not guaranteed and maybe delayed, and third excess band is temporary and non-guaranteed that terminates at any time. These guaranteed, delayed, terminates at any time corresponds to the different likelihood of availability of each band of resources.)
As for claim 9, Desantis also teaches the first likelihood is greater than the second likelihood; and the second likelihood is greater than the third likelihood (paragraphs 14, 12, 9, each is understood as having higher likelihood than the next.).
As for claim 10, DeSantis also teaches availability of the second amount of computing resources for allocation to the one or more workloads is based on a resource usage of the zone of workloads (paragraphs 12, 19, 43-44, 70. On-demand capacity is allocated based on availability, such as if not used by other users. Thus, it is understood as based on resource usage of the zone of workloads).
In addition, Castillas also teaches availability of the second amount of computing resources for allocation to the one or more workloads is based on a resource usage of the zone of workloads (paragraphs 6, 32, 42, granting or denying of request based on availability/capacity of the zone, which is clearly related to resource usage of the zone of workloads in the same band of resources). Rationale to combine same as claim 1 above.
As for claims 11, 16-20, they contain similar limitations as claims 1, 6-10 above respectively. Thus, they are rejected under the same rationales.
Claim(s) 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over DeSantis (US PGPUB 2018/0129628A1), in view of Casillas et al. (EP 3529698 B1), further in view of Rzadca et al. (“Autopilot: workload autoscaling at Google”, EuroSys ’20, April 27, 2020).
As for claim 2, Casillas teaches the use of “consistency or variation of the amount of resources used over time” to determine the priority level/band of resource to allocate to a tenant/group of tenants (paragraph 22), it is well understood variation of amount of resource is a measurement of the differences in resource usage relative to an average amount, and reads upon the claimed limitation. Nevertheless, in the interest of compact prosecution, Examiner will note DeSantis and Casillas do not explicitly use the words “peak to average ratio” even if functionally obvious.
However, Rzadca teaches a known method of resource allocation for workloads including the first historical computing usage of the distributed computing system comprises a peak usage and an average usage of at least one workload associated with the user (Section 3.2.2 – “…Moving Window Recommenders…. the following statistics over s is used to compute the recommendation S[t] at time t: …peak (Smax) returns the maximum from recent samples….average (Savg) computes a time-weighted average of the average signal values….”) This known technique is applicable to the system of DeSantis and Casillas as they both share characteristics and capabilities, namely, they are directed to computer resource allocation in a networked computing environment based on variety of allocation bands.
One of ordinary skill in the art before the effective filing date of the application would have recognized that applying the known technique of Rzadca would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Rzadca to the teachings of DeSantis and Casillas would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such resource allocation features into similar systems. Further, applying peak usage and average usage by workloads to DeSantis and Casillas with resource allocation based on variability of the workload’s resource usage accordingly, would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow improved accuracy and responsiveness to resource demands that reduces resource allocation wastage. (Rzadca, Abstract).
As for claims 12, they are the system claims of claims 2 above. Thus, they are rejected under the same rationales.
Claim(s) 3-4 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over DeSantis (US PGPUB 2018/0129628A1), in view of Casillas et al. (EP 3529698 B1), further in view of Rzadca et al. (“Autopilot: workload autoscaling at Google”, EuroSys ’20, April 27, 2020), further in view of Blanding (US PAT 8156502).
As for claim 3, while DeSantis, Casillas, and Rzadca teaches variability of resource usage based resource allocation to workloads. DeSantis, Casillas, and Rzadca do not explicitly teach variability threshold based resource allocation including a first peak-to-average usage ratio where amount of computing resource is based on average historical amount.
However, Blanding teaching a known method of resource allocation in a distributed network computing environment including determining that the peak-to-average usage ratio satisfies a first threshold; and based on determining that the peak-to-average usage ratio satisfies the first threshold (Claim 7, “…demand types are selected from a set including ….full demand type with relatively low utilization variance…” col. 4, lines 7-15, “…classification deriving statistical parameters that characterize consumption history…average and peak consumption…” and compare …parameter ranges or characteristics for each classification…”);
determining the first amount of computing resources based on an average historical computing usage of the distributed computing system by the one or more workloads over a predetermined amount of time (claims 1, 4, “…classifying said workloads into demand types…planning an allocation of said computing resources to said workloads….as a function of the demand types of said workloads…grouping workloads by demand types to form workload groups to which some resources are collectively allocated…”, col. 4 lines 7-52, col. 5 line 11 -23 teaching allocation based on what each variance type workload needs, i.e., stable variance get stable resource allocation, high variance groups can get additional resources at times of the day with peak requirements. Thus, it’s obvious that those with low variances would get what resource manager determined to be the average because low variance mathematically means low/little deviation from the average and doing so would allow optimal allocation of resources to workload type over time.). This known technique is applicable to the system of DeSantis, Casillas and Rzadca as they both share characteristics and capabilities, namely, they are directed to computer resource allocation in a networked computing environment based on variety of allocation bands.
One of ordinary skill in the art before the effective filing date of the application would have recognized that applying the known technique of Blanding would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Blanding to the teachings of DeSantis, Casillas and Rzadca would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such resource allocation features into similar systems. Further, applying threshold variance based resource allocation to workloads to DeSantis, Casillas and Rzadca with resource allocation based on variability of the workload’s resource usage, and the limit to satisfy the forecasted level of use of the computing resource by workload/entity accordingly, would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow improved automation in resource allocation based on workload characteristics to resource. (Blanding, col. 1 line 51 to col. 2 line 24).
As for claim 4, Blanding also teaches determining that the peak-to-average usage ratio satisfies a second threshold; and based on determining that the peak-to-average usage ratio satisfies the second threshold, determining the first amount of resources further based on a minimum sustained computing usage of the distributed computing system by the one or more workloads over the predetermined amount of time (claims 1, 4, “…classifying said workloads into demand types…planning an allocation of said computing resources to said workloads….as a function of the demand types of said workloads…grouping workloads by demand types to form workload groups to which some resources are collectively allocated…”, col. 4 lines 7-52, col. 5 line 11 -23. Teaching for high variance workloads that it can be allocated different resource amounts based on variance patterns to give workload sufficient resource. Thus, the lowest quantity of resource usage during workload execution that is determined to be allocated is understood as a minimum sustained computing usage as claimed.).
As for claims 13-14, they are the system claims of claims 3-4 above. Thus, they are rejected under the same rationales.
Allowable Subject Matter
Claims 5 and 15 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEVIN X LU whose telephone number is (571)270-1233. The examiner can normally be reached M-F 10am-6pm.
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/KEVIN X LU/Examiner, Art Unit 2199
/LEWIS A BULLOCK JR/Supervisory Patent Examiner, Art Unit 2199
