Mobile Edge Computing (MEC), as a real-time computing paradigm extended to the network edge, has been widely adopted. In recent years, Wireless Power Transfer-Aided Mobile Edge Computing (WPT-MEC) has garnered significant attention. However, it faces challenges in formulating effective offloading strategies and optimally allocating electrical energy resources. Existing solutions exhibit certain limitations, such as heuristic methods, incurring high computational complexity and struggle to adapt to dynamic environments. Although Deep Reinforcement Learning (DRL) overcomes the drawbacks of heuristic algorithms, it requires extensive time and training data. To address these issues, this paper proposes a DRL-Based Adaptive Offloading algorithm for WPT-MEC, termed as DRL-Based Adaptive Offloading (DRLAO) algorithm. This algorithm is able to dynamically adapt to environmental changes, make decisions rapidly, and adjust parameters in real-time. The DRLAO algorithm is comprised of three components: Augmented Deep Neural Network (AugDNN), Order-Preserving Quantization (KOQ) for addressing offloading decision-making, and Modi-fied Secant Method (MSM) for manipulating electrical energy resource allocation. The DRLAO algorithm achieves optimal performance of over 98% in different numbers of Wireless Edge Devices (WEDs) with lower CPU latency, and outperforms the baseline algorithm in terms of effectiveness and performance. In addition, it is able to quickly adapt and converge with minimal oscillation in dynamic environments. The source code is available at https://github.com/Aurora001226IDRLAO.

Recently, hybrid cloud computing paradigm has be widely advocated as a promising solution for Software-as-a-Service (SaaS) providers to effectively handle the dynamic user requests. With such a paradigm, the SaaS providers can extend their local services into the public clouds seamlessly so that the dynamic user request workload to a SaaS can be elegantly processed with both the local servers and the rented computing capacity in the public cloud. However, although it is suggested that a hybrid cloud may save cost compared with building a powerful private cloud, considerable renting cost and communication cost are still introduced in such a paradigm. How to optimize such operational cost becomes one major concern for the SaaS providers to adopt the hybrid cloud computing paradigm. However, this critical problem remains unanswered in the current state of the art. In this paper, we focus on optimizing the operational cost for the hybrid cloud paradigm by theoretically analyzing the problem with a Lyapunov optimization framework. This allows us to design an online dynamic provision algorithm. In this way, our approach can address the real-world challenges where no a priori information of public cloud renting prices is available and the future probability distribution of user requests is unknown. We then conduct extensive experimental study based on a set of real-world data, and the results confirm that our algorithm can work effectively in reducing the operational cost.

Hybrid cloud computing paradigm has recently be widely advocated, where Software-as-a-Service (SaaS) providers can extend their local services into the public clouds seamlessly. In this way, dynamic user request workload to a SaaS can be elegantly handled with the rented computing capacity in public cloud. However, although a hybrid cloud may save cost compared with the private cloud, it still introduces considerable renting cost and communication cost. How to optimize such an operational cost becomes one major concern for the SaaS providers to adopt such a hybrid cloud computing paradigm. However, this critical problem remains unanswered in the current state of the art. In this paper, we focus on optimizing the operational cost for the hybrid cloud model by theoretically analyzing the problem with a Lyapunov optimization framework, and accordingly providing an online dynamic provision algorithm. In this way, our approach can address the real-world challenges where no a priori information of public cloud renting prices is available and the future probability distribution of user requests is unknown. We then conduct experimental study based on a set of real-world data, and the results confirm that our algorithm can work well in reducing the cost.