Joint Resource Allocation for Multi-Service MEC Systems
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Solution Overview
Problem
Existing mobile edge computing (MEC) systems face performance issues when handling diverse and differentiated quality of service (QoS) requirements in real-time, as previous solutions are oriented towards single services and fail to optimize resource allocation effectively for multiple services simultaneously.
Innovation Solution
A method for joint optimization of resource allocation, involving dynamic sub-channel allocation and resource allocation algorithms, which compute optimal CPU speed scaling, user association, sub-carrier assignment, and power allocation to balance power consumption and user satisfaction across multiple services, using a mixed-integer nonlinear programming approach and successive convex approximation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If single-service optimization methods are used, then service-specific performance is improved, but overall system performance for multiple services deteriorates
Solution Approach 1:
The patent combines multiple single-service optimization methods into a unified joint optimization framework that simultaneously optimizes resource allocation for multiple services. The method merges computation offloading optimization, video streaming optimization, and adaptive video streaming optimization into a single system that handles diverse QoS requirements, thereby improving overall system performance while maintaining service-specific performance through integrated resource management.
Solution Approach 2:
The patent creates a universal resource allocation optimization method that can handle multiple service types (computation offloading, video streaming, adaptive video streaming) with different QoS requirements. This multi-functional optimization framework uses a unified objective function and constraint system that adapts to various service scenarios, making the system capable of optimizing for diverse services simultaneously rather than requiring separate optimization methods for each service type.
2Device complexity
If static service types are provided, then system complexity is reduced, but adaptability to dynamically varied user requirements deteriorates
Solution Approach 1:
The patent implements dynamic service type identification and adaptive optimization where the system automatically detects the current service scenario (computation offloading, video streaming, or adaptive video streaming) and adjusts its optimization strategy accordingly. This dynamic approach allows the system to adapt to varying user requirements in real-time without requiring complex manual configuration, using observable system states and performance metrics to determine the appropriate optimization mode.
3Productivity
If joint optimization of multiple services is implemented, then overall system performance is improved, but computational complexity increases
Solution Approach 1:
The patent segments the joint optimization problem into manageable components by identifying distinct service scenarios (computation offloading, video streaming, adaptive video streaming) and applying scenario-specific optimization strategies. This segmentation allows the complex multi-service optimization problem to be broken down into smaller, more tractable sub-problems that can be solved more efficiently while still achieving joint optimization benefits across all services.
Data Source
AI summary
A method for joint optimization of resource allocation includes: obtaining network data volumes of two services; obtaining queue statuses at a time t; computing sub-channel slices; computing a local CPU speed scaling, a user association, a sub-carrier assignment, and a power allocation of service 1; computing a user association, a video quality decision, and a sub-carrier assignment of service 2; obtaining an initial sub-carrier assignment and an initial power allocation; obtaining the user association; obtaining the power allocation and the sub-carrier assignment of service 1; obtaining the video quality decision; obtaining the sub-carrier assignment of service 2; obtaining an optimal data transmission rate and the user association to obtain a data rate allocation; and obtaining an optimal CPU speed scaling, an optimal user association, an optimal sub-carrier assignment, an optimal power allocation, an optimal video quality decision and an optimal sub-channel allocation.