Serverless Processing Unit Routing for Cloud Job Optimization
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Solution Overview
Problem
Serverless cloud computing architectures face challenges in handling heavy processing jobs due to limitations in execution time and high costs associated with stronger processing units, while server computing machines struggle with scalability and response time.
Innovation Solution
A system and method for optimizing serverless cloud processing unit selection based on data specifications and processing constraints, generating a routing table to map jobs to the cheapest available processing units that can successfully complete tasks within given constraints, thereby reducing costs and improving processing times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If serverless cloud processing units are used for heavy processing jobs, then scalability and response time are improved, but processing time may exceed execution time limits and costs increase
Solution Approach 1:
The system dynamically changes processing parameters by selecting different serverless processing units based on job characteristics. The routing table maps job types to optimal processing units with appropriate execution time limits, allowing the system to adapt parameter settings (processing unit selection) to match job requirements while maintaining scalability
Solution Approach 2:
The system implements dynamic resource allocation through a routing table that automatically assigns jobs to appropriate serverless processing units based on real-time job characteristics and constraints. This dynamic matching allows the system to optimize between execution time limits and scalability for each individual job rather than using static allocation
2Speed
If stronger serverless processing units are used, then response time is improved, but costs increase significantly
Solution Approach 1:
The system applies local quality by matching specific job requirements with appropriately sized processing units. Instead of using uniformly strong processing units for all jobs, the routing table enables each job to be processed by a processing unit with just the right level of power needed, optimizing the local match between job demands and resource capabilities to reduce costs while maintaining response time
Solution Approach 2:
The system utilizes cheaper serverless processing units with shorter execution time limits for appropriate jobs. The routing table identifies when less powerful, more cost-effective processing units can successfully complete jobs within their execution time constraints, replacing the need to always use expensive high-performance units
3Duration of action of moving object
If server computing machines are used for heavy processing jobs, then execution time is reduced, but scalability and response time deteriorate
Solution Approach 1:
The system segments the processing workload by dividing jobs into categories that can be routed to different types of processing units. The routing table creates distinct pathways for different job types, allowing server computing machines to handle jobs requiring long processing times while serverless units handle jobs needing fast scalability, thus segmenting the system to resolve the contradiction between processing time and scalability
Data Source
AI summary
A system is provided for optimized selection of a plurality of processing units for resource intensive processing operations. The system includes a processor and a computer readable medium operably coupled thereto, to perform the scheduling operations which include receiving a processing operation for a data input that requires processing in a computing environment, determining at least one constraint requirement imposed on performing the processing operation that are all required to be fulfilled for successful completion of the processing operation, accessing a routing table associated with the computing environment, determining one of the plurality of processing units from the routing table based on fulfilling all of the at least one constraint requirement, and assigning the processing operation to the one of the plurality of processing units on the least costly basis or other optimization consideration. The processing units are serverless in a preferred embodiment.