A QoE-Aware Resource Allocation Algorithm for Accelerating Split Inference in NOMA-based Edge Intelligence

Belangrijkste concepten

A QoE-aware resource allocation algorithm is proposed to accelerate split inference in NOMA-based edge intelligence by optimizing the tradeoff between inference delay, resource consumption, and user quality of experience (QoE).

Samenvatting

The key highlights and insights from the content are:

1. The authors identify two important observations regarding the relationship between quality of service (QoS) and quality of experience (QoE) in edge split inference:

   • Users' QoE and inference delay follow a sigmoid-like curve, allowing for relaxation of strict QoS requirements to reduce resource consumption while maintaining acceptable QoE.
   • Minimizing the sum of all users' inference delay does not guarantee high QoE performance for the overall system due to heterogeneous user requirements.

2. Based on these observations, the authors formulate a joint optimization problem to minimize inference delay, resource consumption, and maximize QoE for edge split inference in a NOMA-based system.

3. To solve this complex optimization problem, the authors propose a gradient descent (GD)-based algorithm called Li-GD. The key idea is to use the optimal results from previous layers' GD procedures as the initial values for the current layer's GD, reducing the complexity caused by discrete parameters.

4. The authors analyze the properties of the proposed Li-GD algorithm, proving its convergence, bounding the complexity, and estimating the approximation error.

5. Experimental results demonstrate that the proposed ERA algorithm outperforms previous studies in achieving the optimal tradeoff between inference delay, resource consumption, and QoE for edge split inference.

Statistieken

The computation task of each layer in the main branch can be calculated as: flδ = mδ1fconv + mδ2fpool + mδ3frelu, where mδ1, mδ2, and mδ3 denote the number of convolutional layers, pooling layers, and ReLU layers, respectively.
The received signal-to-interference-plus-noise ratio (SINR) of AP n for device i on subchannel m is given by: Υn,i
m = pn,i
m |hn,i
m |2 / (∑βn,v
m pn,v
m |hn,v
m |2 + ∑∑βl,t
mpl,t
m|gl,t
m|2 + σ2).
The achievable data rate of user i by SIC on subchannel k of AP j is: Φj,i
k = βj,i
k Bdown/M log2(1 + Ψj,i
k), where Ψj,i
k is the SINR.

Citaten

"Even the AI has been widely used and significantly changed our life, deploying the large AI models on resource limited edge devices directly is not appropriate."
"The model split strategies between device, edge server and cloud has been investigated by previous works, and some excellent works have been proposed, such as [12-19]. These studies find the optimal model segmentation points or early-exist points to minimize inference delay and resource requirements while maintaining high inference accuracy by reinforcement learning [12], convex optimization [13-16], heuristic algorithm [17-19], etc."
"These algorithms mainly concentrate on improving and optimizing the system's Quality of Service (QoS), such as low inference latency and energy consumption, high inference accuracy, etc., ignore the effect of Quality of Experience (QoE) which is one of the critical items for users except for QoS."

Belangrijkste Inzichten Gedestilleerd Uit

A QoE-Aware Split Inference Accelerating Algorithm for NOMA-based Edge Intelligence

by Xin Yuan, Ni... om arxiv.org 09-26-2024

https://arxiv.org/pdf/2409.16537.pdf

Diepere vragen

How can the proposed QoE-aware resource allocation algorithm be extended to handle dynamic user mobility and time-varying channel conditions in edge intelligence scenarios?

To extend the proposed QoE-aware resource allocation algorithm for dynamic user mobility and time-varying channel conditions, several strategies can be implemented. First, the algorithm can incorporate real-time feedback mechanisms that continuously monitor user mobility patterns and channel conditions. This can be achieved through the integration of location-based services and channel state information (CSI) to dynamically adjust resource allocation based on the user's current position and the quality of the communication link.
Second, predictive modeling techniques can be employed to anticipate user movements and channel fluctuations. By utilizing machine learning algorithms, the system can learn from historical data to predict future states, allowing for proactive adjustments in resource allocation before significant changes occur. This predictive capability can enhance the responsiveness of the QoE-aware algorithm, ensuring that users receive optimal service even as conditions change.
Additionally, the algorithm can be designed to operate in a decentralized manner, where edge devices collaborate to share information about mobility and channel conditions. This collaborative approach can improve the overall system's adaptability and resilience, as it allows for localized decision-making that considers the unique circumstances of each user.
Finally, incorporating multi-objective optimization techniques can help balance the trade-offs between QoE, resource consumption, and latency in real-time scenarios. By continuously optimizing these objectives in response to changing conditions, the algorithm can maintain high QoE levels for users while efficiently utilizing available resources.

What are the potential challenges and limitations of the GD-based approach used in the Li-GD algorithm, and how could alternative optimization techniques be explored to further improve the performance?

The GD-based approach used in the Li-GD algorithm presents several challenges and limitations. One significant challenge is the sensitivity to the choice of hyperparameters, such as the learning rate. An inappropriate learning rate can lead to slow convergence or even divergence, making it difficult to find the optimal trade-off between inference delay, QoE, and resource consumption.
Another limitation is the potential for local minima in the optimization landscape. The gradient descent method may converge to a suboptimal solution rather than the global optimum, particularly in complex, non-convex optimization problems typical in edge intelligence scenarios. This can hinder the algorithm's ability to achieve the best possible performance.
To address these challenges, alternative optimization techniques can be explored. For instance, evolutionary algorithms, such as genetic algorithms or particle swarm optimization, can be employed to search the solution space more effectively and avoid local minima. These techniques do not rely on gradient information and can explore a broader range of solutions, potentially leading to better overall performance.
Additionally, reinforcement learning (RL) approaches can be integrated into the resource allocation framework. By treating the resource allocation problem as a sequential decision-making process, RL can adaptively learn optimal strategies based on user feedback and changing conditions, thus improving the algorithm's robustness and adaptability.

Given the importance of QoE in edge intelligence, how could the insights from this work be applied to other resource-constrained AI applications beyond split inference, such as real-time video processing or autonomous navigation?

The insights from the QoE-aware resource allocation algorithm can be effectively applied to various resource-constrained AI applications, including real-time video processing and autonomous navigation. In real-time video processing, maintaining high QoE is crucial, as users expect smooth playback and minimal buffering. By adopting a similar QoE-aware framework, video processing systems can dynamically allocate bandwidth and processing resources based on real-time user feedback and network conditions. This can involve adjusting video quality, resolution, or frame rate to optimize the viewing experience while minimizing resource consumption.
In the context of autonomous navigation, the principles of QoE can be applied to enhance user satisfaction and system performance. For instance, autonomous vehicles can utilize QoE metrics to prioritize safety and comfort during navigation. By analyzing user preferences and real-time environmental data, the system can make informed decisions about route selection, speed adjustments, and obstacle avoidance, ensuring a smoother and more enjoyable ride.
Moreover, the concept of relaxing strict latency requirements, as discussed in the context of split inference, can be beneficial in these applications. For example, in video streaming, slight delays may be acceptable if they lead to significant reductions in resource consumption and improved overall QoE. Similarly, in autonomous navigation, allowing for minor delays in decision-making can enhance safety and reliability without compromising user experience.
Overall, the application of QoE-aware strategies across various resource-constrained AI applications can lead to more efficient resource utilization, improved user satisfaction, and enhanced system performance in dynamic environments.