With the rapid advancement of cloud computing, cloud services have been widely adopted for managing large-scale and complex IoT workflow applications due to their robust computational capabilities. However, efficiently scheduling and deploying these workflows while ensuring quality-of-service (QoS) for diverse users remains a significant challenge for cloud service providers. In this study, we propose a novel multi-stage workflow scheduling algorithm (RE-ACO) for energy-efficient management of reliability-constrained IoT applications in cloud environments. The algorithm operates in three key stages: Task ordering by ACO, reliability constraint distribution with feedback information and energy-aware task assignment. The RE-ACO leverages ACO and an energy-aware task assignment strategy to optimize energy usage without compromising workflow reliability. First, the ACO algorithm determines the optimal task execution sequence. Next, a feedback-based reliability distribution method dynamically assigns sub-reliability constraints to individual tasks. Finally, each task is allocated to a virtual machine (VM) that minimizes energy consumption while meeting its sub-reliability requirement. Simulation results demonstrate that RE-ACO outperforms existing approaches, achieving the lowest energy consumption for reliability-constrained workflow scheduling compared to three benchmark algorithms.

Cloud computing is a potent platform for delivering high-quality computational services to intricate IoT applications. However, effective scheduling approaches are essential to meet application demands while maximizing cloud computing’s potential. In this study, we propose an innovative workflow scheduling method for addressing the cost-effective, deadline-constrained scheduling challenge of IoT applications in cloud computing systems. Our solution, the F-ACO algorithm, leverages a hybrid intelligence approach that combines Ant Colony Optimization (ACO) with a cost-driven heuristic strategy. The primary goal is to minimize workflow scheduling costs while ensuring that workflow deadlines are met. F-ACO introduces a deadline distribution method to derive task sub-deadlines, enabling dynamic adjustments for unscheduled tasks to meet workflow deadlines. Furthermore, we introduce an adaptive ACO-based task ordering mechanism with self-adaptive heuristic information to optimize task scheduling sequences, reducing search space redundancy and enhancing convergence speed. The approach includes a cost-driven task scheduling method designed to allocate each task to a virtual machine with minimal execution cost and idle time, further optimizing the overall workflow scheduling cost. To validate our F-ACO algorithm, we conducted numerous simulations using real-world workflows and compared its performance against state-of-the-art algorithms. Our experimental results affirm F-ACO’s competitive edge in effectively scheduling IoT applications in cloud computing environments.

Subspace identification (SID) has been widely used in system identification and control fields, since it can estimate system models while only relying on the input and output data using reliable numerical operations. However, the high-dimension Hankel matrices are involved to store these data and used to obtain the system models, which increases the computation amount of SID and makes SID unsuitable for the large-scale or real-time identification tasks. In this paper, a novel fast SID method based on cloud workflow processing approach and container technology is proposed to accelerate the traditional algorithm. First, a workflow establishment method of SID is designed to match the distributed cloud environment, based on the computational feature of each calculation stage. Second, a containerised cloud workflow processing system is established to execute the logic-and data-dependent SID workflow mission based on the Kubernetes system. Finally, the experiments show that the computation time is reduced by at most$91.6\%$for the large-scale SID mission and decreased to within 20 ms for the real-time mission parameter.Note to Practitioners—Subspace identification has became a widely used method in various fields, including power grids, chemical processing, data-driven control, and fault detection. However, as systems become larger and more complex, the computational challenges increase. To address this issue, this paper proposes a workflow-based method for subspace identification that can be executed in a cloud environment to accelerate the process. This note outlines the steps that practitioners can take to apply this method. The first step is to design a workflow structure as proposed method in this paper. This structure should be customized to fit the specific needs of the practitioner’s application. The second step is to build a containerized cloud workflow processing system that can execute the workflow. This system should be based on the Kubernetes system and designed to handle the specific computational requirements of the workflow. Practitioners who work in fields where computational efficiency is crucial for system identification operations can benefit from the proposed method. By following the steps outlined above, practitioners can streamline the process of subspace identification and achieve improvements in computational efficiency.

Cloud workflow scheduling is a typical combinatorial optimization problem and becomes more challenging due to the increasing diversity of workflows. However, current research employs the same scheduling strategy on diverse workflows. In fact, a scheduling strategy may perform well on one workflow but poorly on other workflows owning to the unique characteristics of each workflow. Therefore, in practical applications, selecting suitable scheduling strategies for diverse workflows is a critical issue. To solve it, this paper investigates a diverse workflows scheduling problem and presents a classification-based workflow scheduling framework, which includes workflow parser, workflow classifier, workflow scheduler, resource manager and workflow status tracker, to manage and schedule diverse workflows using suitable strategies. Based on the framework, we propose a classification-based workflow scheduling algorithm (CWSA) to optimize the economic cost of workflow execution under deadline constraints. We conduct the experiments using diverse workflow instances randomly generated from five types of real-world workflows to evaluate the proposed CWSA approach. The results demonstrate the superiority of CWSA compared with the state-of-the-art approaches. Note to Practitioners—Diverse workflows (i.e., many workflows with various types, such as Montage, LIGO and Cybernetics) in clouds are widespread. How to efficiently schedule them in cloud is very important. This paper formulates the diverse workflows scheduling problem and proposes a CWSA to solve it. The basic idea of CWSA is to select a suitable scheduling strategy for each workflow. Specifically, in CWSA, we design a classification neural network architecture that consists of a graph neural network and a fully connected neural network to classify each workflow to its suitable deadline distribute strategy by its characteristics and deadline constraint. Then CWSA obtains the sub-deadlines of tasks and assigns tasks to appropriate VMs (Virtual Machines). Furthermore, as an important factor in workflow scheduling, the transmission time between dependent tasks is introduced into the graph neural network, which improves the classification accuracy.

Nowadays, more and more workflows with different computing requirements are migrated to clouds and executed with cloud resources. In this work, we study the problem of stochastic multi-workflows scheduling in clouds and formalize this problem as an optimization problem that is NP-hard. To solve this problem, an efficient stochastic multi-workflows dynamic scheduling algorithm called SMWDSA is designed to schedule multi-workflows with deadline constraints for optimizing multi-workflows scheduling cost. The proposed SMWDSA consists of three stages including multi-workflows preprocessing, multi-workflow scheduling and scheduling feedback. In SMWDSA, a novel task sub-deadlines assignment stretagy is design to assign the task sub-deadlines to each task of multi-workflows for meeting workflow deadline constraints. Then, we propose a task scheduling method based on the minimal time slot availability to execution task for minimizing workflow scheduling cost while meetingt workflow deadlines. Finally, a scheduling feedback strategy is adopted to update the priorities and sub-deadlines of unscheduled tasks, for further minimizing workflow scheduling cost. We conduct the experiments using both synthetic data and real-world data to evaluate SMWDSA. The results demonstrate the superiority of SMWDSA as compared with the state-of-the-art algorithms. Note to Practitioners—Workflow scheduling in clouds is significantly challenging due to not only the large scale of workflows but also the elasticity and heterogeneity of cloud resources. Moreover, minimizing workflow scheduling cost and satisfying workflow deadlines are two critical issues in scheduling with cloud resources, especially the uncertainty of workflow arrive time and task execution time are considered. To meet workflow deadlines, it is an effective strategy to decompose workflow deadline constraints into task sub-deadline constraints. To minimize the workflow scheduling cost, each task in a workflow needs to be assigned to their most suitable VMs for execution. This article presents a novel workflow scheduling algorithm to schedule stochastic multi-workflows in clouds for optimizing multi-workflows scheduling cost and meeting workflows deadlines. This algorithm obtains the task sub-deadline constraints based on the characteristics of workflows for meeting the worklfow deadline constraint. Under the premise of meeting task deadlines, it schedules tasks to a VM with minimum the slot time, for minimizing the cost. Case studies based on well-known real-world workflows data sets suggest that it outperforms traditional ones in terms of success and cost of multi-workflows scheduling. It can thus aid the design and optimization of multi-workflows scheduling in a cloud environment. It can help practitioners better manage the scheduling cost and performance of real-world applications built upon cloud services.

With the migration of more and more workflows to clouds, the workflow scheduling in clouds (WSC) becomes a critical problem. Although many algorithms have been presented for WSC, there is still room and need for improvement. This paper formulates WSC as a constrained optimization problem that optimizes workflow execution cost within a workflow deadline constraint and proposes a fully hybrid workflow scheduling algorithm, called HPCP-PSO to solve it. Unlike previous works, HPCP-PSO is based on the repeated and alternated execution of two different methods, namely, the heuristic IaaS Cloud Partial Critical Paths (IC-PCP) and meta-heuristic Particle Swarm Optimization (PSO). Moreover, HPCP-PSO incorporates with two novel designs: 1) a new solution encoding strategy not only to sufficiently embody the elasticity of cloud resources, but also to reflect the scheduling relationship between assigned and unassigned tasks; 2) a solution repair strategy on each infeasible lease process to utilize a user-defined deadline more effectively and enhance the solution efficiency of the algorithm. Extensive experiments are conducted on four real-world scientific workflows and the results show that compared with IC-PCP, PSO, and HGSA, the proposed algorithm outperforms them on average by 35.83%, 70.53%, and 87.71% in terms of workflow execution cost.

Cloud container services as the new norm of cloud resource provisioning are more flexible and widely used in workflows scheduling. However, it is challenging to minimize the cost for workflows scheduling in cloud container services, especially when workflows arrive time and tasks execution time are uncertain. In this article, a stochastic hybrid workflows [i.e., off-line batch workflows (DIWs) and online stream workflows (DSWs)] scheduling problem in cloud container services is solved. A stochastic hybrid workflows scheduling system (SHWS), which consists of a workflow analyzer, workflow classifier, runtime estimator, workflow scheduler, and resource manager, is designed to manage and schedule DIWs and DSWs. Based on the SHWS, a stochastic hybrid workflows scheduling algorithm (SHWSA) is proposed to minimize the cost and improve resource utilization. We conduct the experiments using both synthetic data and real-world data to evaluate the proposed SHWSA approach. The results demonstrate the superiority of SHWSA compared with the state-of-the-art algorithms.Note to Practitioners—This article investigates a stochastic hybrid workflows scheduling problem in cloud container services. We propose a stochastic hybrid workflows scheduling algorithm, which is named SHWSA. The SHWSA is designed to jointly schedule off-line batch workflows (DIWs) and online stream workflows (DSWs) for minimizing the cost and improving resource utilization in cloud container services. The basic idea is to assign tasks subdeadlines and prioritize tasks for guaranteeing workflows deadlines constraints and the processing dependence requirements of tasks. Experiments show that SHWSA outperforms some state-of-the-art algorithms.