Stream Processing Management Node for Multi-Cloud Resource Allocation

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Solution Overview

Problem

Existing stream computing systems are limited to single environments, making it challenging to optimize cost, performance, and reliability when deploying workloads across multiple cloud environments, and fail to efficiently manage and distribute stream processing jobs across different geographical regions.

Innovation Solution

A method and system that uses a stream processing management node to establish data communication with multiple stream processing instances across various cloud environments, distribute processing units, receive processing results, and perform machine learning-based stream management operations to optimize job distribution and resource allocation, ensuring cost-effectiveness, performance, and reliability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If stream computing systems are deployed across multiple cloud environments, then reliability and data sovereignty are improved, but system complexity and management difficulty increase

Engineering Contradiction:

Improvesystem reliabilityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system segments stream processing jobs into multiple processing units that can be independently distributed across different cloud environments. Each processing unit operates autonomously on specific data streams, allowing the system to achieve multi-environment deployment without managing complex interdependencies between all components.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

A management node is introduced as an intermediary between the stream processing jobs and multiple cloud environments. This mediator handles the complexity of distribution, monitoring, and coordination across environments, shielding users from the underlying system complexity while maintaining reliability through diversified deployment.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Speed

If processing units are distributed across multiple cloud environments, then performance and latency are improved, but resource allocation efficiency deteriorates

Engineering Contradiction:

Improveprocessing latencyVSAvoidresource allocation efficiency

Core Design Contradiction:

SpeedVSProductivity

Solution Approach 1:

The system dynamically allocates processing units to cloud environments based on real-time performance metrics, workload characteristics, and resource availability. The management node continuously monitors processing latency and resource utilization, adjusting the distribution of processing units to optimize both speed and resource efficiency adaptively.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes allocation parameters such as the number of processing units per environment, data partitioning strategies, and communication frequencies based on observed performance. By adjusting these parameters dynamically, the system achieves low latency processing while maintaining efficient resource utilization across heterogeneous cloud environments.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If machine learning is used for stream management operations, then cost optimization and performance tuning are improved, but computational overhead and system complexity increase

Engineering Contradiction:

Improvecost optimizationVSAvoidcomputational overhead

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

Machine learning models are trained in advance using historical stream processing data to learn optimal resource allocation patterns, cost-effective cloud environment selections, and performance tuning parameters. This preliminary training allows the system to make rapid, informed decisions during runtime without requiring complex real-time computations, thus optimizing cost while limiting computational overhead.

Inventive Principle:

Principle #10Preliminary action

4Reliability

If stream processing jobs are distributed across geographical regions, then data sovereignty and reliability are improved, but communication overhead and coordination complexity increase

Engineering Contradiction:

Improvedata sovereigntyVSAvoidcommunication overhead

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system assigns different data streams and processing units to specific geographical regions based on data sovereignty requirements and local processing capabilities. Each region processes data locally with minimal external communication, reducing coordination overhead while maintaining compliance with data sovereignty regulations through region-specific processing policies.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS12079659B2Selection of stream management operations based on machine learning in a distributed computing environment

Publication Date: 2024.09.03 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Computer software that causes a stream processing management node to perform the following operations: (i) establishing data communication between the stream processing management node and a plurality of stream processing instances executing on respective computing environments in a multi-environment computing system; (ii) distributing one or more processing units of a stream processing job to a first set of stream processing instances of the plurality of stream processing instances; (iii) receiving, from the one or more stream processing instances of the first set of stream processing instances, processing results associated with the one or more processing units of the stream processing job; and (iv) performing a machine learning based stream management operation based, at least in part, on the received processing results.