In edge computing, emerging network slicing and computation offloading can support Edge Service Providers (ESPs) better handling diverse distributions of user requests, to improve Quality-of-Service (QoS) and resource efficiency. However, fluctuating traffic and heterogeneous resources seriously hinder their broader application in multi-edge systems. Existing solutions commonly rely on static configurations or prior knowledge, lacking adaptability to changeable multi-edge environments and thus causing unsatisfying QoS and improper resource provisioning. To address this important challenge, we propose SliceOff, a novel resource-efficient offloading framework with traffic-cognitive network slicing for dynamic multi-edge systems. First, we design a new traffic prediction model based on self-attention to capture traffic fluctuations among different edge regions. Next, an adaptive slicing strategy based on random rounding is devised to adjust the resource configuration according to the traffic and demands of edge regions. Finally, we develop an improved Deep Reinforcement Learning (DRL) method with a dual-distillation mechanism to address the complex offloading problem, where twin critics networks and dual policy distillation are integrated to improve the agents exploration and updating efficiency in huge decision spaces. Notably, we carry out rigorous theoretical analysis to prove the effectiveness of the proposed SliceOff. Using the real-world testbed and datasets of user traffic, extensive experiments are conducted to verify the superiority of the proposed SliceOff. The results show that the SliceOff improves resource inefficiency and ESP profits under dynamic multi-edge environments, which outperforms state-of-the-art methods on multiple metrics under various scenarios.

翻译：在边缘计算中，新兴的网络切片与计算卸载技术能够支持边缘服务提供商更好地处理用户请求的多样化分布，从而提升服务质量与资源效率。然而，波动的流量与异构的资源严重阻碍了这些技术在多边缘系统中的广泛应用。现有解决方案通常依赖于静态配置或先验知识，缺乏对多变的多边缘环境的适应性，从而导致服务质量不佳与资源配置不当。为应对这一重要挑战，我们提出了SliceOff，一种面向动态多边缘系统的、结合流量感知网络切片的新型资源高效卸载框架。首先，我们设计了一种基于自注意力的新型流量预测模型，以捕捉不同边缘区域间的流量波动。其次，设计了一种基于随机舍入的自适应切片策略，以根据边缘区域的流量与需求调整资源配置。最后，我们开发了一种改进的、带有双重蒸馏机制的深度强化学习方法，以解决复杂的卸载问题。该方法集成了双评论家网络与双重策略蒸馏，以提升智能体在巨大决策空间中的探索与更新效率。值得注意的是，我们进行了严格的理论分析，以证明所提SliceOff的有效性。利用真实世界的测试平台和用户流量数据集，我们进行了广泛的实验以验证SliceOff的优越性。结果表明，在动态多边缘环境下，SliceOff改善了资源低效问题并提升了边缘服务提供商的收益，其在多种场景下的多项指标上均优于现有先进方法。