Hierarchical Resource Allocation via Dependency Analysis
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Solution Overview
Problem
Sophisticated computing systems face challenges in determining which resources need expansion, replacement, or reallocation due to lack of knowledge about dependencies and processes, often leading to overloading and failures during demand spikes.
Innovation Solution
Implementing client-managed hierarchical resource allocation, where a host analyzes dependencies between computing resources, estimates resource demands, and allocates resources based on historical data and task similarities to prioritize tasks and manage workload effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual process is used to determine resource expansion needs, then administrator control is maintained, but system complexity increases and dependencies are not fully understood
Solution Approach 1:
The system automatically analyzes dependencies between computing resources and performs resource allocation decisions without requiring manual administrator intervention. The dependency analysis module and resource allocation module work autonomously to identify resource needs, evaluate alternatives, and execute allocations based on defined policies, thereby reducing system complexity while maintaining controlled operation.
Solution Approach 2:
The system continuously monitors resource utilization and dependency relationships, using this feedback to dynamically adjust resource allocations. The dependency analysis module receives real-time data about resource usage patterns and system performance, processes this information to identify changing dependencies, and updates allocation decisions accordingly, creating a closed-loop control system.
2Productivity
If capacity is added to a computing resource, then throughput should improve, but system reliability decreases due to unaddressed capacity constraints in other resources
Solution Approach 1:
The system segments the computing system into multiple independent computing resources with clearly defined dependency relationships. The dependency analysis module identifies which resources depend on which, allowing capacity additions to be made to specific resources without affecting the entire system. This segmentation enables targeted capacity expansion that addresses actual bottlenecks rather than adding capacity uniformly across all resources.
Solution Approach 2:
The system performs preliminary dependency analysis before implementing any capacity changes. The dependency analysis module proactively identifies potential capacity constraints and their impact on system throughput before resources are expanded or reallocated. This preliminary assessment prevents premature or ineffective capacity additions by ensuring that capacity is added only where it will actually improve system-wide throughput.
3Speed
If computing resources are not allocated based on dependency analysis, then allocation speed is fast, but system stability decreases during demand spikes
Solution Approach 1:
The system performs dependency analysis and allocation planning in advance, before demand spikes occur. The dependency analysis module continuously monitors system state and pre-identifies resources that will be needed during high-demand periods. This preliminary preparation allows the system to respond quickly to demand spikes without needing to perform complex analysis in real-time, thereby maintaining both speed and stability.
Solution Approach 2:
The resource allocation system dynamically adjusts allocations based on changing demand patterns and dependency relationships. The dependency analysis module continuously updates dependency maps as system conditions change, and the resource allocation module responds by reallocating resources between computing resources. This dynamic adaptation allows the system to maintain stability during demand spikes while preserving the ability to allocate resources efficiently.
Data Source
AI summary
A host may provide a client-managed hierarchical resource allocation. In some aspects, a host may analyze a computing system including a plurality of computing resources to identify, for various tasks, dependencies between the computing resources. The host may receive a new task to be performed by the computing system. The new task may be associated with a similar task performed by the computing system to enable modeling the new task using dependencies between computing resources which are determined from the similar task. The host may receive an estimate of computing resources required by the new task. The host may then allocate at least a portion of the computing resources to perform the new task based on the estimated workload of the new task and similarities between the new task and the similar task.