Thermal-Aware Load Balancing for Edge FaaS Execution
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Solution Overview
Problem
In edge computing networks, thermal and power constraints pose challenges in managing Function-as-a-Service (FaaS) execution, as resource-intensive functions require specialized devices that must maintain thermal stability to avoid overheating, while satisfying Service Level Agreements (SLAs) and Quality-of-Service (QoS) requirements.
Innovation Solution
The implementation of thermal- and power-aware load balancing and cooling management techniques, which involve monitoring thermal telemetry and energy properties across edge entities, generating predictive models, and distributing estimated thermal and energy constraints to optimize resource allocation and execute FaaS functions across peer edge entities, preserving thermal properties and reducing power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resource-intensive FaaS functions are executed on specialized edge devices, then service functionality and performance are improved, but thermal output increases causing overheating risks
Solution Approach 1:
The system dynamically adjusts workload distribution across edge entities based on real-time thermal telemetry data. The load balancer continuously monitors thermal properties and redistributes FaaS functions from overheating devices to cooler ones, making the system adaptive to changing thermal conditions while maintaining service productivity.
Solution Approach 2:
The patent segments the edge computing network into multiple independent edge entities, each with its own thermal characteristics. By dividing the overall workload across these segmented entities rather than concentrating it on single devices, the system can execute resource-intensive FaaS functions while distributing thermal load to prevent any single device from overheating.
2Reliability
If thermal monitoring and predictive modeling are implemented across edge entities, then thermal management capability is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal thermal management framework that operates across multiple edge entities through a standardized interface. The load balancer performs multiple functions including thermal monitoring, predictive modeling, workload distribution, and cooling management within a single system component, reducing overall system complexity despite the enhanced thermal management capabilities.
Solution Approach 2:
The load balancer acts as an intermediary component that centralizes thermal management functions. Rather than requiring each edge device to independently implement complex thermal monitoring and control, the load balancer mediates between the edge entities and the thermal management system, simplifying individual device complexity while improving overall thermal reliability.
3Temperature
If workload is redistributed across multiple edge entities to manage thermal load, then thermal stability is maintained, but communication overhead and latency increase
Solution Approach 1:
The system performs preliminary thermal assessment and predictive modeling before redistributing workloads. By predicting which edge entities will have acceptable thermal conditions in the near future, the load balancer can proactively route functions to appropriate devices, reducing the need for frequent redistributions and minimizing communication overhead associated with thermal management.
Solution Approach 2:
The patent applies local quality by making thermal management decisions specific to each edge entity's characteristics and current state. Rather than using a uniform redistribution strategy, the load balancer tailors workload allocation to the specific thermal properties and capacity of each receiving device, optimizing the balance between thermal stability and communication efficiency for each local context.
Data Source
AI summary
Technologies for managing Function-as-a-Service function requests based on thermal and power awareness include an edge entity device having a circuitry to receive, from an edge device, a request to execute a function in an edge network environment having a plurality of edge entities. The circuitry is also to evaluate thermal and power criteria associated with the request and determine, as a function of a predicted thermal output over a specified time period relative to thermal and power criteria, whether to execute the function. In response to a determination by the circuitry to not execute the function, the circuitry is to select an edge entity of a plurality of edge entities that is able to satisfy the thermal and power criteria. The circuitry is further to forward the request to the selected edge entity.