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 .
Claims 1-20 are pending.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The disclosure is objected to because of the following informalities:
-- different drawings e.g. fig. 1A and 1B are different but are described as same -- in [0004].
-- different drawings e.g. fig. 2A-2C are different but are described as same -- in [0005].
Appropriate correction is required.
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.
Claims 1-20 are rejected under 35 U.S.C. 112 (b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or joint inventor regards as the invention.
The following claim language is not clearly understood:
Claim 1 recites “obtaining, …, using the container instance and the telemetry data, a prediction”. It is unclear how the container instance being used for making the prediction or impact of container instance in making prediction of the future state”.
Claim 7 recites “reducing used computing resources of the data processing system and by the container instance when least condition is met from a set of condition”. It is unclear computing resources is reduced of the data processing system and of the container instance or reducing the resource used by the container of the data processing system or resource of data processing system allocated to the container instance is reduced.
Claim 7 recites “when at least condition is met from the set of conditions”. It is unclear if the “at least condition” is referring to at least one condition …of set of conditions or lowest value of a condition of a set of conditions.
Claims 9 and 15 recite elements of claim 1 and have similar deficiency as claim 1. Therefore, they are rejected for the same rational. Remaining dependent claims 2-8, 10-14 and 16-20 are also rejected due to similar deficiency inherited from the rejected independent claims.
Claim Rejections - 35 USC § 103
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 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wen et al. (US 2020/0027014 A1, hereafter Wen) in view of Tarocchi et al. (US 2023/0032901 A1, hereafter Tarocchi).
As per claim 1, Wen teaches the invention substantially as claimed including a method for managing operation of a data processing system that hosts virtual machines that contribute to computer implemented services provided by the data processing system (fig. 6 Node 652 virtual machine / containers 658 [0090] applications / services), the method comprising:
obtaining, by the data processing system, telemetry data from the virtual machines ([0042] taking usage data measurement e.g. storage/ i/o / other resource usage data 226 227, retrieving usage data 228 [0052] Usage data 225 comprises data that represents the usage of the resources of the system or systems to be evaluated);
identifying, by the data processing system and the telemetry data, a forecasting process of multiple forecasting processes that may be performed to predict a future state of the data processing system ([0042] prediction and runway engine, usage data measurements, capturing system data, organizing retrieved data, evaluating and training prediction models, selecting one of many candidate prediction models; generating predictions based on forecasts arising from the selected prediction models; future time period when the current provisioning of a resource is predicted to be demanded to its capacity [0043] retrieves usage data, observations of specific resources uses over a historical time frame, historical observation);
obtaining, by the data processing system and from a remote entity, a container image based on the forecasting process ([0065] calculate runways using the time-oriented trends 306 resources that are predicted to fall short of demand, suggesting / necessitating the purchase or acquisition of more capacity );
obtaining, by the data processing system and using the container image, a container instance hosted by the data processing system ([0128] runnable, executable container instance, container image);
obtaining, by the data processing system and using the container instance and the telemetry data, a prediction for a future operating state of the data processing system ([0036] fig. 1B historical observations, make predictions, usage measurements of a particular resources [0037] predictions, extend, into the future [0030] distributed resources, resource, executable container; fig. 6 Node 652 virtual machine / containers 658);
updating, by the data processing system, operation of the data processing system based on the prediction for the future operating state to obtain an updated data processing system ([0069] fig. 3 generate usage predictions 304 calculate runways using the time oriented trends 306 generate recommendations 308 recommendation, accepted, effect on virtualized system 309 [0107] fig. 7B recommendation is adopted, effect of the addition of configured resources can be reflected in the runway view 221 0003] computing as a service [0061] distributed resources, e.g. email hosting services [0090] applications or services); and
providing, by the updated data processing system, the computer implemented services ([0069] recommendation accepted [0107] recommendation adopted [0003] computing as a service [0061] distributed resources, e.g. email hosting services [0090] applications or services ).
Wen doesn’t specifically teach obtaining from a remote entity, a container image.
Tarocchi, however, teaches obtaining from a remote entity, a container image ([0013] fetch images from a remote repository ).
It would have been obvious to one of ordinary skills in the art before the effective filing date of the invention was made to combine the teachings of Wen with the teachings of Tarocchi of fetching image from a remote repository to improve efficiency and allow obtaining from a remote entity, a container image to the method of Wen as in the instant invention.
The combination of the cited prior art would have been obvious because applying the known method of fetching image from a remote repository taught by Tarocchi to the method of Wen to yield expected result and improved reliability, scalability and independence as in the instant invention.
As per claim 2, Wen teaches wherein identifying the forecasting process of the multiple forecasting processes comprises ([0042] selecting one of many candidate prediction models):
obtaining at least one piece of information from a list of pieces of information consisting of:
an enumeration of the telemetry data, a desired type of the future state prediction for the data processing system, and available computing resources of the data processing system ([0042] usage data measurements, capturing system data e.g. system and configuration data [0058] determine best models to use to predict future usage based on past time window ); and
selecting, based on the at least one piece of information, the forecasting process to predict the future state of the data processing system ([0034] uses historical observation to determine an applicable prediction model, recommend and forecast ).
As per claim 3, Wen teaches wherein the forecasting process uses a predictive model that ingests the telemetry data and uses the available computing resources to predict the future state of the data processing system ([0034] use historical observations, recommend and forecast [0036] making predictions, historical observations, usage measurement, resource [0037] predictions extend into future [0005] total amount of available disk space, generate prediction based on the total utilization of that space).
As per claim 4, Wen teaches wherein the multiple forecasting processes use predictive models that ingest different input data, operate using different amounts of the available computing resources, and generate different types of the future state predictions ([0042] taking usage data, capturing system data, organizing retrieved data, evaluating and training predictions models, generating predictions [0043] retrieved usage data. historical observation [0008] amount of data to be analyzed [0058] predict future usage based on past time window data [0005] total amount of available disk space, generate predictions based on the total utilizations of that space [0062] future usage predictions, available capacities, time-oriented runway can be calculated, seasonally adjusted predictions can be made for individual users [0086] predictions, runways, resource planning [0099] prediction, cluster, over-provisioned, under provisioned).
As per claim 5, Wen teaches wherein the future state comprises at least one state selected from a group of states consisting of a future health state, a future security state, and a future resource availability state (fig. 1B resource usage demand amount 139 corresponding to the prediction [0062] given predictions and quota limits or available capacities, time oriented runway can be calculated).
As per claim 6, Tarocchi teaches wherein the remote entity is a cloud system that hosts the container images ([0014] remote repository, networking infrastructure [0015] container image repository [0018] fig. 1 image remote server 122 communication network 120 public / private network).
As per claim 7, Wen teaches wherein updating operation of the data processing system comprises:
reducing used computing resources of the data processing system and by the container instance when at least condition is met from a set of conditions consisting of ([0048] recommend migration of infrastructure and workloads i.e. reducing resource usage [0055] sizing unit, determine recommendations for hardware and software changes, increase the available runway [0099] migration of resources, swapping one node/component, increase one while decreasing another node capability ):
selection of an action to update the operation of the data processing system is selected ([0279] making provisioning recommendations [0048] recommend migration of infrastructure and workloads i.e. reducing resource usage [0099] migration of resources, swapping one node/component, increase one while decreasing another node capability);
performance of the action to update the operation of the data processing system ( [0048] recommend migration of infrastructure and workloads i.e. reducing resource usage [0099] migration of resources, swapping one node/component, increase one while decreasing another node capability);
operation of the container instance enters an idle state after generating the prediction; and
available computing resources of the data processing system fall below a threshold amount ([0062] predictions and quota limits or available capacities, time-oriented runway can be calculated [0074] quota, resources, utilization, subjected to allocations or limits [0042] recommend hardware or software changes [0081] decrease in actually available resources [0085] not further decrease the available storage space of the storage pool, space reserved prior to the actual usage).
As per claim 8, Wen teaches wherein reducing the use of the computing resource ([0042] recommend hardware or software changes [0081] decrease in actually available resources).
Tarocchi teaches remaining claim elements of terminating operation of the container instance; and deallocating computing resource committed to the container instance ([0036] terminate, the execution of container instance i.e. allocated resources are released).
Claim 9 recites non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for managing operation of a data processing system that hosts virtual machines that contribute to computer implemented services provided by the data processing system, the operation comprising elements similar to claim 1. Therefore, it is rejected for the same rationale.
Claim 10 recites elements similar to claim 2. Therefore, it is rejected for the same rationale.
Claim 11 recites elements similar to claim 3. Therefore, it is rejected for the same rationale.
Claim 12 recites elements similar to claim 4. Therefore, it is rejected for the same rationale.
Claim 13 recites elements similar to claim 5. Therefore, it is rejected for the same rationale.
Claim 14 recites elements similar to claim 6. Therefore, it is rejected for the same rationale.
Claim 15 recites data processing system, comprising: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations similar to claim 1. Therefore, it is rejected for the same rationale.
Claim 16 recites data processing system for elements similar to claim 2. Therefore, it is rejected for the same rationale.
Claim 17 recites data processing system for elements similar to claim 3. Therefore, it is rejected for the same rationale.
Claim 18 recites the data processing system for elements similar to claim 4. Therefore, it is rejected for the same rationale.
Claim 19 recites the data processing system for elements similar to claim 5. Therefore, it is rejected for the same rationale.
Claim 20 recites the data processing system for elements similar to claim 6. Therefore, it is rejected for the same rationale.
Examiners Note
Applicant is further reminded of that the cited paragraphs and in the references as applied to the claims above for the convenience of the applicant(s) and although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider all of the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Al Hatib et al. (US 2018/0025290 A1) teaches predictive risk model optimization
Bhalla et al. (US 2015/0135012 A1) teaches network node failure predictive system
Cortez et al. (US 2021/0216355 A1) teaches minimizing impact of migrating virtual services
Dettori et al. (US 2018/0039524 A1) teaches predictive layer pre-provisioning in container based virtualization.
Khanna et al. (US 2022/0166670 A1) teaches predicting usage pattern of serverless environment via machine learning
Krishnan et al. (US 2022/0027744 A1) teaches resource data modeling, forecasting and simulation
Srinivasan et al. (US 2018/030229 A1) teaches comparative multi-forecasting analytics service stack for cloud computing resource allocation
Tootaghaj et al. (US 2021/0184942 A1) teaches proactively accommodating predicted future serverless workloads using a machine learning prediction model.
Authorization for Internet Communication
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/ABU ZAR GHAFFARI/Primary Examiner, Art Unit 2195
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