Sliding Window Non-Busy Looping for Cloud CPU Efficiency
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Solution Overview
Problem
Existing packet processing systems in cloud computing environments face high operating costs due to inefficient CPU usage when load is low, as they rely on busy-looping mechanisms that maintain constant resource utilization regardless of system load, leading to increased energy consumption and billing costs.
Innovation Solution
Implementing a sliding window-based non-busy looping mode that dynamically determines when a polling thread can rest, aligning CPU usage with system load by tracking packet processing status through a sliding window template and decision-making table to minimize idle CPU cycles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If thread busy looping is used to continuously check for packets, then packet processing latency is reduced and throughput is improved, but CPU usage and energy consumption increase during low load periods
Solution Approach 1:
The patent implements periodic polling instead of continuous busy looping. The polling thread checks for packets at fixed intervals rather than continuously, allowing the CPU to enter low-power states between polls. This reduces energy consumption during idle periods while still maintaining acceptable packet processing latency through appropriately tuned poll intervals.
Solution Approach 2:
The patent dynamically adjusts the polling behavior based on system state. When packets are detected, the system transitions to active processing mode; when no packets are present for a threshold period, it transitions to idle mode with reduced polling frequency. This dynamic adaptation resolves the contradiction by matching CPU activity level to actual work demand.
2Productivity
If thread busy looping is used to ensure immediate packet processing, then packet processing performance is improved, but operating costs increase in cloud computing environments
Solution Approach 1:
The system uses periodic polling with configurable intervals to balance throughput and cost. During high-load periods, poll frequency increases to maximize throughput; during low-load periods, frequency decreases to reduce operational costs. This periodic adaptation allows the system to maintain productivity when needed while minimizing energy loss during idle times.
Solution Approach 2:
The patent implements feedback mechanisms that monitor packet queue status and adjust polling behavior accordingly. When packets are detected in the queue, the system intensifies processing activity; when queues remain empty, it reduces activity to lower operational costs. This feedback-driven approach ensures productivity is maintained when work is available while reducing energy loss when it is not.
3Speed
If CPU utilization is maintained at high levels through busy looping, then packet processing speed is improved, but resource efficiency decreases during low load periods
Solution Approach 1:
The patent implements dynamic CPU utilization adjustment based on actual packet arrival rates. The system maintains high CPU utilization when packets are present to ensure fast processing, but allows utilization to drop during idle periods to improve resource efficiency. This dynamic behavior resolves the contradiction by adapting CPU activity to match work demand rather than maintaining constant high utilization.
Solution Approach 2:
The system uses periodic checks to monitor for packet arrivals and adjusts CPU activity accordingly. Between packet arrivals, the CPU enters lower-activity states to improve resource efficiency. When packets arrive, the CPU activates to maintain high processing speed. This periodic activation pattern resolves the contradiction between speed and resource efficiency.
Data Source
AI summary
A method is implemented in a computing system for managing resources to decrease busy-looping, the method using a sliding window template including at least a first sliding window. The method includes initializing the sliding window template for a monitored resource, determining a current status of the monitored resource, updating the first sliding window with the current status, determining a first sliding window status based on whether a first sliding window threshold is met, and determining whether to sleep the monitored resource based on a decision-making table that uses at least the first sliding window status as input.