Network Slice Allocation Controller for 5G Resource Optimization
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Solution Overview
Problem
In 5G networks, existing methods fail to efficiently allocate network slices due to heterogeneous requirements and scarce resources, leading to suboptimal revenue and resource utilization, as they do not effectively consider Service Level Agreements (SLAs) and payoffs in real-time.
Innovation Solution
A method and system that dynamically evaluate network slice requests based on SLA requirements and associated payoffs using a network slice controller to maximize a utility function, such as overall network resource utilization or revenue, by accepting or rejecting requests online, and automatically selecting the appropriate network slice template for instantiation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If network slice requests are allocated based on simple first-come-first-served or static rules, then allocation simplicity is maintained, but resource utilization efficiency and revenue optimization deteriorate
Solution Approach 1:
The patent implements dynamic slice allocation by continuously monitoring network state, slice performance metrics, and SLA compliance in real-time. The allocation system adapts its decisions based on current network conditions, demand patterns, and predicted future states, transforming static resource allocation into a dynamic optimization process that maximizes utility while managing complexity through automated control loops
Solution Approach 2:
The system changes allocation parameters dynamically by adjusting slice resource assignments, priority levels, and QoS parameters based on real-time network state and SLA requirements. This allows the system to optimize resource utilization across different time periods and network conditions without requiring complete reconfiguration, balancing efficiency gains with manageable complexity
2Loss of energy
If all network slice requests are accepted to maximize revenue, then revenue potential increases, but SLA fulfillment reliability deteriorates due to resource oversubscription
Solution Approach 1:
The patent performs preliminary evaluation of slice requests against predicted future network states and SLA requirements before final allocation. By anticipating resource constraints and SLA compliance issues in advance, the system can reject or defer requests that would compromise reliability, while accepting those that can be fulfilled, thus optimizing both revenue and SLA fulfillment without reactive failures
Solution Approach 2:
The system implements continuous feedback loops that monitor actual SLA fulfillment, resource utilization, and slice performance. This feedback informs real-time allocation decisions, allowing the system to adjust acceptance criteria dynamically - being more conservative when resources are constrained and more liberal when capacity is available, thereby maintaining SLA reliability while maximizing revenue potential
3Reliability
If network resources are over-allocated to meet current demand, then immediate service quality is maintained, but future resource availability and flexibility deteriorate
Solution Approach 1:
The patent implements dynamic resource allocation that adjusts slice assignments based on predicted future demand patterns and network states. Rather than statically over-allocating resources to guarantee current service quality, the system maintains flexibility by allocating resources dynamically - ensuring service quality when needed while preserving capacity for future high-value opportunities, thus balancing reliability with adaptability
4Productivity
If complex optimization algorithms are used to maximize utility function, then resource allocation optimality improves, but computational overhead and decision-making time deteriorate
Solution Approach 1:
The patent implements a multi-tiered optimization approach where critical SLA constraints and resource limits are enforced as hard rules (partial action), while less critical objectives like revenue optimization are handled through softer, less computationally intensive methods. This selective application of optimization complexity achieves acceptable allocational optimality without the full computational burden of exhaustive optimization across all parameters
Solution Approach 2:
The system performs preliminary filtering and pre-evaluation of slice requests against known constraints and predicted states before applying full optimization algorithms. By eliminating obviously suboptimal or infeasible options in advance, the system reduces the search space for complex optimization, achieving near-optimal results with reduced computational overhead and faster decision-making
Data Source
AI summary
A method of allocating network slices of a network infrastructure includes receiving a network slice request for network resources of the network infrastructure in a form of a network slice. The network slice request includes a service level agreement (SLA) and an associated payoff. It is determined whether to accept the network slice based on whether it is expected that a utility function will be better served by accepting the network slice request or waiting for a further network slice request. It is determined whether the SLA would be fulfilled prior to allocating the network slice. The network slice is allocated and installed in the network infrastructure. Whether the utility function is better served can be determined using a value iteration algorithm or an adaptive algorithm.