Dynamic CPU Task Reassignment for Cache Hit Ratio
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Solution Overview
Problem
In computing storage systems, the dynamic reassignment of tasks between CPUs to balance cache usage and prevent queue depth skew is necessary to improve cache hit ratios and overall data management performance, as existing methods lead to idle CPUs and performance issues due to uneven dispatch queue lengths.
Innovation Solution
Organizing sets of like tasks into groups and reassigned them to CPU groups with minimal queue lengths when the difference in queue lengths exceeds a predetermined threshold, ensuring balanced CPU load and resource utilization by sharing L1/L2/L3 cache resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tasks are assigned to CPU groups without dynamic reassignment, then task execution simplicity is maintained, but queue depth skew increases and cache hit ratios deteriorate
Solution Approach 1:
The patent implements dynamic task reassignment between CPU groups based on real-time queue depth monitoring. When queue depth skew exceeds a threshold, tasks are reassigned from overloaded CPU groups to underutilized ones, transforming the static task assignment into a dynamic system that adapts to changing load conditions, thereby maintaining balanced queue depths and high cache hit ratios
Solution Approach 2:
The system continuously monitors queue depths of different CPU groups and uses this feedback information to trigger reassignment operations. When the queue depth difference between CPU groups exceeds a predetermined threshold, the system initiates task reassignment, creating a closed-loop control mechanism that maintains system performance through responsive adjustments
2Productivity
If tasks are constantly reassigned to balance queue lengths, then cache utilization improves, but system overhead and complexity increase
Solution Approach 1:
The patent applies partial reassignment by only moving tasks when queue depth skew exceeds a predetermined threshold, rather than continuously rebalancing. This selective approach performs reassignment only when necessary to maintain cache hit ratios above acceptable levels, avoiding unnecessary reassignment overhead while still achieving the productivity benefits of balanced cache utilization
Solution Approach 2:
The system changes the operational parameters of CPU groups dynamically by adjusting task assignment based on queue depth metrics. When performance degradation is detected through parameter monitoring (queue depth skew), the system modifies assignment parameters to optimize data management performance, achieving productivity improvements through parameter-based control rather than constant reassignment
3Ease of operation
If CPU groups maintain fixed task assignments, then assignment simplicity is preserved, but queue depth skew increases causing performance issues
Solution Approach 1:
The patent implements self-service through automatic task reassignment that monitors and corrects queue depth skew without external intervention. The system autonomously detects when queue depth imbalance exceeds thresholds and performs reassignment operations, maintaining queue depth balance through self-correcting mechanisms while preserving operational simplicity by eliminating the need for manual task management
Data Source
AI summary
A set of like tasks to be performed is organized into a first group. A last used processing group assigned to the set of like tasks is stored. The set of like tasks is reassigned to an additional group having a minimal queue length upon a determination that the difference between the queue lengths of the additional processing group and the stored processing group is greater than a predetermined threshold.