Workload-Aware Executor Group Autoscaling for Resource Utilization
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Solution Overview
Problem
Existing solutions for autoscaling compute instances in cloud environments often result in low machine utilization due to inefficient task scheduling and resource allocation, leading to noisy neighbor issues and suboptimal performance.
Innovation Solution
Implement a workload-aware autoscaler that dynamically adjusts executor group sizes based on task resource demands and service level agreements, allowing tasks to be scheduled on differently sized executor groups, which can be scaled up or down independently to optimize resource utilization and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If compute instances are allocated to process workloads, then task processing capability is improved, but machine utilization deteriorates due to inefficient resource allocation
Solution Approach 1:
The system dynamically adjusts executor group sizes based on real-time workload conditions. The autoscaler continuously monitors system state and modifies the number of executors in each group, transitioning from static resource allocation to dynamic adaptation. This resolves the contradiction by ensuring compute instances are actively engaged when workloads exist while allowing efficient de-allocation when not needed.
Solution Approach 2:
The patent changes the parameter of executor group size based on workload characteristics. By monitoring workload demands and adjusting the number of executors accordingly, the system optimizes both task processing capability and machine utilization. The autoscaler modifies system parameters (executor counts) in response to changing conditions, preventing both over-provisioning and under-provisioning.
2Productivity
If compute instances are allocated to process workloads, then task processing capability is improved, but resource allocation efficiency deteriorates leading to noisy neighbor issues
Solution Approach 1:
The system segments compute resources into multiple executor groups that can be independently scaled and managed. Each executor group can be optimized for specific workload types or priorities, preventing noisy neighbor issues where one task interferes with another. The autoscaler manages multiple segments (executor groups) rather than treating all compute instances as a monolithic pool, improving resource allocation efficiency.
Solution Approach 2:
Different executor groups are assigned different qualities or characteristics based on local workload requirements. The autoscaler applies different scaling policies and resource allocations to different groups based on their specific demands, ensuring each group receives appropriate resources without being constrained by other groups' requirements. This localized optimization improves overall resource allocation efficiency.
3Loss of energy
If executor group sizes are made dynamic, then resource utilization is improved, but system complexity increases
Solution Approach 1:
The autoscaler implements self-service mechanisms where the system automatically monitors its own state and adjusts executor group sizes without external intervention. The workload-aware autoscaler autonomously detects when scaling is needed and executes scaling operations, reducing the operational complexity burden on users while maintaining dynamic resource utilization. The system serves itself by making scaling decisions based on embedded monitoring and control logic.
Solution Approach 2:
The system incorporates feedback loops where the autoscaler continuously monitors workload conditions and system state, then uses this feedback to adjust executor group sizes. The monitoring component provides feedback about resource usage and workload demands, which the autoscaler processes to make informed scaling decisions. This feedback mechanism enables dynamic resource utilization while managing complexity through automated closed-loop control rather than manual configuration.
Data Source
AI summary
Disclosed examples select a first quantity of executors for a first executor group in the virtual compute cluster; select a second quantity of executors for a second executor group in the virtual compute cluster, the first quantity of executors different from the second quantity of executors; in response to a first task, instantiate the first executor group in the virtual compute cluster based on the first quantity of executors satisfying a first resource demand of the first task; and in response to a second task, instantiate the second executor group in the virtual compute cluster based on the second quantity of executors satisfying a second resource demand of the second task.