With increasing complex workflow application and computational resources requirement, distributed computing has attracted growing attention. Meanwhile, cloud computing has emerged as a prominent solution due to its elasticity, heterogeneity, and on-demand capabilities. However, data security and execution reliability in cloud are still urgent issues that need to be addressed. Based on the data encryption and task redundancy mechanism, this paper presented a many-objective workflow scheduling problem (RSWSP) with the objectives of minimizing the execution time, cost, risk, and non-reliability. Then, a two-stage learning-driven many-objective memetic algorithm (TMMA) with tailored designs is introduced to address the RSWSP. First, several problem-specific heuristics are employed for cooperative initialization, generating a diverse set of initial solutions. Second, a two-stage global diversification approach is implemented to explore the problem space, which clusters the population into sub-populations and adoptive selects leader solutions based on the state of the population. In addition, a learning-driven local intensification strategy is incorporated for exploitation, encompassing six neighbor search operators and a Q-learning-based selection mechanism. Extensive experiments have been conducted to validate the performance of TMMA. The statistical comparison reveals that the TMMA is superior to state-of-the-art algorithms in solving the RSWSP in terms of solution quality and robustness.

The cloud workflow scheduling problem has important applications in modern commercial and industrial areas. In the public cloud environment, the workflow suffers from security threats because of the multiple tenants and the distribution of computational resources. This paper models cloud workflow scheduling as a novel multi-objective optimization problem that aims to minimize execution time, cost, and risk. Due to the complexity of the considered problem, a multi-objective cluster-based cooperative co-evolutionary (CBCC) algorithm with several novel designs is proposed. First, a new initialization strategy is presented to generate potential non-dominated solutions. Based on the cluster-based multi-objective optimization framework, a novel collaboration model is proposed, and it adopts four populations to address the subproblems, respectively. Moreover, a diversification strategy is designed to maintain the diversity of the global archive. Furthermore, a problem-specific intensification strategy is designed to intensify the potential solutions. A comprehensive computational and statistical campaign was carried out to verify the performance of CBCC. The results show that the proposed CBCC outperforms several meta-heuristics adapted from closely related scheduling models in the literature by a significantly considerable margin.Note to Practitioners—This paper describes a novel approach called CBCC for minimizing the cost, time, and risk when scheduling a workflow in the cloud environment. CBCC seamlessly combines the cluster-based multi-objective optimization framework and several problem-specific components such as initialization, diversification, and intensification strategies. As the considered problem has not been previously addressed in the literature, five state-of-the-art algorithms for closely related problems, which include I_MaOPSO (improved many objective particle swarm optimization), EMS-C (evolutionary multi-objective scheduling for cloud), ch-PICEA-g (enhanced multi-objective co-evolutionary algorithm), VaEA (vector angle-based evolutionary algorithm), and DQN-based MARL (Deep-Q-network-based Multi-agent Reinforcement Learning) are adopted as baselines. The results demonstrate that CBCC significantly outperforms the baselines with a 95% confidence level.

Uncertainty is a practical issue in system design optimization because some characteristics of components, such as reliability and cost, cannot be determined precisely in many situations. Considering the imprecise characteristics of components, few works have focused on the multi-objective optimization for the redundancy allocation due to the challenges of comparing multi intervals. To tackle the issue, a novel angle-based bi- objective redundancy allocation algorithm is proposed in this study, introducing three original contributions: 1) An angle-based interval crowding distance (ICA) is especially designed for effective performance and reduced computational time; 2) Two techniques are applied to tackle the problem: An elite selection for mutation is presented for generating better offsprings; A penalty-guided constraint handling technique is introduced for converting the problem into an unconstrained one. 3) Since a set of optimal solutions is obtained by the proposed method and no preference on uncertainties is provided, this paper proposes a novel knee interval method to help DMs make a decision. To be specific, the proposed ICA can describe the distribution of the whole population intuitively and effectively, considering not only the angle between two compared individuals but also the angle range of the interval values. The computational results from two typical experiments demonstrate that the proposed algorithm is more efficient than other state-of-the-art algorithms, generating Pareto sets with less repeating individuals, stronger convergence, wider distribution, less imprecision, and reduced computational time. Note to Practitioners—This article is motivated by two practical problems in multi-objective redundancy allocation in presence of interval uncertainty: First, this paper tries to solve the multi-objective redundancy allocation problem with the imprecise characteristics of components, which is rarely considered in the field of reliability optimization design. Second, the calculation of the crowding distance needs extra time cost and is less efficient. To tackle this issue, an interval crowding angle is especially designed, considering not only the angle between two compared individuals, but also the angle range of the interval values. The proposed method can be embedded in most multi-objective interval evolutionary algorithms to compute the diversity of the individuals. The goal of this study is to allocate the economy and high-reliable components for practitioners. The computational results verify its effectiveness and efficiency. Besides, in many cases the practitioners know only few or no preferences, this paper proposes a knee point analysis of interval values that allows practitioners to select the optimal solution with large hypervolume and less imprecision among a set of solutions.

Workflow scheduling in cloud environments has become a significant topic in both commercial and industrial applications. However, it is still an extraordinarily challenge to generate effective and economical scheduling schemes under the deadline constraint especially for the large scale workflow applications. To address the issue, this article investigates the cloud workflow scheduling problem with the aim of minimizing the whole cost of workflow execution whereas maintaining its execution time under a predetermined deadline. A novel knowledge-based adaptive discrete water wave optimization (KADWWO) algorithm is developed based on the problem-specific knowledge of cloud workflow scheduling. In the proposed KADWWO, a discrete propagation operator is designed based on the idle time knowledge of hourly-based cost model to adaptively explore the huge search space. The adaptive refraction operator is employed to avoid stagnation and expand the available resource pool. Meanwhile, the dynamic grouping based breaking operator is designed to exploit the excellent block structure knowledge of task allocation scheme and corresponding resource to intensify the local region and accelerate convergence. Extensive simulation experiments on the well-known scientific workflow demonstrate that the KADWWO approach outperforms several recent state-of-the-art algorithms.

In consideration of the uncertainty of the scientific workflows, an interval-based multi-objective cloud workflow scheduling problem is investigated, which widely exists in the cloud environment. This problem aims at allocating the workflow to the public cloud environment. The uncertain workload and communication data of workflow as well as the processing ability and bandwidth of the resources are represented by an interval number, which models the uncertainty of these variables. The objectives are to minimize the total execution time and cost. To address this problem, a discrete interval-based multi-objective memetic algorithm (DIMOMA) is proposed. A hybrid initial strategy is employed to generate the potential population. With the contribution-based selection mechanism, the self-adaptive genetic operators are designed to perform a global search in the problem space. Then, a novel local search procedure is incorporated to perform intensification and accelerate the convergence. A comprehensive computational experiment and comparisons with several meta-heuristics adapted from the related problems are conducted based on an extended benchmark set. The simulated results reveal that the proposed method can achieve better trade-off fronts between the execution time and cost of workflow. On the performance metric hypervolume which measures both execution time and cost, the proposed DIMOMA can improve by 3.90%, 9.30%, 6.25%, and 7.74% compared with EMS-C, MOACS, ch-PICEA-g, and I_MaOPSO, respectively. Besides, DIMOMA can achieve better robustness, which means the difference between the lower and upper bounds of the execution time and cost of the solutions obtained by DIMOMA are over smaller than the state-of-the-art algorithms.

With the development of cloud computing, multi-cloud systems have become common platforms for hosting and executing workflow applications in recent years. However, the complexity of workflow scheduling increases exponentially because of the diversified billing mechanisms, heterogeneous virtual machines, and reliability of multi-cloud systems. This article focuses on a multi-objective workflow scheduling problem in multi-cloud systems (MOWSP-MCS). The makespan, cost, and reliability are considered the optimization objectives from the perspective of users. Compared with the classical multi-objective workflow scheduling in the cloud environment, MOWSP-MCS allows users to apply the backup technique to improve reliability. To solve the MOWSP-MCS, this article proposes a reliability-aware multi-objective memetic algorithm (RA-MOMA) containing a diversification strategy and intensification strategy. In the diversification strategy, several problem-specific genetic operators are introduced to construct the diversified offspring individuals. In the intensification strategy, four problem-specific neighborhood operators are designed based on the critical path and resource utilization rate to improve the quality of the individuals in the archive set. A comprehensive numerical experiment is conducted to evaluate the effectiveness of RA-MOMA. The comparisons with several related algorithms demonstrate the superiority of RA-MOMA for solving the MOWSP-MCS.