Query Routing by Computation Module Type for Secure Resource Allocation
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Solution Overview
Problem
Existing load balancers in multi-computer datacenters lack domain knowledge to interpret queries effectively, leading to inefficiencies in caching and increased risk due to user-defined code, without considering historical information for compute resource allocation.
Innovation Solution
A resource allocation system that identifies and routes queries to appropriate computation modules based on computation module type data, filters queries by trust level, and manages lifecycle and scaling of computation modules for different types of client applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If generic load balancers are used to route queries, then device complexity is reduced, but query interpretation capability and caching efficiency deteriorate due to lack of domain knowledge
Solution Approach 1:
The patent introduces a query router as an intermediary component between the load balancer and compute resources. This router possesses domain knowledge about queries and can effectively interpret them to identify similarity, enabling efficient caching while maintaining simple generic load balancers. The router acts as a specialized mediator that bridges the gap between generic routing and domain-specific query understanding.
2Adaptability or versatility
If user-defined code is allowed in compute requests, then query flexibility and functionality are improved, but system security and reliability deteriorate due to introduced risks
Solution Approach 1:
The patent segments the compute request processing into distinct components: a query router that handles routing decisions, a compute resource management layer that executes user-defined code, and an isolation layer that separates user code execution from the core system. This segmentation allows user-defined code to enhance query functionality while containing security risks within isolated execution environments.
Solution Approach 2:
The system implements prior cushioning through pre-established security measures including query validation, compute resource isolation mechanisms, and risk assessment protocols before user-defined code executes. These preventive measures are built into the architecture to cushion against potential security threats while allowing functional flexibility.
3Productivity
If historical information is collected and considered for routing, then query routing accuracy and caching effectiveness are improved, but system complexity and data processing overhead increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and indexing query characteristics and historical execution data before routing decisions are needed. The query router maintains historical information in an optimized format, allowing fast retrieval and comparison during routing without adding significant complexity to the real-time decision-making process.
4Device complexity
If a single-type query processing service is used, then system simplicity is maintained, but adaptability to different client application types deteriorates
Solution Approach 1:
The patent implements universality through a query router designed to handle multiple types of client applications and compute request formats through a unified interface. The router can identify and route different query types (ad-hoc queries, scheduled queries, interactive computations) to appropriate compute resources while maintaining a single, simple service architecture. This multi-functional design allows the system to adapt to diverse client applications without requiring separate processing services for each type.
Data Source
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AI summary
Systems and methods provide a first group of computation modules configured to provide back-end compute resources for a first type of computation module by back-end compute resources. Systems and methods provide a second group of computation modules that are configured to provide back-end compute resources for a second type of computation module wherein the first and second type of computation modules are of different types. In some examples, methods and systems identify the type of computation module needed by each of a plurality of queries associated with different types of client applications based on computation module type data associated with each query and route each of the plurality of queries to an appropriate computation module within the first or second computation modules based on the computation module type data. Related methods are also disclosed.