Container Task Scaling via Standby Mode to Reduce Idle Resources
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Solution Overview
Problem
Existing computing systems face inefficiencies in resource utilization and latency performance due to fixed resource allocation for tasks, leading to idle resources and increased latency during traffic fluctuations, as they either terminate tasks or incur 'cold starts' when demand changes.
Innovation Solution
Implement dynamic task priority management and multi-tiered scaling, adjusting resource allocations and task numbers based on incoming traffic to minimize idle resources and reduce cold starts, thereby optimizing resource utilization and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If tasks are terminated to reduce idle computing resources, then resource utilization improves, but latency performance degrades due to cold starts when demand increases
Solution Approach 1:
The patent implements dynamic resource allocation by transitioning tasks between standby mode (reduced resources) and active mode (full resources) based on real-time traffic conditions. This allows the system to adapt resource levels dynamically rather than maintaining fixed allocations, resolving the contradiction between reducing idle resources and maintaining low latency.
Solution Approach 2:
The patent keeps tasks in a standby mode with reduced but non-zero resource allocation rather than fully terminating them. This preliminary action maintains task readiness and avoids cold starts when traffic increases, while still reducing resources compared to full active mode, thus balancing resource efficiency and latency performance.
2Ease of operation
If fixed resource allocation is used for tasks, then task execution is simple, but resource utilization efficiency decreases during traffic fluctuations
Solution Approach 1:
The patent transitions from fixed resource allocation to dynamic allocation by implementing multi-tiered scaling. Tasks can scale up to full resources during high traffic and scale down to reduced resources during low traffic, automatically adapting to traffic patterns without manual intervention while improving resource utilization efficiency.
Solution Approach 2:
The patent changes the resource allocation parameter from a fixed value to a variable value that depends on traffic conditions. By monitoring traffic and adjusting resource allocation accordingly, the system achieves better resource utilization efficiency while maintaining automated operation simplicity.
3Loss of time
If tasks are scaled up to handle increased traffic, then latency performance improves, but computing resource costs increase
Solution Approach 1:
The patent implements dynamic scaling where tasks automatically transition between standby and active modes based on traffic conditions. During low traffic, tasks remain in standby mode with reduced resource allocation to minimize costs. When traffic increases, tasks quickly transition to active mode to handle the load, optimizing the balance between latency performance and resource costs.
Solution Approach 2:
The patent maintains tasks in a standby state with reduced resources rather than fully terminating them, preparing for quick activation when traffic increases. This preliminary action avoids the need for complete resource reallocation, reducing the peak resource requirements and associated costs while maintaining low latency performance.
Data Source
AI summary
The present application relates to performing a scale-down of the computing resources allocated to executing a software application. For example, the software application for implementing a web server may be packaged as a container image, and one or more instances of the container images may be executed as one or more tasks. The individual tasks may be allocated a set of computing resources such as CPU and memory, and the incoming requests sent to the web server may be distributed across the tasks. If the volume of incoming requests drops below a threshold level, one or more of the tasks may be placed in standby mode, and the amount of computing resources allocated to such tasks may be reduced. When the volume of incoming requests returns above the threshold level, the amount of computing resources allocated to such tasks can be scaled back up to the full amount.