Global Process Consolidation for Networked Service Bandwidth Reduction
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Solution Overview
Problem
Data science models in networked services require large amounts of data, leading to CPU- and memory-heavy tasks such as metadata generation and data loading, which result in significant processing bandwidth loss due to duplication across multiple instances, affecting performance and throughput.
Innovation Solution
Implementing a global process that executes common computation tasks once for multiple instances, reducing duplication by identifying and replacing individual task instances with a global task that generates a shared data set, accessible via fast storage, thereby reducing processing and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If individual instances of common computation tasks are deployed for each user to support multiple users, then service coverage and user support are improved, but processing bandwidth consumption and resource usage increase significantly due to duplication
Solution Approach 1:
The patent merges multiple individual task instances into a single global task that serves all users. The global task consolidates common computation operations (metadata generation, data loading) that were previously executed separately by each user's microservice instance, thereby reducing processing bandwidth consumption while maintaining service coverage for multiple users.
Solution Approach 2:
The global task is designed to perform universal functions for all users simultaneously. Instead of having specialized individual instances for each user, a single global task instance provides multi-functional support by generating datasets that are shared across all user accounts, reducing resource usage while maintaining adaptability.
2Productivity
If multiple instances of microservices are deployed to support tens, hundreds, or thousands of users, then user support capacity is improved, but processing bandwidth and memory storage capacity are lost due to duplication of effort
Solution Approach 1:
The patent combines multiple microservice instances into a single global task that handles common computation operations. By merging duplicate efforts in metadata generation, data loading, and dataset creation, the system maintains high user support capacity while eliminating processing bandwidth loss associated with running multiple separate instances.
3Adaptability or versatility
If individual task instances are used for each user account, then customization and user-specific processing are improved, but resource utilization decreases due to redundant computation
Solution Approach 1:
The global task provides universal processing capability that serves all user accounts through shared datasets. While individual customization is reduced, the system achieves better resource utilization by having one global task perform computations that benefit all users, rather than each user having dedicated instances performing redundant work.
Data Source
AI summary
Techniques are disclosed relating to a computer system identifying usage of a plurality of individual instances of a common computation task by a plurality of users of a networked service. These individual instances of the common computation task may generate a respective data set. Techniques also include creating, by the computer system, a global process to perform the common computation task. Execution of the global process may include generation of a global data set that includes at least portions of the respective data sets. Additionally, techniques include modifying, by the computer system, respective accounts of a subset of the plurality of users to use the global process in place of using a respective instance of the common computation task, as well as providing, by the computer system, the global data set generated by the global process to the respective accounts of the subset of users.