Elastic Compute Resource Autoscaling via Node Warming
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Solution Overview
Problem
Cloud-based database analytics systems face challenges in minimizing system downtime during compute resource expansions and contractions, which are complex and disruptive, especially in handling sudden surges or erratic queries, making short interruptions unacceptable.
Innovation Solution
A system and method that schedules compute nodes to be active during specific time windows, determines which nodes to execute queries, and dynamically allocates resources to manage query execution, allowing for efficient expansion and contraction of cloud-based resources with minimal downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If compute resources are expanded or contracted in current cloud platforms, then resource capacity is adjusted, but system downtime occurs and complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-warming compute nodes before they are needed for query execution. Compute nodes are activated and prepared in advance during idle periods or before predicted workload peaks, so that when queries arrive, ready-to-use compute nodes are immediately available, eliminating system downtime during resource expansion.
Solution Approach 2:
The system introduces an intermediary layer (the query router and compute node manager) that sits between the incoming queries and the compute nodes. This intermediary intelligently routes queries to appropriate compute nodes, manages the activation and warming of nodes, and handles the complexity of resource management, thereby isolating the complexity from the overall system operation.
2Adaptability or versatility
If compute resources are expanded or contracted in current cloud platforms, then resource capacity is adjusted, but operation complexity increases
Solution Approach 1:
The system implements self-service by automatically managing the lifecycle of compute nodes. The query router monitors system state, automatically activates dormant compute nodes when workload increases, and manages their warming process without human intervention. This automation eliminates the complexity of manual resource expansion and contraction operations.
Solution Approach 2:
The system employs feedback mechanisms where the query router continuously monitors query arrival patterns, system load, and compute node status. Based on this feedback, the system dynamically adjusts the activation and allocation of compute nodes, creating a self-regulating system that automatically responds to changing conditions without complex manual control.
3Productivity
If compute nodes are activated on demand, then resource utilization improves, but query execution delay increases due to cold start
Solution Approach 1:
The system performs preliminary warming of compute nodes in advance before they are needed for query execution. During periods of low utilization or before predicted workload peaks, the system activates and warms compute nodes so that when queries arrive, the nodes are already prepared and can immediately execute queries without cold start delays.
Solution Approach 2:
The system implements periodic activation and warming of compute nodes during idle periods or scheduled maintenance windows. By periodically preparing compute nodes in advance during low-utilization periods, the system ensures that when workload increases, pre-warmed nodes are available to handle queries immediately, balancing resource utilization with query execution speed.
Data Source
AI summary
A system may include a storage device. The system may include a plurality of processing nodes. The plurality of processing nodes communicates with the storage device. At least one processing node schedules a group of compute nodes to be active during a selected time window. The at least one processing node receives a query and determines that the query is to be executed by one of the plurality of processing nodes and the group of compute nodes. The at least one processing node schedules the query to be executed by the determined one of the plurality of processing nodes or the group of compute nodes. A method and computer-readable medium are also disclosed.