RPC Data Enhancements for Low-Memory Distributed Processing
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Solution Overview
Problem
RPC frameworks face challenges in efficiently handling large data sets, leading to high demands on volatile memory and resource contention, particularly in distributed systems with varying workloads and engines.
Innovation Solution
An application management system with an enhancements server and engines that apply data enhancements based on queues, utilizing machine learning models to identify triggers and apply enhancements dynamically, optimizing workload distribution and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If gRPC handles large data sets, then data processing capability is improved, but volatile memory demand increases
Solution Approach 1:
The patent segments large data sets into smaller batches that are processed in discrete steps. The RPC framework divides incoming data into manageable chunks, processes them individually or in small groups, and releases memory promptly. This segmentation allows the system to handle large overall data volumes while maintaining low peak memory usage, as each segment is processed and discarded sequentially rather than loading everything into memory simultaneously.
Solution Approach 2:
The patent applies preliminary filtering and preprocessing actions to data before it enters the main processing pipeline. Enhancement triggers identify which data items require full processing, allowing the system to skip detailed processing for items that don't meet criteria. This preliminary action reduces the volume of data that needs to be held in volatile memory during intensive processing operations.
2Adaptability or versatility
If RPC frameworks process varying workloads across multiple engines, then system adaptability is improved, but resource contention increases
Solution Approach 1:
The patent implements dynamic workload distribution where the system continuously monitors engine availability, current load, and performance metrics. Task allocation is not static but adapts in real-time based on system state. When engines become available or less loaded, more tasks are assigned; when engines are overloaded, task assignment is reduced. This dynamic approach maintains high adaptability while reducing resource contention through responsive load balancing.
Solution Approach 2:
The patent incorporates feedback mechanisms where engines report their status, performance metrics, and resource utilization back to the RPC framework. This feedback loop enables the system to adjust task allocation based on actual engine conditions rather than assumptions. The framework uses this feedback to optimize workload distribution, assigning tasks to engines that can handle them efficiently while avoiding overloading any single engine, thereby reducing resource contention.
3Measurement precision
If data enhancements are applied dynamically based on machine learning triggers, then processing precision is improved, but computational overhead increases
Solution Approach 1:
The patent applies partial processing by using machine learning models to identify only those data items that warrant full enhancement processing. Rather than applying enhancements to all data uniformly, the system uses triggers to select a subset of items that meet specific criteria. This partial action approach maintains high precision for relevant items while reducing overall computational overhead by skipping unnecessary processing for items that don't require enhancements.
Solution Approach 2:
The patent changes processing parameters dynamically based on data characteristics. The machine learning model analyzes data parameters and determines whether enhancement processing is warranted, adjusting the processing intensity accordingly. For items that trigger enhancement conditions, full processing is applied; for others, minimal or no processing occurs. This parameter-based differentiation maintains precision where needed while reducing computational overhead overall.
Data Source
AI summary
An application management system using Remote Procedure Call (RPC) frameworks can include enhancements. These enhancements can be used on files that are distributed to engines throughout the RPC framework. Enhancements can be stored in an enhancement server. The enhancements can include password detection, logo detection, cleartext detection, or other checks or modifications that may be useful to a variety of engines in the RPC framework. The enhancements can be used to ensure that unencrypted sensitive data, passwords, or files with confidential information (as indicated by logos or other pictorial markings) are not distributed throughout the RPC framework.