The development of computer architecture towards multi-core processors brings new opportunities and challenges for efficient task scheduling in scientific workflows. This paper investigates the key challenges of scheduling scientific workflows, typically represented as Directed Acyclic Graphs (DAGs), in multi-core and dynamic distributed computing environments. Traditional static and dynamic scheduling algorithms face limitations in handling complex DAG structures and complex computational environments. To address these issues, we propose an improved LinearPB algorithm that simplifies the node priority calculation through linear evaluation, thereby reducing computational overhead and enhancing scheduling performance. Our extensive experiments demonstrate that the LinearPB algorithm significantly reduces execution time, outperforms existing algorithms in most cases.

Cloud-fog architecture, as a hybrid distributed architecture, combines the powerful computing capabilities of cloud computing with the low-latency of fog computing, providing more efficient data processing services for Internet of Things (IoT) applications. In cloud-fog environment, efficient workflow scheduling algorithms are crucial to ensure the Quality of Service (QoS) of user applications, especially in terms of response time and energy consumption. To this end, this paper proposed an algorithm named Priority-Based Genetic Algorithm using deep learning model (PBGA). PBGA uses heuristic methods in the task sorting phase and introduces a modified genetic algorithm using a deep learning model to allocate resources to tasks. Additionally, the algorithm addresses resource overload by incorporating a task migration strategy. Experimental results indicate that compared to benchmark algorithms, PBGA achieves improvements of 3.5% to 57.4% in response time and 2.3% to 28.3% in energy consumption.

In a typical distributed computing system, as the availability of resources and the precise execution time of different calculations are usually difficult to predict, how to effectively schedule complex calculations composed of interdependent tasks has become a challenge. The priority-based (PB) scheduling scheme, which is designed to maximize the parallelism of ready tasks, has shown better performance than other existing algorithms. However, the PB algorithm has the problem of excessive computing overhead and long running time in some cases. To address this issue, this paper proposes the priority-based level (PBL) algorithm. Experiments results show that the PBL , in comparison with their counterparts, manages to significantly reduce the algorithm running overhead while maintaining the scheduling results.

Abstract Nowadays, scientists are dealing with large‐scale scientific workflows that need a high processing capacity platform to facilitate on‐time completion. Cloud computing is the ideal platform to overcome this problem as it has several resources that scientists may choose from depending on the size of their applications. However, using cloud computing requires some monetary charges. Recently cloud computing providers started a new pricing schema that offers to their users a set of resources with specific combinations of CPU frequency configurations settings and price. The selected configurations settings reflect energy consumption. Besides, the configuration selection to meet users' satisfaction (minimum cost) and providers' satisfaction (energy saving) is crucial. Therefore, a multiobjective (cost and energy) efficient mechanism is essential. In this article, we address an important novel problem concerning multiobjective deadline constrained workflow scheduling in the cloud. We first study the relationship between cost minimization and minimization of the energy consumption in a cloud environment, and then discuss, develop, and propose an algorithm with two variants to help the system satisfy both sides (users and providers) at the same time during the selection of the configuration. The proposed heuristic is evaluated using specified real‐world applications. The observed results indicate that our heuristic can reduce significantly the energy consumption and the cost at the same time.

There has been an increasing interest of integrating blockchain into cyber-physical systems (CPS). The design of password hashing schemes (PHSs) is in the core of blockchain security. However, no existing PHS seems to meet both the requirements of sufficient security and small code size for blockchain-based CPSs. In this article, a novel memory-hard PHS based on the classic PBKDF2 is proposed. Evaluation results show that the proposed scheme is promising for blockchain-based CPS, as it manages to provide enhanced security in comparison to PBKDF2 with limited increase in code size.

Nowadays, cloud computing providers offer to their users the computing resources that are capable of operating on the CPU frequency in between minimum and maximum values. This gives to the users a big number of Virtual Machine(VM) configurations to choose from when planning for the execution of their applications. On the user side, higher CPU frequency incurs a high monetary cost, on the providers side higher CPU frequency incurs high energy consumption. The reduction rate of cost and the reduction rate of energy differ from each other. A big challenge that arises is how to select the proper CPU frequency that can lead to energy-cost-efficient configuration and strike a good balance between energy-cost and deadline. In this paper, an algorithm that achieves energy-cost-aware VM provisioning by selecting different CPU frequencies for each VM in order to execute workflow within a deadline is presented.

In this paper, we assume workflows under deadline constraints are submitted to the cloud from time to time. Every time a workflow is submitted, the cloud needs to determine whether it can agree with the specific constraint set by the user. If the cloud agrees to admit the workflow, cloud resources can be allocated for its execution in a way the deadline constraint can be met, while the existing load in the underlying resources is considered. The focus of this paper is how to schedule the tasks of each admitted workflow so that the resource utilization can be maximized. A variety of online scheduling algorithms have been proposed and evaluated using a simulator that manages to generate a stream of workflows for which an optimal schedule, with 100% resource utilization and without deadline violation, is guaranteed to exist.

Summary Nowadays, cloud service providers usually offer users virtual machines with various combinations of configurations and prices. As this new service scheme emerges, the problem of choosing the cost‐minimized combination under a deadline constraint is becoming more complex for users. The complexity of determining the cost‐minimized combination may be resulted from different causes: the characteristics of user applications and providers' setting on the configurations and pricing of virtual machine. In this paper, we proposed an algorithm with two variants to help the users to schedule their workflow applications on clouds so that the cost can be minimized and the deadline constraints can be satisfied. The proposed algorithm is evaluated by extensive simulation experiments with two realistic workflows.

In practical Cloud/Grid computing systems, DAG scheduling may be faced with challenges arising from severe uncertainty about the underlying platform. For instance, it could be hard to have explicit information about task execution time and/or the availability of resources, both may change dynamically, in difficult to predict ways. In such a setting, the development of various kinds of just-in-time scheduling schemes, which aim at maximizing the parallelism of ready tasks of DAG, seems to be a promising approach to cope with the lack of environment information and achieve efficient DAG execution. Although many attempts have been tried to develop such just-in-time scheduling heuristics, most of them are based on DAG decomposition, which results in complicated and suboptimal solutions for general DAGs. This paper presents a priority-based heuristic, which is not only easy to apply to arbitrary DAGs, but also exhibits comparable or better performance than the existing solutions.