MEC Network Slicing Resource Allocation via MILP
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Solution Overview
Problem
Current network slicing and multi-access edge computing (MEC) technologies face challenges in efficiently allocating resources across edge nodes due to the tight coupling of networking, storage, and computation resources, leading to resource over-provisioning and performance degradation in 5G networks.
Innovation Solution
A unified MEC slicing framework that formulates the edge slicing problem as a mixed integer linear programming (MILP) problem, providing three algorithms: a centralized optimal algorithm, an approximation algorithm leveraging virtualization, and a low-complexity algorithm for efficient resource allocation, accounting for resource coupling and optimizing resource utilization across edge nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If network operators deploy multiple slices on a common physical infrastructure to meet diverse service requirements, then service customization and adaptability are improved, but resource allocation complexity and device complexity increase
Solution Approach 1:
The patent segments the network slicing problem into two distinct phases: (1) slice admission control that determines which slice requests to accept based on resource availability, and (2) resource allocation that distributes specific resources to admitted slices. This segmentation simplifies the overall complexity by breaking down the resource allocation complexity while maintaining service customization capabilities through the two-phase approach.
Solution Approach 2:
The patent implements dynamic resource allocation where the system continuously monitors resource availability and adjusts slice admissions and allocations in real-time. The slice admission controller dynamically evaluates incoming slice requests against current resource states, and the resource allocator dynamically distributes resources based on changing conditions, enabling adaptability without requiring static complex configurations.
2Adaptability or versatility
If edge nodes allocate resources to multiple slice requests simultaneously, then service diversity and adaptability are improved, but resource over-provisioning increases
Solution Approach 1:
The patent implements feedback mechanisms where edge nodes continuously monitor resource utilization levels and feed this information back to the slice admission controller and resource allocator. This feedback loop enables the system to adjust slice admissions and allocations based on actual resource consumption patterns, preventing over-provisioning while maintaining service diversity. The controller uses real-time resource state information to make informed decisions about which slices to admit and how to allocate resources.
Solution Approach 2:
The patent changes the parameter of resource allocation from static pre-configuration to dynamic adjustment based on real-time conditions. The system monitors resource utilization parameters and adjusts slice admissions and allocations accordingly, allowing service diversity while optimizing resource usage to prevent over-provisioning. The resource allocator dynamically modifies allocation parameters based on current resource states and slice requirements.
3Manufacturing precision
If a centralized optimal algorithm is used to solve the edge slicing problem, then resource allocation optimality is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the centralized optimal algorithm into two separate functions: slice admission control and resource allocation. The admission control phase uses simplified rules to quickly determine which slices to accept, while the resource allocation phase uses optimization techniques to distribute resources efficiently. This segmentation reduces computational complexity by avoiding the need to solve the complete optimization problem from scratch for every slice request, while still achieving near-optimal resource allocation.
Solution Approach 2:
The patent performs preliminary actions by pre-evaluating slice requests during the admission control phase before committing to full resource allocation. The system preliminarily assesses whether slice requests can be accommodated based on current resource availability, filtering out infeasible requests before engaging in complex optimization. This preliminary action reduces the computational burden of the subsequent resource allocation phase while maintaining optimality for admitted slices.
4Productivity
If resource allocation decisions are made at edge nodes with minimal overhead, then processing speed and scalability are improved, but resource utilization efficiency decreases
Solution Approach 1:
The patent introduces a centralized slice admission controller as an intermediary between slice requests and edge nodes. This intermediary collects slice requests, evaluates them against global resource availability, and makes admission decisions that optimize overall resource utilization. The controller then distributes allocation decisions to edge nodes, enabling them to execute allocations quickly without performing complex optimization themselves. This intermediary approach balances processing speed at edge nodes with resource utilization efficiency through centralized coordination.
Data Source
AI summary
Methods and systems are provided for allocating resources to users in a wireless network including a plurality of edge nodes that provide wireless network access and multi-access edge computing functions. Slice requests are received from the users for a type of resource, including one or more of networking resources, storage resources, and computation resources. A set of slice requests to be admitted is determined based on resource availability constraints among one or more of the networking resources, the storage resources, and the computation resources at each edge node.