Telemetry-Driven Dual Autoscaling for Dynamic Cluster Resources
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Solution Overview
Problem
Existing data systems face inefficiencies in utilizing computing resources due to fixed and static assignments that do not adapt to changing demand, leading to suboptimal performance and resource waste.
Innovation Solution
Implement a dual autoscaling mechanism comprising a slow autoscaler and a fast autoscaler, where the slow autoscaler adjusts cluster configurations based on historical and predicted workload data, while the fast autoscaler reacts quickly to sudden changes using real-time telemetry, with conflict resolution to manage independent operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computing resources are assigned to execute operations in a cluster group, then operations can be performed using distributed computing power, but the fixed and static assignments prevent adaptation to demand changes leading to resource waste
Solution Approach 1:
The patent implements dynamic resource assignment where computing resources can be automatically added to or removed from cluster groups based on real-time demand metrics. The system monitors operational queues and performance indicators, then dynamically adjusts cluster composition and size, allowing resources to transition between active and inactive states according to workload requirements.
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring operational queues, performance metrics, and demand signals from the data system. This feedback loop enables the autoscaling mechanism to detect when resources are underutilized or overwhelmed, triggering automatic adjustments to resource allocation to maintain optimal performance levels.
2Speed
If more computing resources are allocated to execute operations, then operational speed increases, but resource utilization efficiency decreases when demand is low
Solution Approach 1:
The patent implements dynamic resource allocation where the system automatically scales computing resources up or down based on real-time demand assessment. When operational queues indicate high demand, additional resources are provisioned to maintain execution speed; when demand decreases, resources are released to improve utilization efficiency and reduce waste.
Solution Approach 2:
The system changes operational parameters by adjusting the number of active computing resources based on monitored performance metrics and demand signals. This parameter adjustment allows the system to optimize the balance between execution speed and resource efficiency, scaling resource allocation to match actual workload requirements rather than maintaining fixed capacity.
3Adaptability or versatility
If computing resources remain assigned to operations that no longer need them, then resource availability for new operations decreases, but reassigning resources causes operational delays
Solution Approach 1:
The patent implements a self-service mechanism where the autoscaling system automatically monitors resource utilization and initiates reassignment actions without manual intervention. When resources become underutilized, the system autonomously identifies suitable operations for resource reallocation and manages the transition process, minimizing both resource availability constraints and reassignment delays.
Solution Approach 2:
The system uses feedback from resource utilization monitoring to trigger automatic reassignment operations. When metrics indicate that currently assigned resources are no longer needed, the feedback loop initiates automated reassignment processes that balance resource availability with operational continuity, reducing both idle resource time and reassignment disruption.
Data Source
AI summary
Autoscaling techniques can optimize usage of computing resources in a data system while also quickly reacting to change in workloads. The computing resources are arranged in different clusters. Autoscaling can be partitioned into two separate, independent autoscaling phases: a slow autoscaler and a fast autoscaler.