Multi-zone Container Scaling via Energy-Aware Worker Selection
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Solution Overview
Problem
Current container orchestration systems face challenges in efficiently scaling multi-zone container clusters, leading to inefficiencies in energy usage and increased carbon footprints due to heterogeneity in server energy efficiency and non-linear power consumption relationships, which are not effectively addressed by existing scaling methods.
Innovation Solution
A method that establishes a connection between an upper layer container orchestration controller and lower layer resource manager controllers to share worker profile data, determine estimated utilization and incremental power consumption, and select the most energy-efficient worker to add or remove from a target cluster, optimizing energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional scaling methods are used in multi-zone container clusters, then cluster scalability is achieved, but energy efficiency deteriorates due to homogeneous resource allocation ignoring server energy efficiency heterogeneity
Solution Approach 1:
The patent applies local quality by differentiating resource allocation based on individual server energy efficiency characteristics. Instead of uniform scaling, the system evaluates and selects workers based on their specific energy efficiency profiles, assigning tasks to the most efficient available workers. This local differentiation optimizes energy consumption while maintaining cluster scalability.
Solution Approach 2:
The patent changes the scaling parameter from purely workload-driven to a composite metric incorporating energy efficiency. The system modifies the scaling decision parameters to include worker energy efficiency profiles, utilization rates, and power consumption characteristics, enabling energy-aware scaling that balances productivity with energy conservation.
2Speed
If scaling decisions are made without considering worker energy efficiency profiles, then scaling speed is maintained, but energy consumption increases due to non-linear power consumption relationships
Solution Approach 1:
The patent implements preliminary action by pre-evaluating and caching worker energy efficiency profiles before scaling decisions are needed. The system proactively gathers energy efficiency data, utilization histories, and power consumption characteristics of available workers, storing this information for rapid retrieval during scaling operations. This preparation enables fast, energy-aware scaling decisions without sacrificing speed.
Solution Approach 2:
The patent employs feedback mechanisms where the system continuously monitors actual worker performance and energy consumption, comparing observed data against predicted values. This feedback loop enables the system to refine its energy efficiency models and make increasingly accurate scaling decisions, balancing speed with energy optimization through adaptive learning.
3Ease of manufacture
If existing scaling methods are used, then implementation simplicity is maintained, but carbon footprint increases due to inability to address energy efficiency heterogeneity
Solution Approach 1:
The patent introduces an intermediary layer in the form of an energy efficiency evaluation module that sits between the workload scheduler and the worker selection process. This intermediary component standardizes energy efficiency data collection, normalization, and evaluation, simplifying the overall implementation while enabling carbon-aware scaling decisions. The mediator abstracts complex energy management details from the core scaling logic.
Solution Approach 2:
The patent achieves universality by designing a multi-functional scaling framework that handles both traditional workload scheduling and energy efficiency optimization within a single system. The same scaling infrastructure supports multiple objectives: meeting workload demands, optimizing energy consumption, and reducing carbon footprint, thereby eliminating the need for separate specialized systems.
Data Source
AI summary
An embodiment for improved methods for energy efficient scaling of multi-zone container clusters is provided. The embodiment may establish a connection between an upper layer container orchestration controller associated with multiple container cluster zones and lower layer resource manager controllers corresponding to multiple datacenters. The embodiment may determine additional workers are needed to perform a task and request worker offers from the lower layer resource manager controllers. The embodiment may receive the worker offers including worker profile data at the upper layer container orchestration controller. The embodiment may utilize the upper layer container orchestration controller to determine estimated expected utilization and corresponding incremental power consumption for each of the received worker offers, utilize the upper layer container orchestration controller to accept the received worker offer corresponding to a most energy efficient worker, and add the most energy efficient worker to a target cluster.