In recent years, constant developments in Internet of Things (IoT) generate large amounts of data, which put pressure on Cloud computing’s infrastructure. The proposed Fog computing architecture is considered the next generation of Cloud Computing for meeting the requirements posed by the device network of IoT. One of the obstacles of Fog Computing is distribution of computing resources to minimize completion time and operating cost. The following study introduces a new approach to optimize task scheduling problem for Bag-of-Tasks applications in Cloud–Fog environment in terms of execution time and operating costs. The proposed algorithm named TCaS was tested on 11 datasets varying in size. The experimental results show an improvement of 15.11% compared to the Bee Life Algorithm (BLA) and 11.04% compared to Modified Particle Swarm Optimization (MPSO), while achieving balance between completing time and operating cost.

With the appearance of more and more devices connected to the Internet, the world has witnessed an ever-growing number of data to be processed. Among those, many tasks require swift execution time, while the storage and computation capability of Internet of Things (IoT) devices are limited. To address the demands of delay-sensitive tasks, we present a vehicular edge–cloud computing (VECC) network that leverages powerful computation capabilities through the deployment of servers in proximity to task-generated devices, as well as the utilization of idle resources from smart vehicles to share the workload. Because these limited resources are vulnerable to sudden data arising, it is imperative to incorporate cloud servers to prevent system overload. The challenge now is to find a task offloading strategy that collaborates both edges and cloud resources to minimize the total time surpassing the quality baseline of each task (tolerance time) and make all tasks meet their soft deadlines of quality. To reach this goal, we first model the task offloading problem in VECC as a Markov decision process (MDP). Then, we propose advantage-oriented task offloading with a dueling actor-insulator network scheme to solve the problem. This value-based reinforcement learning (RL) method helps the agent find an effective policy when not knowing all the state attributes changes. The effectiveness of our method is demonstrated by performance evaluations based on real-world bus traces in Rio de Janeiro (Brazil). The experimental results show that our proposal reduces the tolerance time by at least 8.81% compared to other RL algorithms and 75% compared to greedy approaches.

In recent years, the utilization of fog cloud-based Internet of Things (IoT) applications has been steadily rising due to the exponential growth of data produced by interconnected smart devices. However, cloud providers who are responsible for these IoT applications face two critical problems: how to protect the system from untrusted users and how to allocate processing units to meet the demands with acceptable costs. The Fog-Cloud Blockchain System (FCB), proposed in past research, provides a perfect solution for the former question by integrating Blockchain’s security qualities into the Fog-Cloud paradigm. In this article, we address the latter question by proposing an improved version of the Life Choice-based Optimization algorithm (ILCO) to solve the task scheduling for Bag-of-Task applications in the FCB system. Task scheduling is one of the most prominent problems in resource allocation. Our proposed algorithm not only increases the convergence speed but also maintains diversity better, optimizing the FCB’s power, latency, and cost. Under a single-objective problem setting, ILCO outperforms LCO and similar state-of-the-art methods by achieving better results for FCB’s latency and power consumption.

Multi-access Edge Computing (MEC) is a promising technology to enhance the performance of latency-critical applications by providing additional computing capabilities at network edges. However, in traditional cellular-based MEC, users at the cell edge experience signal attenuation and inter-cell interference, resulting in increased risks of transmission outages and offloading failures. We introduce an integration of MEC functionalities and the User-centric Network, harnessing the strengths of both paradigms. The main objective is to minimize overall delay and energy consumption while adhering to specific delay and energy constraints. To address this problem, we formulate it as a Markov Decision Process and then propose a Generalized Advantage Policy for Resource allocation and Offloading strategy to jointly optimize task partitioning, transmit power control, and computing resource allocation. Simulation results demonstrate that the proposed optimization scheme significantly reduces energy consumption and delays for users, while also guaranteeing delay and battery constraints.