Profit-Aware MEC Offloading via Prediction-Assisted Network Slicing
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Solution Overview
Problem
Existing solutions for Mobile Edge Computing (MEC) network slicing and computation offloading struggle to adapt dynamically to spatio-temporal variability in user traffic and service demands, leading to under-supply or over-supply of slice resources, which degrades Quality-of-Service (QoS) and profits for Edge Service Providers (ESPs).
Innovation Solution
The proposed SliceOff framework decouples the optimization problem of maximizing long-term ESP profits into sub-problems of Edge network Slicing (EnS) and Computation offloading Access (CoA). For EnS, a gated recurrent neural network predicts user requests, and the optimal slice partitioning is derived. For CoA, an improved deep reinforcement learning with twin critic-networks and a delay mechanism addresses Q-value overestimation and high variance to achieve near-optimal offloading and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If network slicing is implemented with fixed configurations, then network management and resource isolation are improved, but the system cannot adapt to spatio-temporal variability in user traffic and service demands
Solution Approach 1:
The patent implements dynamic network slicing by enabling Edge Service Providers to dynamically create, modify, and delete network slices in real-time based on predicted user traffic patterns and service demands. The system transitions from static slice configurations to dynamic resource allocation, allowing slices to adapt their parameters (bandwidth, computing resources, latency guarantees) according to changing conditions while maintaining isolation and management benefits.
2Adaptability or versatility
If deep reinforcement learning is applied to compute offloading optimization, then adaptiveness to dynamic environments is improved, but computational complexity and system overhead increase
Solution Approach 1:
The patent employs traffic prediction mechanisms that analyze historical data and user behavior patterns to forecast future service demands before they occur. This preliminary action allows the system to pre-allocate resources and prepare network slices in advance, reducing the need for complex real-time reinforcement learning computations and lowering overall system overhead while maintaining high adaptiveness.
3Productivity
If network slicing resources are statically allocated, then resource provisioning is simplified, but under-supply or over-supply situations occur degrading QoS and ESP profits
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor actual user traffic, service completion rates, and resource utilization across network slices. This feedback information is fed back to the orchestration system, which automatically adjusts slice configurations and resource allocations to match actual demand patterns, preventing both under-supply (degrading QoS) and over-supply (reducing ESP profits) situations.
4Productivity
If existing DRL-based methods are used for network slicing and offloading, then optimization potential is improved, but Q-value overestimation and high variance cause unstable convergence
Solution Approach 1:
The patent replaces traditional DRL approaches with a hybrid system combining traffic prediction, game theory-based resource allocation, and simplified control mechanisms. This substitution eliminates the convergence instability inherent in standard DRL by using more reliable mathematical models for resource optimization while maintaining the ability to handle dynamic and uncertain MEC environments.
Data Source
AI summary
A profit-aware Offloading Framework towards Prediction-assisted MEC Network Slicing is provided. Towards MEC network slicing, formulate the optimization problem of maximizing long-term ESP profits and decouple it into the sub-problems of Edge network Slicing (EnS) and Computation offloading Access (CoA). For the slicing sub-problem, use a gated recurrent neural network (GRNN) to accurately predict user requests in different regions, and then use the optimal partitioning of network slices with the predicted requests and expected demands; For the offloading sub-problem, incorporating results from slice partitioning, use an improved deep reinforcement learning with twin ritic-networks and delay mechanism, solving the Q-value overestimation and high variance for approximating the optimal offloading and resource allocation.