With the rapid growth of the Internet of Things (IoT), millions of devices have been interconnected within the network. Cloud computing and edge computing are jointly playing a crucial role in processing and analyzing the large number of tasks generated by end devices. Considering the latency and energy consumption, we propose a novel task offloading framework in collaborative edge-cloud computing. This framework first introduces tiered pricing for task offloading services inspired by electricity pricing. The tiered pricing strategies are transformed into continuous functions using the convex approximation method for the Sign Function. Then, we propose a task offloading strategy to optimize the utilities for both end devices and edge nodes. End devices offload tasks to edge nodes to minimize their task processing costs. After receiving the tasks, each edge node determines whether to offload them to the cloud center and in what proportion, aiming to maximize the profit. To facilitate efficient task offloading, we introduce a one-to-many matching algorithm to establish stable matches between end devices and edge nodes. Simulation results demonstrate that the utilities of end devices and edge nodes can converge within a certain number of iterations. We then analyze the impact of pricing on the strategies of edge nodes and end devices. We also compare the proposed algorithm with other algorithms, revealing that our algorithm achieves stable matching as well as effective load balancing of edge nodes.

The Industrial Internet of Things (IIoT) has revolutionized industrial processes by facilitating the seamless connectivity and communication of devices and systems within industrial environments. As a critical component, edge computing provides the flexible data sensing and real-time processing services, which facilitates a comprehensive enhancement for IIoT systems. However, due to the tremendous increase in IIoT devices and the limited resources of edge servers, ensuring efficient resource sharing while prioritizing the benefits of both parties would be the driving force in edge-enabled IIoT systems. In this work, we propose a distributed resource sharing mechanism in edge-enabled IIoT systems. The objective of proposed mechanism is maximizing social welfare which optimizes the utility of IIoT device and cost of edge server simultaneously. In particular, we integrate the soft-defined network (SDN) technique into the threelayer IIoT framework to support agile resource management and formulate the resource sharing optimization models in multiple scenarios. Furthermore, we design the distributed alternating direction method of multipliers (ADMMs) algorithms which appropriately decompose the primal problem into multiple subproblems for IIoT devices and edge servers, and explore their interactive relationships of resource requesting and sharing using the characteristic of ADMM. Finally, extensive performance evaluations are conducted under multiple IIoT scenarios to manifest the effectiveness and reliability of proposed distributed mechanism in terms of convergence rate, social welfare and fault tolerance.

Enterprises can reduce the computational burden and costs substantially by migrating and deploying their partial or even whole applications to the cloud, so as to promote and realize their digital transformation. In this article, we study the following problems in the migration and deployment of enterprise applications: i) How the migration time factor influences application migration indirectly? ii) What is the optimal deployment strategy for multiple applications? In this regard, many existing schemes that aim to optimize the economic cost can neither model the optimal migration strategy nor the optimal deployment resource allocation appropriately for enterprise applications. To tackle these limitations, first, this article aims at minimizing migration time by allocating the bandwidth of the access links for applications migration and formulates a strictly convex optimization problem. After that, the article concentrates on modelling the deployment interactions for resource allocation between enterprise application and cloud physical machines as a non-convex optimization problem. The successive approximation method is used to approximate the problem into a series of strictly convex optimization problems and an algorithm is proposed to achieve the optimal resource allocation for applications deployment problem. Numerical results illustrate the effective performance of the proposed schemes of enterprise application migration and deployment in comparison with other methods.