Instance Service Dynamic Scaling for Cloud Workloads
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Solution Overview
Problem
Existing network computing and storage systems face challenges in accommodating unanticipated traffic spikes and customer needs, as virtual resources often fail to respond adequately, leading to inefficiencies and increased costs.
Innovation Solution
A computing resource service provider offers an instance service that allows customers to select from various instance types with different resource allocations, enabling dynamic reallocation and auto-scaling to meet workload demands, ensuring adequate performance and cost-effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If virtual resources are used to accommodate computing needs, then resource efficiency is improved, but the ability to respond to unanticipated traffic spikes deteriorates
Solution Approach 1:
The patent implements dynamic instance type selection that allows computing resources to adapt their configuration based on real-time workload conditions. The system automatically transitions between different instance types (e.g., from smaller to larger instances) in response to traffic spikes, making the virtual resource system both efficient during normal operation and adaptable during peak demand.
Solution Approach 2:
The system changes key parameters of virtual computing instances including instance type, number of instances, and resource allocation based on workload conditions. This parameter adaptation enables the system to optimize resource efficiency during steady-state operation while maintaining the capability to scale up rapidly when traffic spikes occur.
2Adaptability or versatility
If automated mechanisms are introduced to handle unanticipated traffic spikes, then adaptability is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service automated mechanisms where the computing system automatically monitors workload conditions, selects appropriate instance types, and scales resources without human intervention. The system autonomously handles traffic spikes by triggering pre-defined scaling policies and automatically provisioning additional computing capacity, thereby improving adaptability while managing complexity through automation rather than manual processes.
3Adaptability or versatility
If multiple instance types are used to meet workload demands, then adaptability is improved, but instance selection complexity increases
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor workload characteristics, performance metrics, and resource utilization across multiple instance types. This feedback information is used to automatically determine the optimal instance type selection, simplifying the decision-making process by using data-driven insights rather than complex manual analysis. The system learns from performance feedback to make informed instance type selections that balance adaptability with selection simplicity.
Data Source
AI summary
An instance service of a computing resource service provider may provide computing system instances to customers of the computing resource service provider. The computing system instances may be used by the customer to execute various customer workloads. Furthermore, the computing system instances may include an instance type indicating an amount of computing resources allocated to computing system instance of the instance type. The instance service may obtain requirement and/or constraints associated with the workload and determine a configuration of instance types to include in a set of instances configured to execute the customer workload.