We seek the best traffic allocation scheme for the edge-cloud computing
network that satisfies constraints and minimizes the cost based on burstable
billing. First, for a fixed network topology, we formulate a family of integer
programming problems with random parameters describing the various traffic
demands. Then, to overcome the difficulty caused by the discrete feature of the
problem, we generalize the Gumbel-softmax reparameterization method to induce
an unconstrained continuous optimization problem as a regularized continuation
of the discrete problem. Finally, we introduce the Gumbel-softmax sampling
network to solve the optimization problems via unsupervised learning. The
network structure reflects the edge-cloud computing topology and is trained to
minimize the expectation of the cost function for unconstrained continuous
optimization problems. The trained network works as an efficient traffic
allocation scheme sampler, remarkably outperforming the random strategy in
feasibility and cost function value. Besides testing the quality of the output
allocation scheme, we examine the generalization property of the network by
increasing the time steps and the number of users. We also feed the solution to
existing integer optimization solvers as initial conditions and verify the
warm-starts can accelerate the short-time iteration process. The framework is
general with solid performance, and the decoupled feature of the random neural
networks is adequate for practical implementations.

本研究提出了一种基于 Gumbel-softmax 重参数化方法的边缘 - 云计算网络最佳流量分配方案的求解方法，可通过无监督学习训练出 Gumbel-softmax 采样网络来解决离散优化问题，并且该方法适用于波动性计费且在可行性和成本函数价值方面都表现出优异的性能。

基于神经网络的边缘云计算分位数优化

Neural Quantile Optimization for Edge-Cloud Computing

As a specific category of artificial intelligence (AI), generative artificial
intelligence (GenAI) generates new content that resembles what is created by
humans. The rapid development of GenAI systems has created a huge amount of new
data on the Internet, posing new challenges to current computing and
communication frameworks. Currently, GenAI services rely on the traditional
cloud computing framework due to the need for large computation resources.
However, such services will encounter high latency because of data transmission
and a high volume of requests. On the other hand, edge-cloud computing can
provide adequate computation power and low latency at the same time through the
collaboration between edges and the cloud. Thus, it is attractive to build
GenAI systems at scale by leveraging the edge-cloud computing paradigm. In this
overview paper, we review recent developments in GenAI and edge-cloud
computing, respectively. Then, we use two exemplary GenAI applications to
discuss technical challenges in scaling up their solutions using edge-cloud
collaborative systems. Finally, we list design considerations for training and
deploying GenAI systems at scale and point out future research directions.

本文概述了生成人工智能和边缘云计算的最新发展，并利用两个示例应用程序讨论了使用边缘云协作系统扩大其解决方案的技术挑战。最后，我们列出了在规模上训练和部署 GenAI 系统的设计注意事项，并指出了未来的研究方向。

边缘云计算的规模化生成人工智能概览

An Overview on Generative AI at Scale with Edge-Cloud Computing

The Internet of Vehicles over Vehicular Ad-hoc Networks is an emerging
technology enabling the development of smart city applications focused on
improving traffic safety, traffic efficiency, and the overall driving
experience. These applications have stringent requirements detailed in Service
Level Agreement.
Since vehicles have limited computational and storage capabilities,
applications requests are offloaded onto an integrated edge-cloud computing
system. Existing offloading solutions focus on optimizing the application's
Quality of Service (QoS) in terms of execution time, and respecting a single
SLA constraint. They do not consider the impact of overlapped multi-requests
processing nor the vehicle's varying speed. This paper proposes a novel
Artificial Intelligence QoS-SLA-aware adaptive genetic algorithm (QoS-SLA-AGA)
to optimize the application's execution time for multi-request offloading in a
heterogeneous edge-cloud computing system, which considers the impact of
processing multi-requests overlapping and dynamic vehicle speed. The proposed
genetic algorithm integrates an adaptive penalty function to assimilate the SLA
constraints regarding latency, processing time, deadline, CPU, and memory
requirements. Numerical experiments and analysis compare our QoS-SLA-AGA to
random offloading, and baseline genetic-based approaches. Results show
QoS-SLA-AGA executes the requests 1.22 times faster on average compared to the
random offloading approach and with 59.9% fewer SLA violations. In contrast,
the baseline genetic-based approach increases the requests' performance by 1.14
times, with 19.8% more SLA violations.

该论文提出一种基于人工智能的 QoS-SLA-aware 自适应遗传算法，包括车辆的速度和多请求处理重叠的影响，以优化异构边缘云计算系统中多请求卸载应用的执行时间。与基于随机卸载和基线基因法相比，实验结果表明该算法可以平均提高 1.22 倍的请求数执行速度，并减少 59.9％的 SLA 违规。