SLO-Based Regulator for Smooth Background Process Scaling
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Solution Overview
Problem
Cloud infrastructure services face challenges in managing foreground and background processes that share resources, leading to counterproductive performance due to unawareness and sudden changes in load, which can impact overall system efficiency and customer experience.
Innovation Solution
Implementing a self-regulating process, or SLO-based regulator, that monitors historical information and feedback to adjust the background process's speed gradually, ensuring it meets service level objectives by communicating with other regulators and dispatching threads accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If background processes run at high speed to meet service level objectives, then productivity is improved, but sudden changes in load cause instability and impact foreground processes
Solution Approach 1:
The background process dynamically adjusts its execution speed based on current system conditions and service level objectives. The regulator monitors metrics like queue depth, execution time, and resource availability to modulate the background process speed in real-time, allowing it to adapt between high-speed execution during low-load periods and conservative execution during high-load periods, thus resolving the contradiction between productivity and stability.
Solution Approach 2:
The system implements feedback mechanisms where the regulator continuously monitors performance metrics and system state, comparing actual progress against service level objectives. Based on this feedback, the regulator adjusts the background process speed to meet SLOs while preventing sudden load changes from impacting foreground processes, thereby balancing productivity with stability through closed-loop control.
2Productivity
If background processes make sudden speed changes to meet deadlines, then productivity is improved, but resource consumption increases and system efficiency decreases
Solution Approach 1:
The background process employs dynamic speed adjustment rather than static high-speed execution. The regulator modulates execution speed based on real-time conditions, allowing the process to run at optimal speeds only when necessary and reduce speed during normal conditions, thereby meeting productivity goals while minimizing resource consumption and avoiding the waste associated with constant high-speed operation.
Solution Approach 2:
The system changes operational parameters such as execution speed, thread pool size, and processing batch size based on system state and service level objectives. By adjusting these parameters dynamically rather than maintaining fixed high values, the system achieves productivity improvements when needed while reducing resource consumption during normal operation, resolving the contradiction between productivity and energy loss.
3Device complexity
If background processes operate independently without coordination, then device complexity is reduced, but counterproductive performance occurs due to unawareness of system state
Solution Approach 1:
The regulator acts as an intermediary component between the background process and the system state. It monitors system conditions, service level objectives, and process progress, then translates this information into appropriate speed adjustments. This intermediary layer enables coordinated operation without requiring complex direct communication between all components, maintaining reasonable system architecture while improving overall efficiency through informed decision-making.
4Loss of energy
If background processes use minimum resources to meet SLOs, then energy efficiency is improved, but the ability to handle peak loads decreases
Solution Approach 1:
The background process dynamically adjusts resource allocation based on current load conditions and service level objectives. During normal operation, it uses minimum necessary resources to maintain energy efficiency. When peak loads are detected or SLOs are at risk, the system rapidly scales up resource usage to handle the increased demand, thus resolving the contradiction between energy efficiency and load handling capability through dynamic adaptation.
Solution Approach 2:
The system performs preliminary actions by proactively adjusting resource allocation and process speed based on predicted needs and current trends. The regulator monitors metrics like queue depth and execution time to anticipate future load requirements, preparing the system in advance by pre-scaling resources or pre-positioning processing capacity, thereby ensuring both energy efficiency during normal periods and adequate load handling capability when needed.
Data Source
AI summary
Techniques are disclosed that enable a self-regulating process to meet a service level objective (SLO). In some embodiments, a self-regulating process is a background process comprising a regulator that receives background job requests and historical information related to the background process for evaluation to determine actions (e.g., speed up, slow down, or maintain the same speed), enabling the background process to adjust its pace gradually and smoothly even when encountering unexpected big changes in load.