Elastic Scaling System for Processing Operators
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Solution Overview
Problem
Dynamic demand for application services can lead to latency and inefficiencies due to mismatched computational resources and operators, causing bottlenecks during peak demand and underutilization during low demand.
Innovation Solution
An elastic scaling system that dynamically adjusts the number of resources and operators using a topology-aware scheduler, congestion monitor, and scaling scheduler to match demand, with stateful scaling and consistent hashing to minimize data loss and migration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of computational resources and operators is increased to handle peak demand, then the system can process more requests, but resources are underutilized during low demand periods
Solution Approach 1:
The system implements dynamic scaling by monitoring demand metrics and automatically adjusting the number of computational resources and operators in real-time. This allows the system to transition from static resource allocation to dynamic adaptation, ensuring resources match actual demand levels and preventing both over-provisioning and under-provisioning
Solution Approach 2:
The system employs feedback mechanisms through congestion monitors and metrics collection that continuously track system performance and demand patterns. This feedback loop enables the scaling scheduler to make informed decisions about resource allocation, adjusting capacity based on actual system state and demand signals
2Loss of energy
If computational resources are scaled down to reduce costs during low demand, then resource efficiency improves, but latency increases during peak demand
Solution Approach 1:
The system performs preliminary actions by pre-warming operators and pre-allocating resources before peak demand occurs. The scaling scheduler anticipates demand patterns and proactively adjusts capacity, preventing latency issues before they arise rather than reacting after performance degradation occurs
Solution Approach 2:
The system transitions from static resource allocation to dynamic scaling, allowing computational resources to flexibly adapt to changing demand conditions. This dynamic approach ensures sufficient capacity is available during peak periods while maintaining efficiency during low-demand periods
3Productivity
If the number of operators is increased to handle higher demand, then processing capacity increases, but data loss may occur during scaling operations
Solution Approach 1:
The system implements self-service mechanisms where operators automatically manage their own state and data during scaling operations. Each operator maintains its state independently and can be scaled individually, allowing the system to add or remove operators without forcing a complete system shutdown or data loss
Solution Approach 2:
The system segments the processing workload into independent operators that can be scaled individually. This segmentation allows granular control over scaling operations, enabling the system to adjust capacity by adding or removing specific operators while maintaining continuity of others, thereby preventing data loss during scaling
4Loss of energy
If resources are dynamically scaled to match demand, then efficiency improves, but system complexity increases
Solution Approach 1:
The system employs universal components that perform multiple functions. The scaling scheduler, for example, handles both resource allocation and operator management, while congestion monitors simultaneously track system performance and trigger scaling events. This multi-functionality reduces the need for separate specialized components, managing complexity
Data Source
AI summary
Example implementations relate to scaling a processing system. An example implementation includes receiving an application having a number of operators for performing a service in the processing system. A metric of the processing system may be monitored while the application runs, and the processing system may be scaled where the metric surpasses a threshold. In an example, the processing system may be scaled by increasing or decreasing the number of operators of the application.