DETAILED ACTION
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 .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Doddaiah et al. 20220326865 herein Doddaiah.
Per claim 1, Doddaiah discloses: receiving a plurality of read requests from at least one of a host and an internal process of the storage device; (fig. 4, ¶0051; the QoS processor 270 can include a QoS controller 350 that can identify patterns related to matching IO sequence storage tier relationships.) determining a first workload pattern for the storage device based on characteristics of the plurality of read requests, wherein the characteristics include at least one of a request frequency, an access locality, and a data size; identifying a data placement technique used by the storage device and one or more workload patterns associated with the data placement technique; (¶0051; Further, the QoS controller 350 can correlate the matching storage tier relationship patterns with IO workload patterns identified by the workload analyzer 250. For example, the QoS controller can use, e.g., a machine learning (ML) engine configured to perform, e.g., one or more self-learning techniques such as a recursive learning technique.) comparing the first workload pattern with the one or more workload patterns to determine whether the first workload pattern matches with the one or more workload patterns; (¶0051; The ML engine can use one or more of the self-learning techniques to identify the matching IO sequence storage tier patterns and their corresponding correlations with IO workload patterns.) and based on determining a mismatch between the first workload pattern and the one or more workload patterns, obtaining a second data placement technique by dynamically modifying the data placement technique of the storage device to improve the QoS (¶0051; Based on the ML engine's output, the QoS controller 350 can dynamically generate QoS policies 325a-c that consider QoS relationships between the array's storage resources and current and/or anticipated IO workloads).
Per claim 2, Doddaiah discloses: wherein the dynamically modifying the data placement technique of the storage device comprises: identifying an optimal data placement technique for the storage device by comparing the first workload pattern with a reference table, ((¶0051; Further, the QoS controller 350 can correlate the matching storage tier relationship patterns with IO workload patterns identified by the workload analyzer 250. For example, the QoS controller can use, e.g., a machine learning (ML) engine configured to perform, e.g., one or more self-learning techniques such as a recursive learning technique.) and wherein the reference table comprises a plurality of data placement techniques and one or more respective workload patterns corresponding to the plurality of data placement techniques (fig. 3, ¶0050; In embodiments, the QoS manager 360 can include storage QoS demotion policies, promotion policies, and static policies 325a-c. The QoS manager 360 can predefine the policies 325a-c based on the array's configuration and a storage vendor-client service level agreement (SLA). For example, the manager 360 can read the array's config file that defines its configuration. Additionally, the manager 360 can parse anticipated IO workload information and characteristics from the SLA. In embodiments, the policies 325a-c can include instructions that the QoS controller 350 can execute to perform QoS updates).
Per claim 3, Doddaiah discloses: further comprising at least one of: performing subsequent write operations in the storage device based on the second data placement technique; and modifying storage of existing data by relocating data blocks in the storage device based on the second data placement technique (fig. 3, ¶0055; if the tracks can fulfill Silver QoS service level requirements, they would have a delta step value of −1 and satisfy the promotion deduplication relationship requirement. Accordingly, the QoS processor 270 can then relocate the target tracks with performance capabilities to tracks that match the source track's capabilities).
Per claim 4, Doddaiah discloses: wherein the plurality of read requests comprises at least one of host read requests and internal read requests of the storage device (¶0027; the hosts 115a-n can include host-operated applications. The host-operated applications can generate data for the array 105 to store and/or read data the array 105 stores. The hosts 114a-n can assign different levels of business importance to data types they generate or read. As such, each SLO can define a service level (SL) for each data type the hosts 114a-n write to and/or read from the array 105.).
Per claim 5, Doddaiah discloses: wherein the reference table is generated by monitoring performance of the storage device for a plurality of workload patterns and possible combinations of the data placement technique during initial boot up of the storage device (fig. 1, ¶0038; For example, the analyzer 250 can include logic and/or circuitry configured to analyze the one or more IO workload 207 received by the HA 121. The analysis can include identifying one or more characteristics of each IO of the workload 207. For example, each IO can include metadata including information associated with an IO type, data track related to the data involved with each IO, time, performance metrics, and telemetry data, and the like. Based on historical and/or current IO characteristic data, the analyzer 250 can identify IO patterns using, e.g., one or more machine learning (ML) techniques. Using the identified IO patterns, the analyzer 250 can determine whether the array 105 is experiencing an intensive IO workload.; the examiner notes that the initial boot is merely loading the configuration data).
Per claim 6, Doddaiah discloses: dynamically modifying the data placement technique of the storage device by the host, based on the first workload pattern ((¶0051; Based on the ML engine's output, the QoS controller 350 can dynamically generate QoS policies 325a-c that consider QoS relationships between the array's storage resources and current and/or anticipated IO workloads).
Claims 7-12 are the apparatus claims corresponding to the method claims 1-6 and are rejected under the same reasons set forth in connection with the rejection of claims 1-6. The examiner notes that Doddaiah teaches a QOS processor.
Claims 13-18 are the CRM claims corresponding to the method claims 1-6 and are rejected under the same reasons set forth in connection with the rejection of claims 1-6. The examiner notes that Doddaiah teaches a QOS processor.
Remark
Examiner respectfully requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist Examiner in prosecuting the application.
Conclusion
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BABOUCARR . FAAL
Primary Examiner
Art Unit 2138
/BABOUCARR FAAL/Primary Examiner, Art Unit 2138
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