Context-Aware Request Identification for Fair Serverless Scheduling
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Solution Overview
Problem
Serverless computing platforms face challenges in addressing sophisticated security threats and resource management inefficiencies due to inadequate context-awareness in request identification and resource allocation, leading to suboptimal performance during high demand or resource contention.
Innovation Solution
A multi-layered RequestID System implementing context-aware request identifiers, graduated depth analysis, and business-aligned resource allocation, including UUID generation, machine learning-based validation, and dynamic prioritization to enhance security and fairness in serverless functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If context-aware request identification and business-aligned resource allocation are implemented, then security against sophisticated attacks and resource utilization efficiency are improved, but system complexity increases
Solution Approach 1:
The system segments the request processing into multiple independent layers: request identification layer (generating RequestIDs), validation layer (ML-based fraud detection), admission control layer (business logic enforcement), and resource allocation layer (prioritized scheduling). Each layer operates independently with well-defined interfaces, allowing the complex security and resource management functions to be modularized and managed systematically.
Solution Approach 2:
The patent introduces a RequestID as an intermediary token that carries business context information through the system. This single identifier mediates between multiple system components (security services, resource managers, billing systems), allowing them to make context-aware decisions without requiring direct complex interactions between each other, thus reducing overall system complexity.
2Productivity
If context-aware admission control and prioritized resource allocation are implemented, then resource utilization efficiency and business priority alignment are improved, but processing time and system latency increase
Solution Approach 1:
The system performs preliminary actions by pre-establishing business context, customer tiers, and priority rules before request processing begins. The RequestID is generated with embedded business context information upfront, and admission control policies are pre-configured. This allows the admission control layer to make rapid decisions based on pre-computed business value metrics without performing complex real-time analysis during request processing.
Solution Approach 2:
The system applies partial analysis depth based on request characteristics and business priority levels. High-priority requests receive full context-aware analysis while lower-priority requests undergo simplified processing. The ML validation layer can be selectively applied based on risk assessment, avoiding unnecessary processing time for low-risk requests while maintaining security for critical ones.
3Device complexity
If binary decision models are used for request handling, then system simplicity is maintained, but false positives and false negatives increase reducing security effectiveness
Solution Approach 1:
The patent replaces static binary decision models with dynamic multi-level decision models that adapt to contextual information. The admission control layer evaluates multiple factors including business context, customer tier, request pattern history, and risk scores to determine appropriate response levels. This dynamic approach allows graduated responses (allow, monitor, restrict, block) based on the specific context of each request, improving security effectiveness without requiring overly complex systems.
Data Source
AI summary
The present invention is directed to systems and methods for request identification in serverless functions including checking incoming requests against cached requests, validating, contextualizing, and generating UUIDs for incoming requests, applying policies to incoming requests, making execution decisions and scheduling incoming requests for execution.