Serverless Task Conversion to Distributed Computing Framework
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Solution Overview
Problem
Serverless computing frameworks face performance and reliability issues with data-intensive tasks, particularly in streaming scenarios and complex applications, leading to increased processing time and resource consumption, which can compromise service levels and efficiency.
Innovation Solution
Converting selected serverless application tasks into a Distributed Computing Framework (DCF) format to optimize performance, based on latency and throughput metrics, allowing for automatic transformation without user intervention and leveraging computational efficiencies of DCF workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If serverless computing frameworks are used for data-intensive tasks, then ease of operation and scalability are improved, but processing time and resource consumption increase
Solution Approach 1:
The patent segments serverless tasks by identifying data-intensive tasks that require DCF conversion and separating them from regular serverless tasks. The system automatically detects and converts only the necessary tasks to DCF format, while leaving other tasks in the serverless environment, thus resolving the contradiction by applying different processing approaches to different task types.
Solution Approach 2:
The patent changes the execution parameter of selected tasks from serverless computing mode to distributed computing framework mode. By dynamically adjusting the computing paradigm parameter based on task characteristics (data-intensive vs. regular tasks), the system optimizes processing time for specific task types while maintaining overall ease of operation.
2Adaptability or versatility
If serverless computing frameworks are used for data-intensive tasks, then scalability is improved, but reliability deteriorates
Solution Approach 1:
The system segments tasks based on their reliability requirements and computational characteristics. Data-intensive tasks are automatically identified and converted to DCF format which provides better reliability, while regular tasks continue to run in the serverless environment. This segmentation allows the system to maintain scalability while improving reliability for specific task types.
Solution Approach 2:
The patent introduces an intermediary conversion mechanism that translates serverless tasks into DCF format when needed. This intermediary layer acts as a bridge between the serverless computing model and distributed computing framework, allowing tasks to leverage the reliability of DCF when processing data-intensive workloads while maintaining the scalability of serverless computing.
3Ease of operation
If serverless computing frameworks are used for complex applications, then ease of operation is improved, but service levels are compromised
Solution Approach 1:
The system dynamically changes the computing execution parameter for complex applications by detecting data-intensive task characteristics and converting them to DCF format. This parameter change enables the system to maintain ease of operation for developers while ensuring service level requirements are met through optimized processing of complex data-intensive tasks.
4Productivity
If DCF conversion is applied to serverless tasks, then performance is improved, but device complexity increases
Solution Approach 1:
The patent implements a self-service automated detection and conversion system that identifies data-intensive tasks and converts them to DCF format without requiring user intervention. The system automatically manages the complexity of DCF conversion by handling task analysis, format conversion, and execution orchestration internally, thus improving performance while masking the underlying device complexity from users.
Solution Approach 2:
The system introduces an intermediary automated conversion layer that handles the complexity of DCF integration. This intermediary component manages the translation between serverless and DCF formats, handling the device complexity internally while presenting a simplified interface to users and maintaining high performance through optimized task execution.
Data Source
AI summary
Aspects of the technology provide improvements to a Serverless Computing (SLC) workflow by determining when and how to optimize SLC jobs for computing in a Distributed Computing Framework (DCF). DCF optimization can be performed by abstracting SLC tasks into different workflow configurations to determined optimal arrangements for execution in a DCF environment. A process of the technology can include steps for receiving an SLC job including one or more SLC tasks, executing one or more of the tasks to determine a latency metric and a throughput metric for the SLC tasks, and determining if the SLC tasks should be converted to a Distributed Computing Framework (DCF) format based on the latency metric and the throughput metric. Systems and machine-readable media are also provided.