Edge Workload Scheduling via Carbon and Cost Scoring
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Solution Overview
Problem
Existing information handling systems face challenges in efficiently executing workloads across distributed computing environments, particularly in managing workload distribution, resource utilization, and user experience in dynamic environments like a user's home with varying AV and network configurations.
Innovation Solution
A method that utilizes edge computing devices to execute workloads by determining valid execution slots based on criteria such as workload duration, carbon emissions, utility costs, and disruption penalties, and automatically migrates user sessions across devices and environments to ensure seamless gaming experiences across different rooms in a home.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workloads are distributed across multiple edge computing devices in a distributed computing environment, then resource utilization and productivity are improved, but system complexity and difficulty of managing workload distribution increase
Solution Approach 1:
The patent introduces a workload management system that acts as an intermediary between multiple edge computing devices and incoming workloads. This mediator schedules workloads by determining valid execution slots based on device availability, performance characteristics, and current load conditions, thereby simplifying the complexity of direct multi-device coordination while maintaining high resource utilization and productivity
Solution Approach 2:
The system performs preliminary actions by pre-evaluating and caching execution slot availability for each edge computing device before workload assignment. By determining valid execution slots in advance based on device schedules, maintenance windows, and performance metrics, the system reduces real-time scheduling complexity while improving workload distribution efficiency
2Speed
If edge computing devices are used to execute workloads locally, then latency is reduced and speed is improved, but energy consumption and carbon emissions increase
Solution Approach 1:
The patent implements dynamic workload scheduling that adapts execution slot selection based on real-time energy conditions and workload characteristics. The system dynamically adjusts whether to execute workloads locally on edge devices or defer to centralized processing by evaluating current energy consumption patterns, carbon emission factors, and workload urgency, thereby optimizing the balance between execution speed and energy usage
Solution Approach 2:
The system changes operational parameters by introducing energy cost and carbon emission metrics as scheduling constraints. Execution slots are scored and selected based on multiple parameters including estimated energy consumption, carbon intensity of local versus remote processing, and workload performance requirements, allowing the system to select optimal execution locations that minimize environmental impact while maintaining acceptable speed
3Object-affected harmful factors
If computing devices are scheduled based on multiple criteria including carbon emissions and utility costs, then environmental impact is reduced, but the complexity of scheduling algorithms increases
Solution Approach 1:
The patent creates simplified models or copies of complex scheduling decisions by pre-calculating execution slot scores based on carbon emissions, utility costs, and device availability. These pre-computed scores serve as proxies for the full multi-criteria evaluation, allowing the scheduling algorithm to make environmentally optimal decisions without repeatedly performing complex calculations for each workload assignment
4Adaptability or versatility
If user sessions are automatically migrated across devices and environments, then user experience and adaptability are improved, but system complexity and disruption penalties increase
Solution Approach 1:
The patent implements feedback mechanisms that monitor user location, device availability, and session state in real-time. The workload management system receives feedback about user movement between environments and automatically triggers session migration only when appropriate conditions are met, such as when the user enters a new room with available computing resources. This feedback-driven approach improves adaptability while avoiding unnecessary migrations that would increase system complexity
Data Source
AI summary
Systems and methods described herein may provide a system that enables execution of workloads within a distributed computing environment that includes one or more edge computing devices and/or one or more user computing devices. A computing device may determine a workload for execution within a distributed computing environment that includes a plurality of computing devices. The computing device may determine valid execution slots within the distributed computing environment and may select a first execution slot of the valid execution slots. A first edge computing device may be assigned to execute the workload during the first execution slot. The first execution slot may be selected based on scores computed for the valid execution slots. The scores may be determined based on at least one criteria selected from the group consisting of (i) workload durations, (ii) carbon emissions, (iii) utility costs, and (iv) a disruption penalty.