Performance-Driven Load Shifting via 5G Offload
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack efficient methods for dynamically shifting user program workloads between local and cloud computing resources based on real-time performance assessments, leading to suboptimal resource utilization and user experience.
Innovation Solution
A method and system utilizing a 5G mobile network for seamless load shifting, which includes a load shifting service that assesses client device performance and offloads workloads to cloud resources, employing a service API, crowdsourcing population database, cloud resource allocator, and virtual appliances to optimize resource allocation and utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If workloads are processed locally on client devices, then processing speed and responsiveness are improved, but device energy consumption increases and processing capability is limited by local hardware
Solution Approach 1:
The system dynamically adjusts workload allocation between local and cloud resources based on real-time performance assessments. The load shifting service continuously monitors client device performance metrics and automatically transfers workloads between local and cloud computing resources, enabling the system to adapt energy consumption and processing speed to current conditions rather than using a fixed allocation strategy
Solution Approach 2:
The load shifting service acts as an intermediary between client devices and cloud computing resources. It assesses performance metrics, determines optimal workload placement, and manages the transfer of workloads between local and cloud environments, thereby mediating the trade-off between local processing speed and energy consumption without requiring direct client-device-to-cloud management
2Adaptability or versatility
If workloads are offloaded to cloud computing resources, then processing capability and resource availability are improved, but processing latency and network dependency increase
Solution Approach 1:
The system dynamically selects between local and cloud processing based on real-time performance assessments. When cloud resources provide better capability, workloads are offloaded; when local processing is sufficient or network conditions are poor, workloads remain local. This dynamic adaptation minimizes latency while maximizing resource availability
Solution Approach 2:
The load shifting service implements continuous feedback loops by monitoring performance metrics from both local devices and cloud resources. This feedback mechanism enables the system to learn from past performance and make informed decisions about workload placement, adjusting to network conditions and resource availability in real-time to optimize the balance between capability and latency
3Power
If cloud computing resources are used, then processing power and scalability are improved, but system complexity and infrastructure requirements increase
Solution Approach 1:
The load shifting service serves as an intermediary that abstracts the complexity of cloud resource management from client devices. It handles performance assessment, workload packaging, transmission, and coordination with cloud resources, thereby enabling clients to access enhanced processing power without directly managing the complex infrastructure required to support it
Data Source
AI summary
A method and system for performance-driven load shifting. Specifically, the method and system disclosed herein entail transferring user program workloads, for processing, between local computing resources available on a client device and cloud computing resources available on an offload domain based on the assessed performance score of the client device at any given point in time. Seamless load shifting is further guaranteed due substantively to the employment of a mobile network facilitating communications between the client device and the offload domain.