Component Profile Translation for Dynamic Resource Allocation
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Solution Overview
Problem
The performance of a runtime system is hindered by inefficient resource allocation among its components, as the resource usage expectations vary based on different system scenarios, leading to conflicts in allocation strategies between productive and development environments.
Innovation Solution
The system translates system profiles into component profiles using a translation rule set, allowing for optimized resource allocation during component initialization, ensuring that resources are allocated based on the specific needs of each component according to the system's usage scenario, whether it's for faster response times or reduced overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If resources are allocated based on component needs in productive environments, then response time is improved, but system overhead increases
Solution Approach 1:
The system dynamically adjusts resource allocation strategies based on the operational mode (productive vs development). The runtime system translates system profiles into component profiles and modifies resource allocation parameters accordingly, allowing the same system to optimize for response time in productive mode and for overhead reduction in development mode.
Solution Approach 2:
The invention changes allocation parameters such as memory size, thread count, and resource limits based on the detected system scenario. By translating system profiles into component profiles with scenario-specific parameters, the system adapts resource allocation to match the operational context, resolving the contradiction between performance and overhead.
2Use of energy by moving object
If resources are allocated uniformly to all components, then system overhead is reduced, but performance deteriorates
Solution Approach 1:
The system applies different resource allocation strategies to different components based on their specific needs and the system scenario. Instead of uniform allocation, the runtime system translates system profiles into component-specific profiles that assign resources according to each component's requirements, achieving both low overhead and high performance.
Solution Approach 2:
The system uses system profiles and scenario detection to feedback-adjust resource allocation. By monitoring the operational mode and translating system profiles into component profiles, the system continuously optimizes resource distribution to balance overhead and performance based on actual system needs.
3Speed
If resource allocation strategies are optimized for productive environments, then response time improves, but adaptability to development environments decreases
Solution Approach 1:
The runtime system implements a universal resource allocation mechanism that can adapt to multiple environments (productive and development). By translating system profiles into component profiles and detecting operational modes, the same system can optimize for response time in productive environments and for overhead reduction in development environments, achieving environmental adaptability.
Solution Approach 2:
The system dynamically switches allocation strategies based on the detected environment. The runtime system monitors system profiles and scenario information to determine whether to apply productive or development mode allocation, enabling the same system to adapt its behavior to different environmental contexts.
4Productivity
If resource allocation is customized for each component, then component performance improves, but system complexity increases
Solution Approach 1:
The runtime system acts as an intermediary between system profiles and component allocation. It translates system profiles into component profiles, abstracting the complexity of resource allocation from individual components. This mediator approach allows customized allocation without increasing component-level complexity.
Solution Approach 2:
The system segments the resource allocation process into distinct phases: system profile identification, translation to component profiles, and resource assignment. This segmentation isolates the complexity to the runtime system while keeping components simple, achieving customized allocation without burdening individual components with complex logic.
Data Source
AI summary
Systems, methods and techniques relating to publishing mobile applications are described. A described technique includes identifying, at a second component container contained in a first component container, a first component container profile associated with the first component container, translating at least a portion of the first component container profile to a second component container profile associated with the second component container, and initializing the second component container based, at least in part, on the second component container profile.