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 . This Non-Final Office Action is in reply to communication filed on Receipt Date07/26/2024. Claimed priority is granted from foreign Application Number CN202310982104.6 filed on 08/04/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/30/2024, 03/21/2025, and 09/30/2025 was filed after the mailing date of the original application filed on 07/26/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 are rejected under 35 U.S.C. 103 as being unpatentable over Cao et al., US 20150373071 A1, in view of Shen et al., US 20180285766 A1.
Regarding claim 1, Cao teaches the invention substantially as claimed. Cao discloses a slow node detection method during task running (par. 0009, 0023, 0071; a running streaming application in which performance is monitored and bottleneck operators are detected during execution), comprising: obtaining running indicator information of a streaming data processing task (0067; streams performance monitor uses performance thresholds and metrics to measure performance of the streaming application and individual operators), wherein the streaming data processing task comprises sub-tasks of a plurality of operator types (Streaming applications with multiple operators coupled together in a flow graph, each processing streaming tuples 0004; it later illustrates operators A-J forming different processing portions of the stream graph 0073); determining a target operator type that is abnormal according to a topological relationship between respective operator types and abnormality reason information (0066 illustrates flow graph explicitly defines upstream/downstream relationships among A-J) that is indicated by the running indicator information (0079, high utilization, backed-up buffers, and tuples being dropped because an operator is too busy; 0075 CPU and buffer usage, network utilization as bottleneck conditions ); filtering first sub-tasks of the target operator type from the sub-tasks of the plurality of operator types (0076). Cao identifies underperforming/bottleneck stream-processing functionality but does not clearly distinguish individual slow parallel sub-tasks within a bottleneck operator and distributed multi-node environments, cloud computing nodes, and operators deployed to VMs/cloud resources and recognize bottleneck conditions relating to physical hardware and virtual-machine performance. Cao fails to disclose “...determining, from the running indicator information, to-be-detected indicator information respectively corresponding to the first sub-tasks; and determining, by using the to-be-detected indicator information, a second sub-task that runs slowly from the first sub-tasks, and determining a slow node machine according to an execution machine on which a computing node that executes the second sub-task is located.” This feature is well known in the art, as evidenced by Shen. Shen expressly groups tasks according to execution time and identifies the slowest task cluster as straggler tasks; tasks are labeled based on how much their execution time diverges from the others (0047-0048. It further expressly states that tasks with statistically slower execution times are labeled stragglers (0070). Shen states that performance information is collected for tasks from the host devices preformatting those tasks and includes resource assignments of the respective host device. Shen further uses characteristics of the specific hast device utilized to complete the task when diagnosing stragglers 0038.
Accordingly, It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to apply Shen’s task-level straggler detection and host-device correlation to Cao’s identified bottleneck operator, thereby examining the individual task instances corresponding to the bottleneck operator, thereby examining the individual task instances corresponding to the bottleneck operator determining from their performance indicators which task instance is running slowly, and identifying the execution machine hosting that slow task. A person of ordinary skill would have been motivated to make the combination because Cao seeks to identify and alleviate performance bottlenecks in distributed streaming applications, while Shen teaches a known technique for more precisely diagnosing the particular task and host responsible for poor performance. The combination would predictably provide finer-grained localization of the source of Cao’s detected streaming bottleneck. By this rationale, claim 1 is rejected.
Regarding claims 2, 3, 9, 10, and 18, the combination Cao-Shen teaches:
2. The method according to claim 1, wherein, after determining the slow node machine, the method further comprises: according to resource amounts required for respective target computing nodes used for executing the second sub-task in the slow node machine applying for a target resource for each target computing node in advance by using a service migration interface that is preset; setting shield information for the slow node machine, wherein the shield information is used to indicate that a new sub-task of the target operator type is unable to be scheduled to a computing node in the slow node machine for execution; and creating, by using the target resource that is applied, an alternative computing node used for replacing the target computing node, and executing the second sub-task associated with the target computing node by using the alternative computing node (Cao teaches requesting cloud resources/VMs through a resource request interface and deploying helper operators on the provisioned VMs. See Cao 0065, 0069 and 0071-0074. Shen teaches resource allocation and task scheduling in distributed systems based on sub-tasks... See Shen 0035, 0040-0042).
3. The method according to claim 1, wherein determining the slow node machine according to the execution machine on which the computing node that executes the second sub-task is located comprises: in response to a target quantity of second sub-tasks being greater than a preset quantity, determining, according to execution machines on which computing nodes that execute the second sub-tasks are located, task quantities corresponding to the execution machines; and determining the slow node machine according to first ratios of the task quantities corresponding to the execution machines to the target quantity respectively (Shen, 0038, 0047-0049).
9. A slow node detection apparatus during task running, comprising: an obtaining module, configured to obtain running indicator information of a streaming data processing task, wherein the streaming data processing task comprises sub-tasks of a plurality of operator types; a first determining module, configured to determine a target operator type that is abnormal according to a topological relationship between respective operator types and abnormality reason information that is indicated by the running indicator information; a second determining module, configured to filter first sub-tasks of the target operator type from the sub-tasks of the plurality of operator types, and determine, from the running indicator information, to-be-detected indicator information respectively corresponding to the first sub-tasks; and a third determining module, configured to determine, by using the to-be-detected indicator information, a second sub-task that runs slowly from the first sub-tasks, and determine a slow node machine according to an execution machine on which a computing node that executes the second sub-task is located (Similar to claim 1, see Cao, 0066, 0068, 0071, 0075-0079, and see Shen 0033, 0038, 0047, 0049, 0052-0056, 0070-0071. The same reasons set forth with respect to claim 1, with similar motivation and reason to combine used for the rejection of claim 1 are valid for this claim. By this rationale, claim 9 is rejected).
10. An electronic device, comprising: a processor and a memory, wherein the memory stores a machine-readable instruction that is capable of being executed by the processor, the processor is configured to execute the machine-readable instruction stored in the memory, and when the machine-readable instruction is executed by the processor, the processor performs a slow node detection method during task running, wherein the slow node detection method during task running comprises: obtaining running indicator information of a streaming data processing task, wherein the streaming data processing task comprises sub-tasks of a plurality of operator types; determining a target operator type that is abnormal according to a topological relationship between respective operator types and abnormality reason information that is indicated by the running indicator information; filtering first sub-tasks of the target operator type from the sub-tasks of the plurality of operator types, and determining, from the running indicator information, to-be-detected indicator information respectively corresponding to the first sub-tasks; and determining, by using the to-be-detected indicator information, a second sub-task that runs slowly from the first sub-tasks, and determining a slow node machine according to an execution machine on which a computing node that executes the second sub-task is located (See Cao, 0045, 0049, together with 0066-0068, 0071, 0075-0079; see Shen 0036-0038, 0072-0076).
18. A non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores a computer program, and when the computer program is run by an electronic device, the electronic device performs the slow node detection method during task running according to claim 1 (See Cao, 0057-0063; Shen similarly implements its straggler-task analysis through machine-executable instructions and machine accessible storage media).
Allowable Subject Matter
Claims 4-8, 11-17, 19 and 20 are 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
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/JUDE JEAN GILLES/Primary Examiner, Art Unit 2459
September 25, 2026
