With the explosive growth of mobile data and the unprecedented demand for computing power, resource-constrained edge devices cannot effectively meet the requirements of Internet of Things (IoT) applications and Deep Neural Network (DNN) computing. As a distributed computing paradigm, edge offloading that migrates complex tasks from IoT devices to edge-cloud servers can break through the resource limitation of IoT devices, reduce the computing burden and improve the efficiency of task processing. However, the problem of optimal offloading decision-making is NP-hard, traditional optimization methods are difficult to achieve results efficiently. Besides, there are still some shortcomings in existing deep learning methods, e.g., the slow learning speed and the weak adaptability to new environments. To tackle these challenges, we propose a Deep Meta Reinforcement Learning-based Offloading (DMRO) algorithm, which combines multiple parallel DNNs with Q-learning to make fine-grained offloading decisions. By aggregating the perceptive ability of deep learning, the decision-making ability of reinforcement learning, and the rapid environment learning ability of meta-learning, it is possible to quickly and flexibly obtain the optimal offloading strategy from a dynamic environment. We evaluate the effectiveness of DMRO through several simulation experiments, which demonstrate that when compared with traditional Deep Reinforcement Learning (DRL) algorithms, the offloading effect of DMRO can be improved by 17.6%. In addition, the model has strong portability when making real-time offloading decisions, and can fast adapt to a new MEC task environment.

With the proliferation of compute-intensive and delay-sensitive mobile applications, large amounts of computational resources with stringent latency requirements are required on Internet of Things (IoT) devices. One promising solution is to offload complex computing tasks from IoT devices either to Mobile Edge Computing (MEC) or Mobile Cloud Computing (MCC) servers. MEC servers are much closer to IoT devices and thus have lower latency, while MCC servers can provide flexible and scalable computing capability to support complicated applications. To address the tradeoff between limited computing capacity and high latency, and meanwhile, ensure the data integrity during the offloading process, we consider a blockchain scenario where edge computing and cloud computing can collaborate towards secure task offloading. We further propose a blockchain-enabled IoT-Edge-Cloud computing architecture that benefits both from MCC and MEC, where MEC servers offer lower latency computing services, while MCC servers provide stronger computation power. Moreover, we develop an Energy-Efficient Dynamic Task Offloading (EEDTO) algorithm by choosing the optimal computing place in an online way, either on the IoT device, the MEC server or the MCC server with the goal of jointly minimizing the energy consumption and task response time. The Lyapunov optimization technique is applied to control computation and communication costs incurred by different types of applications and the dynamic changes of wireless environments. During the optimization the best computing location for each task is chosen adaptively without requiring future system information as prior knowledge. Compared with previous offloading schemes with/without MEC and MCC cooperation, EEDTO can achieve energy-efficient offloading decisions with relatively lower computational complexity.

City Internet of Things (IoT) applications are becoming increasingly complicated and thus require large amounts of computational resources and strict latency requirements. Mobile Cloud Computing (MCC) is an effective way to alleviate the limitation of computation capacity by offloading complex tasks from mobile devices to central clouds. Besides, Mobile Edge Computing (MEC) is a promising technology to reduce latency during data transmission and save energy by providing services in a timely manner. However, it is still difficult to solve the task offloading challenges in heterogeneous cloud computing environments, where edge clouds and central clouds work collaboratively to satisfy requirements of city IoT applications. In this paper, we consider the heterogeneity of edge and central cloud servers in the offloading destination selection. To jointly optimize the system utility and the bandwidth allocation for each mobile device, we establish a hybrid offloading model including the collaboration of MCC and MEC. A Distributed Deep learning-driven Task Offloading (DDTO) algorithm is proposed to generate near-optimal offloading decisions over the mobile devices, edge cloud server and central cloud server. Experimental results demonstrate the accuracy of the DDTO algorithm, which can effectively and efficiently generate near-optimal offloading decisions in edge and cloud computing environments. Furthermore, it achieves high performance and greatly reduces the computational complexity when compared with other offloading schemes that neglect the collaboration of heterogeneous clouds. More precisely, the DDTO scheme can improve the computational performance by 63%, compared with the local-only scheme.