In the era of the Internet of Things, the significant increase in data volume, time, and space complexity presents great challenges to workflow scheduling in resource-constrained clouds. This study proposes an efficient hybrid algorithm, denoted white shark optimization (WSO) algorithm with budget constraints (BC-WSO), designed to adhere to budget constraints. The primary objective of BC-WSO is to optimize the scheduling length and cost. This objective is achieved by employing a heuristic algorithm that utilizes the predicted makespan matrix (PMMS) alongside the WSO algorithm as its foundation. The PMMS can minimize the scheduling length of a workflow application and satisfy the task prioritization dependencies. BC-WSO incorporates PMMS into the population initialization phase to improve the accuracy of WSO and accelerate the convergence process. Extensive experiments in two real-world scientific workflow applications show that BC-WSO outperforms current state-of-the-art meta-heuristic algorithms in simultaneously optimizing scheduling length and cost.

As the demand for Big Data analysis and artificial intelligence technology continues to surge, a significant amount of research has been conducted on cloud computing services. An effective workflow scheduling strategy stands as the pivotal factor in ensuring the quality of cloud services. Dynamic voltage and frequency scaling (DVFS) is an effective energy-saving technology that is extensively used in the development of workflow scheduling algorithms. However, DVFS reduces the processor's running frequency, which increases the possibility of soft errors in workflow execution, thereby lowering the workflow execution reliability. This study proposes an energy-aware reliability enhancement scheduling (EARES) method with a checkpoint mechanism to improve system reliability while meeting the workflow deadline and the energy consumption constraints. The proposed EARES algorithm consists of three phases, namely, workflow application initialization, deadline partitioning, and energy partitioning and virtual machine selection. Numerous experiments are conducted to assess the performance of the EARES algorithm using three real-world scientific workflows. Experimental results demonstrate that the EARES algorithm remarkably improves reliability in comparison with other state-of-the-art algorithms while meeting the deadline and satisfying the energy consumption requirement.

Mobile edge computing effectively enhances service quality and decreases system cost by processing resource-intensive tasks at the network edge. Today, unmanned aerial vehicles (UAVs) are increasingly being utilized for task offloading services in remote areas due to their convenient deployment and flexible mobility. However, the complex task environment when using UAVs brings great challenges to the optimization strategy’s capacity to solve and converge in a stable manner. To solve this issue, a differential rate rule (DRR) is proposed in this work with the goal of improving the update stability of the agent in the actor–critic reinforcement learning (RL). Second, a UAV-assisted task offloading algorithm called DSUTO is designed based on DRR and maximum entropy RL. Finally, a UAV-assisted mobile device-edge-cloud collaborative computing model is constructed with time-varying channel obstacles and user movement, thus solving a multi-objective joint optimization problem on the task completion cost (including delay and energy consumption) and UAV endurance under resource constraints. The experiment results demonstrate that DSUTO not only has excellent performance in terms of convergence and stability, but also significantly reduces the total system cost by 21.38% compared with the latest benchmark algorithms under complex environment conditions.