Computational Storage Orchestration for Data-Local FaaS Execution
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Solution Overview
Problem
Current orchestration solutions for edge computing face challenges such as vendor-lock, sub-optimal performance due to incorrect resource declarations, and difficulty in selecting correct resource allocations for microservices workloads, leading to overprovisioning and higher costs.
Innovation Solution
An intent-driven model for orchestration that maps service level objectives across systems and resources, using a meta-language to express key performance indicators and allow for nested and graduated Service Level Agreements (SLAs) to adapt to bursts and optimize resource use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional orchestration solutions are used for edge computing, then service deployment is possible, but vendor-lock occurs and resource allocation is sub-optimal
Solution Approach 1:
The patent introduces an intent-driven orchestration layer that acts as an intermediary between service level objectives and resource allocation. This mediator translates high-level intent statements into optimized resource declarations, enabling vendor independence while maintaining performance optimality through abstracted resource management
2Ease of operation
If detailed resource declarations are used for microservices, then resource allocation can be specified, but user complexity increases and overprovisioning occurs
Solution Approach 1:
The system enables self-service resource allocation where the orchestration layer automatically translates intent statements into optimized resource declarations. Users simply specify their service level objectives without needing to manually configure detailed resource parameters, eliminating overprovisioning while maintaining simplicity
Solution Approach 2:
The patent transforms resource allocation from detailed parameter specification to high-level intent declaration. By changing the parameter representation from concrete resource counts to abstract service level objectives, the system simplifies user operation while improving resource efficiency through automated optimization
3Adaptability or versatility
If static resource allocation is used, then deployment is straightforward, but system adaptability to changing demands is poor
Solution Approach 1:
The patent implements dynamic resource allocation where resource declarations are continuously optimized based on changing service level objectives and system conditions. The orchestration layer dynamically translates intent statements into resource allocations, enabling the system to adapt to varying demands without increasing user-facing complexity
Data Source
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AI summary
Various systems and methods for implementing computational storage are described herein. An orchestrator system is configured to: receive, at the orchestrator system, a registration package, the registration package including function code, a logical location of input data for the function code, and an event trigger for the function code, the event trigger set to trigger in response to when the input data is modified; interface with a storage service, the storage service to monitor the logical location of the input data and notify a location service when the input data is modified; interface with the location service to obtain a physical location of the input data, the location service to resolve the physical location from the logical location of the input data; and configure the function code to execute near the input data