Workload Assignment Using Node Feedback and Resource Stability
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Solution Overview
Problem
Efficiently assigning workloads to nodes in a multi-node environment while respecting Service Level Agreements (SLAs) is challenging due to varying node configurations and limited knowledge of actual resource needs, leading to potential resource misallocation and performance impacts.
Innovation Solution
A method that obtains feedback from distributed nodes on resource allocation and workload execution, identifying candidate nodes based on available resources and stability of resource adjustments, and assigns workloads using weighted criteria to optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workloads are assigned to nodes without feedback mechanisms, then the assignment process is simple and fast, but resource allocation efficiency deteriorates and misallocation occurs
Solution Approach 1:
The patent implements feedback mechanisms where nodes report their resource allocation status and workload execution performance to a central controller. The controller uses this feedback to adjust workload assignments dynamically, ensuring efficient resource utilization while adapting to changing node conditions and workload requirements.
2Productivity
If nodes continuously adjust resource allocations, then resource utilization improves, but system stability deteriorates due to frequent changes
Solution Approach 1:
The patent implements dynamic resource allocation where nodes can adjust their resource assignments based on current workload demands and performance conditions. The system transitions from static to dynamic allocation, allowing nodes to adapt their resource distribution in real-time while maintaining stability through controlled adjustment mechanisms.
3Measurement precision
If detailed feedback from all nodes is collected, then assignment accuracy improves, but communication overhead and processing time increase
Solution Approach 1:
The patent collects feedback from nodes selectively rather than continuously from all nodes. The system determines the minimum necessary feedback information needed for accurate workload assignment and only gathers that specific data, avoiding unnecessary communication overhead while maintaining sufficient measurement precision for effective resource allocation.
4Reliability
If workload assignment considers multiple node factors, then assignment quality improves, but decision complexity increases
Solution Approach 1:
The patent transforms complex multi-factor assignment decisions into a standardized parameter evaluation framework. The system converts various node characteristics and workload requirements into comparable parameters, enabling quality workload assignment through systematic parameter comparison rather than complex decision logic.
Data Source
AI summary
Techniques are provided for assigning workloads in a multi-node processing environment using resource allocation feedback from each node. One method comprises obtaining feedback from distributed nodes that process workloads, wherein the feedback for a given node indicates (i) an allocation of resources, and (ii) a number of executing workloads. In response to receiving a given workload to be processed, candidate nodes are identified to execute the given workload; and the given workload is assigned to a given candidate node based on an amount of available resources on each candidate node and/or a stability of resource adjustments made for each candidate node. The stability of the resource adjustments made for each candidate node can be evaluated based on a maximum resource adjustment made for a given candidate node relative to a maximum resource adjustment made for each of the candidate nodes.