Serverless Workflow Platform Automating Cloud Resource Allocation
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Solution Overview
Problem
Current workflow management systems in cloud-based infrastructures require manual configuration of cloud resources, leading to inefficiencies such as increased costs, slower runtime, and execution errors, especially in serverless computing environments where users lack control over provisioning and maintenance of computing infrastructure.
Innovation Solution
A serverless workflow enablement and execution platform (SWEEP) that utilizes machine learning and optimization techniques to select the appropriate cloud service providers, allocate resources, and manage task execution configurations, including timing and resource allocation, based on user inputs and historical data to optimize workflow execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual configuration of cloud resources is used, then users have control over resource provisioning, but system complexity increases and execution efficiency decreases
Solution Approach 1:
The system performs self-service by automatically selecting cloud service providers and configuring resources for workflow tasks without requiring manual user intervention. The automated resource provisioning system analyzes task requirements and independently makes deployment decisions, eliminating the complexity of manual configuration while maintaining execution control.
Solution Approach 2:
An automated resource provisioning system acts as an intermediary between the workflow execution engine and cloud service providers. This intermediary layer handles the complexity of resource configuration and CSP selection, shielding users from complexity while enabling efficient automated resource allocation and workflow execution.
2Reliability
If multiple cloud service providers are used, then system reliability improves, but resource allocation complexity increases
Solution Approach 1:
The automated resource provisioning system provides universal functionality by working with multiple cloud service providers through a unified interface. It manages diverse cloud resources (IaaS, PaaS, SaaS, serverless) through common processes, enabling multi-cloud deployment without proportionally increasing management complexity.
Solution Approach 2:
The system dynamically changes parameters such as CSP selection, resource allocation, and configuration based on workflow requirements and provider availability. This parameter adaptation enables reliable multi-cloud execution by automatically adjusting to optimal configurations without requiring complex manual management of each cloud environment.
3Speed
If automated resource provisioning is implemented, then execution speed improves, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring resource provisioning rules and CSP selection criteria before workflow execution. This advance preparation enables rapid automated decision-making during execution, improving speed while containing complexity through pre-established automation logic rather than complex real-time processing.
Data Source
AI summary
The present disclosure provides computing systems and methods that optimize the execution of workflows that include computational tasks (e.g., which may take the form of functions or containers). In general, the proposed systems and methods can be referred as to or embodied within a serverless workflow enablement and execution platform (also referred to herein as a workflow management system). The serverless workflow platform can facilitate performance of a large-scale computational workflow. In particular, the serverless workflow platform can facilitate performance of serverless workflows that are executed on serverless execution platforms.