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Cloud computing provides the on-demand service of the user with the use of distributed physical machines, in which security has become a challenging factor while performing various tasks. Several methods were developed for the cloud computing workflow scheduling based on optimal resource allocation; still, the security consideration and efficient allocation of the workflow are challenging. Hence, this research introduces a hybrid optimization algorithm based on multi-objective workflow scheduling in the cloud computing environment. The Regressive Whale Water Tasmanian Devil Optimization (RWWTDO) is proposed for the optimal workflow scheduling based on the multi-objective fitness function with nine various factors, like Predicted energy, Quality of service (QoS), Resource utilization, Actual task running time, Bandwidth utilization, Memory capacity, Make span equivalent of the total cost, Task priority, and Trust. Besides, secure data transmission is employed using the triple data encryption standard (3DES) to acquire enhanced security for workflow scheduling. The method's performance is evaluated using the resource utilization, predicted energy, task scheduling cost, and task scheduling time and acquired the values of 1.00000, 0.16587, 0.00041, and 0.00314, respectively. [ABSTRACT FROM AUTHOR]

Workflow scheduling and resource provisioning are two of the most critical issues in cloud computing. Developing an optimal workflow scheduling strategy in the heterogeneous cloud environment is extremely difficult due to its NP-complete nature. Various optimization algorithms have been used to schedule the workflow so that users can receive Quality of Service (QoS) from cloud service providers as well as service providers can achieve maximum gain but there is no such model that can simultaneously minimize execution time and cost while balancing the load among virtual machines in a heterogeneous environment using JAYA approach. In this article, we employed the hybrid JAYA algorithm to minimize the computation cost and completion time during workflow scheduling. We considered the heterogeneous cloud computing environment and made an effort to evenly distribute the load among the virtual machines. To achieve our goals, we used the Task Duplication Heterogeneous Earliest Finish Time (HEFT-TD) and Predict Earliest Finish Time (PEFT). The makespan is greatly shortened by HEFT-TD which is based on the Optimistic Cost Table. We used a greedy technique to distribute the workload among Virtual Machines (VMs) in a heterogeneous environment. Greedy approach assigns the upcoming task to a VM which have lowest load. In addition, we also considered performance variation, termination delay, and booting time of virtual machines to achieve our objectives in our proposed model. We used Montage, LIGO, Cybershake, and Epigenomics datasets to experimentally analyze the suggested model in order to validate the concept. Our meticulous experiments show that our hybrid approach outperforms other recent algorithms in minimizing the execution cost and makespan, such as the Cost Effective Genetic Algorithm (CEGA), Cost-effective Load-balanced Genetic Algorithm (CLGA), Cost effective Hybrid Genetic Algorithm (CHGA), and Artificial Bee Colony Algorithm (ABC).

In recent years,due the advantages of on-demand resource provisioning and pay-as-you-go billing model,it is increa-singly popular to execute large-scale workflow applications in cloud environments.Cloud service providers offer resources with different capabilities at different prices.In order to improve resource utilization,many cloud service providers provide transient resources at a much lower price than normal resources.Spot instance provided by Amazon EC2 can greatly reduce the execution cost of workflow.One of the main problems of workflow scheduling in cloud is to find a cheaper scheduling method on the premise of meeting the deadline.To solve this problem,a deadline constrained scheduling optimization algorithm for workflow in clouds using spot instance(Spot-ProLis) is proposed.The algorithm takes into account the case that the data transmission time of the same virtual machine is zero,and uses the method of probabilistic upward rank to order tasks.In the resource allocation stage,spot instances are added as candidate resources,which effectively reduces the execution cost.Experiment results show that compared with the classical ProLis algorithm,Spot-ProLis has significant advantages in reducing the execution cost.

With the development of current computing technology, workflow applications have become more important in a variety of fields, including research, education, health care, and scientific experimentation. A group of tasks with complicated dependency relationships constitute the workflow applications. It can be difficult to create an acceptable execution sequence while maintaining precedence constraints. Workflow scheduling algorithms (WSA) are gaining more attention from researchers as a real-time concern. Even though a variety of research perspectives have been demonstrated for WSAs, it remains challenging to develop a single coherent algorithm that simultaneously meets a variety of criteria. There is very less research available on WSA in the heterogeneous computing system. Classical scheduling techniques, evolutionary optimisation algorithms, and other methodologies are the available solution to this problem. The workflow scheduling problem is regarded as NP-complete. This problem is constrained by various factors, such as Quality of Service, interdependence between tasks, and user deadlines. In this paper, an efficient meta-heuristic approach named Multi-objective Artificial Algae (MAA) algorithm is presented for scheduling scientific workflows in a hierarchical fog-cloud environment. In the first phase, the algorithm pre-processes scientific workflow and prepares two task lists. In order to speed up execution, bottleneck tasks are executed with high priority. The MAA algorithm is used to schedule tasks in the following stage to reduce execution times, energy consumption and overall costs. In order to effectively use fog resources, the algorithm also utilises the weighted sum-based multi-objective function. The proposed approach is evaluated using five benchmark scientific workflow datasets. To verify the performance, the proposed algorithm's results are compared to those of conventional and specialised WSAs. In comparison to previous methodologies, the average results demonstrate significant improvements of about 43% in execution time, 28% in energy consumption and 10% in total cost without any trade-offs. [ABSTRACT FROM AUTHOR]

In recent periods, cloud-computing environments are widely utilizing scientific workflows for executing large-scale applications. The workflow scheduling with the scientific standard for optimizing quality of service (QoS) parameters is a hard task. In the existing research studies, several metaheuristics optimization algorithms are employed for satisfying the QoS parameters such as resource utilization, cost, and makespan. Still, the existing metaheuristics optimization algorithms are insignificant for maintaining the balance between exploitation and exploration in a search space, because the algorithms are easily trapped in local optima. For addressing the above-stated issues, a data aware based adaptive gravitational search algorithm (DA-AGSA) technique is implemented to minimize the cost and makespan, and to schedule workflows in the cloud-computing platform. In the conventional GSA technique, a random coefficient is replaced by an adaptive weight function for improving convergence rate, and further, the weight function is multiplied with an acceleration term for facilitating quicker convergence. In this article, the performance of the DAAGSA technique is validated by utilizing workflow sim for scheduling multiple workflows. An extensive experimental investigation showed that the DA-AGSA technique almost reduced 20% of the cost and 15% of the makespan compared to the conventional optimization algorithms on the Montage, CyberShake, and Epigenomics workflows with 1000 tasks. In addition, the DA-AGSA technique achieved a reliability of 0.99, 0.98, and 0.98 on the Montage, CyberShake and Epigenomics workflows with 1000 tasks. [ABSTRACT FROM AUTHOR]

Quickly identifying astronomical transients from synoptic surveys is critical to many recent astrophysical discoveries. However, each of the data processing pipelines in these surveys contains dozens of stages with highly varying time and space requirements. Properly predicting the resources required to run these pipelines is critical for the allocation of computing resources and reducing the discovery response time. We propose a machine learning strategy for this prediction task and demonstrate its effectiveness using a set of timing measurements from the intermediate Palomar Transient Factory (iPTF) workflow. The proposed model utilizes the spatiotemporal correlation of astronomical images, where nearby patches of the sky (space) are likely to have a similar number of objects of interest and workflows executed in the recent past (time) are likely to use a similar amount of time because the machines and data storage systems are likely to be in similar states. We capture the relationship among these spatial and temporal features in a Bayesian network and study how they impact the prediction accuracy. This Bayesian network helps us to identify the most influential features for predictions. With proper features, our models achieve errors close to the random variance boundary within batches of images taken at the same time, which can be regarded as the intrinsic limit of prediction accuracy.

Quickly identifying astronomical transients from synoptic surveys is critical to many recent astrophysical discoveries. However, each of the data processing pipelines in these surveys contains dozens of stages with highly varying time and space requirements. Properly predicting the resources required to run these pipelines is critical for the allocation of computing resources and reducing the discovery response time. We propose a machine learning strategy for this prediction task and demonstrate its effectiveness using a set of timing measurements from the intermediate Palomar Transient Factory (iPTF) workflow. The proposed model utilizes the spatiotemporal correlation of astronomical images, where nearby patches of the sky (space) are likely to have a similar number of objects of interest and workflows executed in the recent past (time) are likely to use a similar amount of time because the machines and data storage systems are likely to be in similar states. We capture the relationship among these spatial and temporal features in a Bayesian network and study how they impact the prediction accuracy. This Bayesian network helps us to identify the most influential features for predictions. With proper features, our models achieve errors close to the random variance boundary within batches of images taken at the same time, which can be regarded as the intrinsic limit of prediction accuracy.

At present, workflow scheduling in cloud computing environment is still a challenging optimization topic due to its NP-complete characteristics. In order to obtain better scheduling results, researchers are constantly coming up with new methods. In this study, we offer a hybrid metaheuristic for solving workflow scheduling in cloud to minimize the makespan of the workflow considering the heterogeneity of virtual resources. This hybrid approach combines the excellent optimization properties of Heterogeneous Earliest Finish Time (HEFT), Teaching–Learning-Based Optimization (TLBO), Opposition-Based Learning (OBL), and genetic manipulations, which is named Hybrid TLBO (HTLBO). Firstly, a HEFT-based method is proposed to produce the high-quality diverse initial population. Secondly, a Mixed OBL (MOBL) model is designed, in which the boundary search information and the population historical search information are systematically taken into account. Finally, an enhanced learner stage using genetic operations are added to effectively help the algorithm to jump out of the local optima. Rigorous experiments over various scientific workflows are conducted to validate HTLBO’s performance. The obtained results are compared to HEFT and some state-of-the-art hybrid metaheuristics in terms of average makespan, running time and non-parametric statistics. A significant improvement in schedule quality demonstrates that HTLBO can increase population diversity and achieve a good balance between scheduling effectiveness and efficiency.

Hyper-heuristics have a high level of generality and adaptability, allowing them to effectively solve a wide range of complex optimization problems. With reinforcement learning, hyper-heuristics can use experience and knowledge gained to tackle unforeseen problems, allowing hyper-heuristics to adapt and improve over time. Our paper proposes a framework for using policy-based reinforcement learning to improve the performance of hyper-heuristics. The framework trains hyper-heuristic agents to select the best generalized constructive low-level heuristics to solve combinatorial optimization problems. The framework evaluation was performed using three benchmarking problems: traveling salesman, capacitated vehicle routing, and bin packing problems. The results showed that the proposed framework can outperform existing meta-heuristic and hyper-heuristic-based algorithms for all large problem instances in all problem domains. The proposed framework was also evaluated by applying it to a cost optimization problem for workflow scheduling on a hybrid cloud with a deadline constraint. Eight agents were trained on medium-sized workflows with two deadlines and tested against traditional meta-heuristic and hyper-heuristic methods to solve smaller and larger workflows with unforeseen deadlines. Four workflow applications, three workflow sizes, and three deadlines were used in the evaluation. The results showed that our proposed framework provided significantly better solutions of up to 98% for benchmarking problems, and up to 22% for cost optimization in workflow scheduling. Moreover, trained with small problem instances, the framework performed well for unforeseen larger problem instances implying its generalization. The proposed framework, thus, has the potential to improve both the generality and performance of solving large combinatorial optimization problems.

Cloud computing is a promising platform for running massive workflow applications based on a pay-per-use model. In cloud computing, the reduction of energy consumption and providing security to workflow scheduling are the key research areas. The primary focus of the existing algorithms, viz., particle swarm optimization (PSO), crow Search optimization (CSO) and other non-metaheuristic algorithms like Round Robbin (RR), SJF, Min-Min, Min-Max etc., is based on the execution time and cost of the workflow applications as a budget constraint. However, these algorithms failed to adequately determine energy consumption, resource utilization, and security in workflow scheduling. To address this issue, a multi-objective scheduling framework is proposed. In this paper, the framework performs dynamic workflow scheduling using universal unique identification- Blake (UUID-Blake), Manhattan Distance-Partition around algorithm (MD-PAM), Linear Scaling-Crow Search Optimization (LS-CSO), Anova-Recurrent Neural Network. The implementation of this framework was achieved in three phases (Phase 1, Phase 2, and Phase 3). Phase 1 is about user registration and authentication using UUID-Blake, which enhances security by allowing legitimate users into the cloud environment. Phase 2 deals with clustering and resource monitoring using MD-PAM and A-RNN, to reduce makespan the similar tasks are clustered using task length and maximize the resource utilization by predicting the resource availability. Phase 3 deals with the scheduling of dynamic workflows using LS-CSO by selecting suitable virtual machines. We have considered the heterogeneous computing scheduling problem (HCSP) and grid workload archive (GWA)-T-12 Bitbrains datasets for comparing our proposed framework with existing works. Based on the result analysis, the proposed LS-SCO outperformed when compared with the algorithms CSO, PSO and RR has achieved better performance.

Cloud computing plays a significant role in everyone’s lifestyle by snugly linking communities, information, and trades across the globe. Due to its NP-hard nature, recognizing the optimal solution for workflow scheduling in the cloud is a challenging area. We proposed a hybrid meta-heuristic cost-effective load-balanced approach to schedule workflow in a heterogeneous environment. Our model is based on a genetic algorithm integrated with predict earliest finish time (PEFT) to minimize makespan. Instead of assigning the task randomly to a virtual machine, we apply a greedy strategy that assigns the task to the lowest-loaded virtual machine. After completing the mutation operation, we verify the dependency constraint instead of each crossover operation, which yields a better outcome. The proposed model incorporates the virtual machine’s performance variance as well as acquisition delay, which concedes the minimum makespan and computing cost. One of the most astounding aspects of our cost-effective hybrid genetic algorithm (CHGA) is its capacity to anticipate by creating an optimistic cost table (OCT) while maintaining quadratic time complexity. Based on the results of our meticulous experiments on some real-world workflow benchmarks and comprehensive analysis of some recently successful scheduling algorithms, we concluded that the performance of our CHGA is melodious. CHGA is 14.58188%, 11.40224%, 11.75306%, and 9.78841% cheaper than standard Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Cost Effective Genetic Algorithm(CEGA), and Cost-Effective Load-balanced Genetic Algorithm (CLGA), respectively.

Distributed computing workflow is an effective paradigm to express a range of applications with cloud computing platforms for scientific research explorations. One of the most difficult application areas of cloud computing technology is task scheduling. In a cloud, heterogeneous context, job scheduling with minimal execution cost and time, as well as workflow reliability, are critical. While working in the heterogeneous cloud environment, tasks that are successfully executed are widely identified by considering the failure of the processor or any communication technologies link. It will also have an impact on the workflow's reliability as well as the user's service quality expectations. This research paper proposes a Critical Parent Reliability-based Scheduling (CPRS) method that uses the reliability parameter to plan the task while taking into account the user-defined cost and deadline metrics. The effectiveness of the algorithm is compared to current algorithms utilizing scientific workflows as a benchmark, such as Cybershake, Sipht, and Montage. The simulation results supported the assertions by efficiently allocating resources to the cloudlets and stabilizing all of the aforementioned parameters using sufficient performance metrics growth. [ABSTRACT FROM AUTHOR]

Nowadays, heterogeneous cloud resources are charged by cloud providers according to the pay-as-you-go pricing model. To execute workflow applications in clouds under deadline constraints, cloud resources have to be utilized appropriately and judiciously, challenging traditional workflow scheduling algorithms, which are either inapplicable to the cloud environment or fail to fully exploit the features of scheduling problem for cost optimization. In this paper, we propose a heuristic algorithm CSDW and a meta-heuristic algorithm N-WOA to minimize the execution cost of the given workflow subject to the deadline constraint in clouds. CSDW first assigns the sub-deadline to each task based on the modified probabilistic upward rank, and then tasks are sorted and mapped to appropriate instances, finally instance-type upgrading and downgrading method is adopted to further accelerate workflow execution and reduce the total cost, respectively. N-WOA employs whale optimization algorithm for deadline-constrained cost optimization by refining the task ordering step in CSDW. By simulation experiments on scientific workflows with existing algorithms, the results demonstrate the capability of the proposed algorithms in meeting the deadlines and reducing the execution costs, CSDW is highly competitive and N-WOA achieves the best performance in all cases. [ABSTRACT FROM AUTHOR]

Making sound trade-offs between the energy consumption and the makespan of workflow execution in cloud platforms remains a significant but challenging issue. So far, some works balance workflows' energy consumption and makespan by adopting multi-objective evolutionary algorithms, but they often regard this as a black-box problem, resulting in the low efficiency of the evolutionary search. To compensate for the shortcomings of existing works, this paper mathematically formulates the cloud workflow scheduling for an infrastructure-as-a-service (IaaS) platform as a multi-objective optimization problem. Then, this paper tailors a knowledge-driven energy- and makespan-aware workflow scheduling algorithm, namely EMWSA. Specifically, a critical task adjustment-based local search strategy is proposed to intelligently adjust some critical tasks to the same resource of their successor tasks, striving to simultaneously reduce workflows' energy consumption and makespan. Further, an idle gap reuse strategy is proposed to search the optimal energy consumption of each non-critical task without affecting the operation of other tasks, so as to further reduce energy consumption. Finally, in the context of real-world workflows and cloud platforms, we carry out comparative experiments to verify the superiority of the proposed EMWSA by significantly outperforming 4 representative baselines on 19 out of 20 workflow instances. [ABSTRACT FROM AUTHOR]

Cloud Computing is no longer just a fad and announcement. Since the beginning of this decade and up to the present, cloud computing has changed and evolved. Workflows are employed in many different scientific disciplines to coordinate data demanding applications, but their virtualized apps, platforms, compute, and storage must be promptly provisioned, scaled, and released instantly. The vast majority of researchers agree that using scientific workflows as a paradigm to define, arrange, and communicate complex scientific analysis is helpful. Workflow scheduling aims to optimally assign and execute sequence of tasks on various virtual machine instances shared and controlled by the workflow scheduler. Numerous scheduling algorithms have been proposed to tackle difficult problems more quickly than metaheuristic ones due to the NP-complete nature of this problem and its dependence on theproblem size. Energy consumption has emerged as a major issue in the cloud computing environment, in addition to the standard quality of service(QoS) restrictions like time and cost to handle this issue. The goal of this paper is to develop the Dynamic Provisioning Based on Demand (DPBD) algorithm for workflow scheduling. While sequential task completion and task priority are design restrictions, the dual objectives of the cloud service are to minimize makespan and energy usage. Using the non-dominated sorting genetic algorithm (NSGA-II), the multi-objective optimization problem's Pareto optimal solutions are found. Our approach is validated by simulating a complex workflow application. [ABSTRACT FROM AUTHOR]

Cloud computing is widely used in various fields, which can provide sufficient computing resources to address users' demands (workflows) quickly and effectively. However, resource failure is inevitable, and a challenge to optimize the workflow scheduling is to consider the fault tolerance. Most of previous algorithms are based on failure prediction and fault-tolerant strategies, which can cause the time delay and waste of resources. In this paper, combining the above two methods through a deep reinforcement learning framework, an adaptive fault-tolerant workflow scheduling framework called RLFTWS is proposed, aiming to minimize the makespan and resource usage rate. In this framework, the fault-tolerant workflow scheduling is formulated as a markov decision process. Resubmission and replication strategy are as two actions. A heuristic algorithm is designed for the task allocation and execution according to the selected fault-tolerant strategy. And, double deep Q network framework (DDQN) is developed to select the fault-tolerant strategy adaptively for each task under the current environment state, which is not only prediction but also learning in the process of interacting with the environment. Simulation results show that the proposed RLFTWS can efficiently balance the makespan and resource usage rate, and achieve fault tolerance. [ABSTRACT FROM AUTHOR]

Cloud computing provides users with usage-based IT services on-demand basis. In these cloud centers, physical machines (PMs) are combined with virtual machines (VMs). Improper planning in workflow scheduling and VM consolidation disturbs the load balancing capability of the system thereby reducing the overall energy of the system with rapid increase in execution time. In this paper, the energy-efficient multi-objective adaptive Manta ray foraging optimization (MAMFO) is proposed for efficient workflow planning. It also optimizes the multi-objective factors such as energy consumption and resource utilization, i.e., CPU and memory. Dynamic Threshold with Enhanced Search and Rescue (DT-ESAR) is introduced for the VM Consolidation System. The dynamic threshold identifies the hosts that are underutilized, overutilized, and normalized. ESAR migrates the VMs from one host to another based on the threshold number. The proposed framework improves energy efficiency and minimizes the time span of the process flow. The experimental results show the efficiency of the proposed approach in terms of energy consumption, makespan, number of migrations and overall SLA. The proposed framework energy consumption is 0.234 kWh, the makespan is 107.25, the number of VM migrations performed is 51, and the overall SLA is 5.23. To determine whether the proposed MAMFO/DT-ESAR method is effective, the findings are compared with the existing methods. Utilizing CloudSim for the experimental evaluation, it is found that the suggested approach significantly improved resource utilization and energy efficiency. [ABSTRACT FROM AUTHOR]

Heterogeneous cloud datacenters are well-suited and cost-efficient platforms for execution of scientific workflows requested from academics. Workflow scheduling algorithms have drastic impacts on the objectives that stakeholders in the system expect. This paper models the scientific workflow scheduling issue to a bi-objective optimization problem with makespan and reliability optimization approach because the users not only expect to have quick response, but also they need reliable executions. To address the issue, a new system framework and different concepts are introduced. A centralized log as a repository module is embedded in the system framework to register all kinds of system failures. In addition to, the new scheduling failure factor (SFF), which has reciprocal relation with system reliability, is defined. Therefore, the broker module quantifies the failure proneness of all resources and the most reliable ones are incorporated in scheduling model. The aforementioned scheduling model is then formulated to a bi-objective optimization problem with makespan and SFF minimization viewpoint which is an NP-Hard problem. To solve this combinatorial problem, a hybrid bi-objective discrete cuckoo search algorithm (HDCSA) is proposed. The proposed hybrid algorithm utilizes different novel Levy flight operators commensurate with discrete search space that makes good balance between exploration and exploitation in optimization process. The proposed HDCSA was validated in 12 extensive scenarios that were conducted on both symmetric and asymmetric scientific workflows in different conditions. The final results prove that the proposed bi-objective HDCSA scheduler has the amount of 22.11%, 12.97%, 11.81%, 12.18%, and 12.42% on average improvement against other state-of-the-arts in terms of makespan, SFF, speedup, efficiency, and SLR, respectively, which are prominent performance evaluation metrics is this scheduling domain. [ABSTRACT FROM AUTHOR]

Task Scheduling is a tremendous challenge in cloud computing as tasks are varied in terms of their processing capacity and interdependent on each other i.e. workflows. Therefore, it is difficult to map workflows to suitable virtual resources in cloud environment. Many authors developed various schedulers, which addresses makespan and Quality of service metrics. Authors have not addressed parameters makespan in combination with migration time, which also affects performance of cloud computing environment. In this manuscript, a new workflow-scheduling mechanism is proposed, which takes priorities of tasks and schedules tasks effectively on to corresponding virtual resources. Whale optimization algorithm as the methodology to model this algorithm. Extensive simulations carried out on workflowsim simulator. It was evaluated aganist existing PSO, CS, ACO, GA algorithms. Finally, from simulation results, it was identified that makespan, migration time and energy consumption were minimized to a good extent. [ABSTRACT FROM AUTHOR]

Scientific workflows consist of numerous tasks subject to constraints on data dependency. Effective workflow scheduling is perpetually necessary to efficiently utilize the provided resources to minimize workflow execution cost and time (makespan). Accordingly, cloud computing has emerged as a promising platform for scheduling scientific workflows. In this paper, level- and hierarchy-based scheduling approaches were proposed to address the problem of scheduling scientific workflow in the cloud. In the level-based approach, tasks are partitioned into a set of isolated groups in which available virtual machines (VMs) compete to execute the groups' tasks. Accordingly, based on a utility function, a task will be assigned to the VM that will achieve the highest utility by executing this task. The hierarchy-based approach employs a look-ahead approach, in which the partitioning of the workflow tasks is performed by considering the entire structure of the workflow, whereby the objective is to reduce the data dependency between the obtained groups. Additionally, in the hierarchy-based approach, a fair-share strategy is employed to determine the share (number of VMs) that will be assigned to each group of tasks. Dividing the available VMs based on the computational requirements of the task groups provides the hierarchy-based approach the advantage of further utilizing the VMs usage. The results show that, on average, both approaches improve the execution time and cost by 27% compared to the benchmarked algorithms. [ABSTRACT FROM AUTHOR]

Cloud computing is a prominent approach for complex scientific and business workflow applications in the pay-as-you-go model. Workflow scheduling poses a challenge in cloud computing due to its widespread applications in physics, astronomy, bioinformatics, and healthcare, etc. Resource allocation for workflow scheduling is problematic due to the computationally intensive nature of the workflow, the interdependence of tasks, and the heterogeneity of cloud resources. During resource allocation, the time and cost of execution are significant issues in the cloud-computing environment, which can potentially degrade the service quality that is provided to end users. This study proposes a method focusing on makespan, average utilization, and cost. The authors propose a task's dynamic priority for workflow scheduling using MONWS, which uses the min-max algorithm to minimize the finish time and maximize resource utilization by calculating the dynamic threshold value for scheduling tasks on virtual machines. When the experimental results were compared to existing algorithms, MONWS achieved a 35% improvement in makespan, an 8% increase in maximum average cloud utilization, and a 4% decrease in cost. [ABSTRACT FROM AUTHOR]

Devising an efficient workflow scheduling algorithm is paramount to explore high performance from Heterogeneous Computing Environments. In this paper, two scheduling algorithms are proposed to minimize the schedule length. A novel list-based heuristic scheduling algorithm namely Global Highest degree Task First (GHTF) algorithm is proposed which focuses on increasing the degree of parallelism to reduce the makespan. The proposed heuristic based Branch and Bound strategy namely Critical Path/Earliest Finish Time (CP/EFT) algorithm is devised to schedule workflows to optimality while minimizing the time complexity for evaluating each state in the search space. The GHTF algorithm has shown improvement in the schedules with no additional overhead in computations. Experimental results disclosed that GHTF algorithm generated better schedules by 5–20 percent while CP/EFT algorithm improved the schedules by 10.76–23.45 percent compared to classical list scheduling algorithms. Moreover, CP/EFT algorithm outperformed GHTF algorithm by 5 percent. [ABSTRACT FROM AUTHOR]

Cloud computing has long been recognized as the best way to execute and manage high-performance applications across a variety of domains. Yet, cloud computing service providers are offering to their users computing resources with various combinations of configurations and prices. Selecting a proper configuration setting for the optimization of both cost and makespan while running high-performance computing applications in a cloud computing environment remains a complex problem. This complexity comes from a variety of reasons, including the configurations and pricing of computer resources specified by providers, as well as the characteristics of user applications. To tackle this problem, we proposed a three-variant algorithm to assist users in scheduling their workflow applications on clouds to reduce the makespan and monetary costs. Extensive simulation tests with various experimental settings are used to assess the suggested algorithm. [ABSTRACT FROM AUTHOR]

Workflow scheduling is essential to simultaneously optimize the makespan and economic cost for cloud services and has attracted intensive interest. Most of the existing multi-objective cloud workflow scheduling algorithms regard the focused problems as black-boxes and design evolutionary operators to perform random searches, which are inefficient in dealing with the elasticity and heterogeneity of cloud resources as well as complex workflow structures. This study explores the characteristics of cloud resources and workflow structures to design a knowledge-based evolutionary optimization operator, named KEOO, with two novel features. First, we develop a task consolidation mechanism to reduce the number of cloud resources used, reducing the economic cost of workflow execution without delaying its finish time. Then, we develop a critical task adjustment mechanism to selectively move the critical predecessors of some tasks to the same resources to eliminate the data transmission overhead between them, striving to improve the economic cost and finish time simultaneously. At last, we embed the proposed KEOO into four classical multi-objective algorithms, i.e., NSGA-II, HypE, MOEA/D, and RVEA, forming four variants: KEOO-NSGA-II, KEOO-HypE, KEOO-MOEA/D, and KEOO-RVEA, for comparative experiments. The comparison results demonstrate the effectiveness of the KEOO in improving these four algorithms in solving cloud workflow scheduling problems. [ABSTRACT FROM AUTHOR]

It is becoming increasingly attractive to execute workflows in the cloud, as the cloud environment enables scientific applications to utilize elastic computing resources on demand. However, despite being a key to efficiently managing application execution in the cloud, traditional workflow scheduling algorithms face significant challenges in the cloud environment. The gradient-based optimizer (GBO) is a newly proposed evolutionary algorithm with a search engine based on the Newton's method. It employs a set of vectors to search in the solution space. This study designs a gradient-based scheduler by using GBO for workflow scheduling to minimize the usage costs of workflows under given deadline constraints. Extensive experiments are conducted on well-known scientific workflows of different sizes and types using WorkflowSim. The experimental results show that the proposed scheduling algorithm outperforms five other state-of-the-art algorithms in terms of both the constraint satisfiability and cost optimization, thereby verifying its advantages in addressing workflow scheduling problems. [ABSTRACT FROM AUTHOR]

Nowadays, how to reduce the execution cost of workflow under the premise of meeting the deadline constraint is one of the main problems of workflow scheduling in cloud. A three-step list scheduling algorithm can solve this problem effectively. However, the algorithm can only form static sub-deadline in deadline allocation stage. To provide convenience for users to deploy workflow tasks, cloud service providers provide users with three types of instances. Among them, spot instance has great price advantage and can greatly reduce the execution cost of workflow. To solve the above problems, this paper proposed a cost optimization algorithm based on dynamic allocation of deadline for workflow scheduling (S-DTDA). This algorithm used particle swarm optimization to dynamically allocate the deadline, which made up for the defect of the three-step list scheduling algorithm. In the virtual machine selection stage, the algorithm added the spot instance to the candidate resources, which greatly reduced the execution cost. Experimental results show that compared with other classical algorithms, this algorithm has obvious advantages in experimental success rate and execution cost. In conclusion, S-DTDA algorithm can effectively solve the cost optimization problem of deadline constraint in workflow scheduling. [ABSTRACT FROM AUTHOR]

The execution of the real-time workload in the multi-cloud platform with the Service Level Agreement (SLA) requirements is a challenging task. Existing workload scheduling models have addressed issues related to minimizing execution time, cost, and energy with application reliability prerequisite. However, these models are not efficient in maximizing resource utilization under a multi-cloud platform. In addressing resource utilization issues, this paper presents a Workload Scheduling Resource Utilization Maximization (WS-RUM) technique for the multi-cloud platform. The WS-RUM technique leverages a multi-objective such as energy, processing efficiency, and fault-tolerant offloading mechanism employing a dragonfly soft computing algorithm. The WS-RUM improves resource utilization by minimizing both energy and processing time for real-time workload execution in comparison with existing workload execution. [ABSTRACT FROM AUTHOR]

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Cloud computing possesses the merit of being a faster and cost-effective platform in terms of executing scientific workflow applications. Scientific workflow applications are found in different domains, such as security, astronomy, science, etc. They are represented by complex sizes, which makes them computationally intensive. The main key to the successful execution of scientific workflow applications lies in task resource mapping. However, task-resource mapping in a cloud environment is classified as NPcomplete. Finding a good schedule that satisfies users' quality of service requirements is still complicated. Even if different studies have been carried out to propose different algorithms that address this issue, there is still a big room for improvement. Some proposed algorithms focused on optimizing different objectives such as makespan, cost, and energy. Some of those studies fail to produce lowtime complexity and low-runtime scientific workflow scheduling algorithms. In this paper, we proposed a non-constrained, low-runtime, and low-time-complexity scientific workflow scheduling algorithm for cost minimization. Since the proposed algorithm is a list scheduling algorithm, its key success is properly selecting computing resources and its operating CPU frequency for each task using the maximum cost difference and minimum cost-execution difference from the mean. Our algorithm achieves almost the same cost reduction results as some of the current states of the arts while it is still low complex and uses less run-time. [ABSTRACT FROM AUTHOR]

Workflow computing has become an essential part of many scientific and engineering fields, while workflow scheduling has long been a well-known NP-complete research problem. Major previous works can be classified into two categories: heuristic-based and guided random-search-based workflow scheduling methods. Monte Carlo Tree Search (MCTS) is a recently proposed search methodology with great success in AI research on game playing, such as Computer Go. However, researchers found that MCTS also has potential application in many other domains, including combinatorial optimization, task scheduling, planning, and so on. In this paper, we present a new workflow scheduling approach based on MCTS, which is still a rarely explored direction. Several new mechanisms are developed for the major steps in MCTS to improve workflow execution schedules further. Experimental results show that our approach outperforms previous methods significantly in terms of execution makespan. [ABSTRACT FROM AUTHOR]

Due to large volumes of data from Cloud Computing and from the Internet of Things, the companies' software ecosystem requires an efficient integration of applications and services. Performance improvement from integration platforms' runtime systems is directly related to task scheduling strategies from integration processes. It is still a challenge to find the proper heuristic for a given integration process subject to high inbound data rates. This article proposes a simulation tool for the field of Enterprise Application Integration, which implements different scheduling heuristics and allows the extraction of performance metrics. Three task scheduling heuristics were simulated during the integration process, and the results were evaluated through statistical tests. [ABSTRACT FROM AUTHOR]

Abstract A workflow is an effective way for modeling complex applications and serves as a means for scientists and researchers to better understand the details of applications. Cloud computing enables the running of workflow applications on many types of computational resources which become available on-demand. As one of the most important aspects of cloud computing, workflow scheduling needs to be performed efficiently to optimize resources. Due to the existence of various resource types at different prices, workflow scheduling has evolved into an even more challenging problem on cloud computing. The present paper proposes a workflow scheduling algorithm in the cloud to minimize the execution cost of the deadline-constrained workflow. The proposed method, EDQWS, extends the current authors’ previous study (DQWS) and is a two-step scheduler based on divide and conquer. In the first step, the workflow is divided into sub-workflows by defining, scheduling, and removing a critical path from the workflow, similar to DQWS. The process continues until only chain-structured sub-workflows, called linear graphs, remain. In the second step which is linear graph scheduling, a new merging algorithm is proposed that combines the resulting linear graphs so as to reduce the number of used instances and minimize the overall execution cost. In addition, the current work introduces a scoring function to select the most efficient instances for scheduling the linear graphs. Experiments show that EDQWS outperforms its competitors, both in terms of minimizing the monetary costs of executing scheduled workflows and meeting user-defined deadlines. Furthermore, in more than 50% of the examined workflow samples, EDQWS succeeds in reducing the number of resource instances compared to the previously introduced DQWS method.

Cloud computing delivers a desirable environment for users to run their different kinds of applications in a cloud. Numerous of these applications (tasks), such as bioinformatics, astronomy, biodiversity, and image analysis, are deadline-sensitive. Such tasks must be properly allocated to virtual machines (VMs) to avoid deadline violations, and they should reduce their execution time and cost. Due to the contradictory environment, minimizing the application task's completion time and execution cost is extremely difficult. Thus, we propose a Cost-aware Quantum-inspired Genetic Algorithm (CQGA) to minimize the execution time and cost by meeting the deadline constraints. CQGA is motivated by quantum computing and genetic algorithm. It combines quantum operators (measure, interference, and rotation) with genetic operators (selection, crossover, and mutation). Quantum operators are used for better population diversity, quick convergence, time-saving, and robustness. Genetic operators help to produce new individuals, have good fitness values for individuals, and play a significant role in preserving the evolution quality of the population. In addition, CQGA used a quantum bit as a probabilistic representation because it has higher population diversity attributes than other representations. The simulation outcome exhibits that the proposed algorithm can obtain outstanding convergence performance and reduced maximum cost than benchmark algorithms. [ABSTRACT FROM AUTHOR]

Workflow scheduling poses a significant challenge in cloud computing environments, offering the potential to optimize execution time and reduce economic costs. In this paper, we present BFWS, a bidirectional workflow scheduling approach with a feedback mechanism, designed to address the deadline-constrained cost-aware workflow scheduling problem. BFWS is built upon a bidirectional scheduling framework that facilitates resource allocation from both the forward and the backward directions of the workflow. This framework incorporates a dynamic sub-deadline prediction method, leveraging prior knowledge and real-time scheduling progress to enhance the accuracy of task sub-deadline predictions. Additionally, a cost-aware resource selection operator is integrated into the framework, enabling the selection of appropriate virtual machines based on considerations of cost and time constraints. To further enhance decision-making and optimize performance, we introduce a feedback mechanism that enables BFWS to adapt and improve over time. Through extensive experiments using real-world workflows, the superior performance of BFWS is validated compared to the state-of-the-art algorithms. Moreover, the effectiveness of the bidirectional scheduling approach and the feedback mechanism is demonstrated individually through rigorous validation experiments. These findings highlight the potential of BFWS in achieving efficient and effective workflow scheduling in cloud computing environments. • Introducing bidirectional scheduling for task flexibility. • Implementing a feedback mechanism for continuous improvement. • Testing on diverse real-world workflows, demonstrating competitiveness. [ABSTRACT FROM AUTHOR]

Edge computing is one of the predominant technologies which facilitates the option of bringing out the computing resources closer to the location of the end users when they are utilized by them. This facility offered by edge computing technology need to reduce the utilization of network bandwidth and response time with respect to the user's workflow. In this paper, Multi-Strategy Improved Sand Cat Swarm Optimisation Algorithm (MSISCSOA)-based workflow scheduling mechanism is proposed for handling the challenges of workflow scheduling in cloud-edge computing environment. The core objective of this MSISCSOA-based workflow scheduling algorithm targets on minimizing the execution latency and energy consumption to facilitate timely and on-demand end users' satisfaction of resources. This MSISCSOA scheme is adopted with the improvement introduced using random variation and elite collaborative strategies, such that well-balanced the trade-off between exploration and exploitation is achieved. This improvement is introduced over Sand Cat Optimization Algorithm (SCOA) using the merits of dynamic random search and joint opposite selection strategies that accelerates the convergence of the algorithm with increased global optimization and searching efficiency. It specifically improved SCOA using random variation for escaping from the local point of optimality. It also used well distributed pareto fronts and population evolution multi-strategy that aids in searching solutions with maximized diversity. The simulation experiments conducted using the datasets of Montage, Cybershake, LIGO and SIPHT an average confirmed minimized execution latency of 21.38 % and energy consumptions of 19.56 %, better than the baseline Ant Colony Optimization Algorithm-Based Workflow Scheduling (IACOAWS), Quadratic Penalty Function-based Particle Swarm Optimization Algorithm (QPF-PSOA), Biogeography Optimization (BBO) Algorithm based Multi-Objective Task Scheduling (BBOAMOTS) and Different Evolution-based Task Clustering and Scheduling (DETCS) approaches used for comparative investigation. • In this paper, Multi-Strategy Improved Sand Cat Swarm Optimisation Algorithm (MSISCSOA)-based workflow scheduling mechanism is proposed for handling the challenges of workflow scheduling in cloud-edge computing environment. • The core objective of this MSISCSOA-based workflow scheduling algorithm targets on minimizing the execution latency and energy consumption to facilitate timely and on-demand end users' satisfaction of resources. • This MSISCSOA scheme is adopted with the improvement introduced using random variation and elite collaborative strategies, such that well-balanced the trade-off between exploration and exploitation is achieved. • This improvement is introduced over Sand Cat Optimization Algorithm (SCOA) using the merits of dynamic random search and joint opposite selection strategies that accelerates the convergence of the algorithm with increased global optimization and searching efficiency. • The simulation experiments conducted using the datasets of Montage, Cybershake, LIGO and SIPHT an average confirmed minimized execution latency of 21.38 % and energy consumptions of 19.56 % better than the baseline approaches used for comparative investigation. [ABSTRACT FROM AUTHOR]

One of the most difficult aspects of scheduling operations on virtual machines in a multi-cloud environment is determining a near-optimal permutation. This task requires assigning various computing jobs with competing objectives to a collection of virtual machines. A significant number of NP-hard problem optimization methods employ multi-objective algorithms. As a result, one of the most successful criteria for discovering the best Pareto solutions is Pareto dominance. In this study, the Pareto front is calculated using a novel multi-objective minimum weight approach. In particular, we use particle swarm optimization (PSO) to expand the FR-MOS multi-objective scheduling algorithm by using fuzzy resource management to maximize variety and obtain optimal Pareto convergence. The competing objectives include reliability, cost, utilization of resources, risk probability, and time makespan. Most of the previous studies provide numerous symmetry or equivalent solutions as trade-offs for different objectives, and selecting the optimum solution remains an issue. We propose a novel decision-making strategy named minimum weight optimization (MWO). Multi-objective algorithms use this method to select a set of permutations that provide the best trade-off between competing objectives. MWO is a suitable choice for attaining all optimal solutions, where both the needs of consumers and the interests of service providers are taken into consideration. (MWO) aims to find the best solution by comparing alternative weights, narrowing the search for an optimal solution through iterative refinement. We compare our proposed method to five distinct decision-making procedures using common scientific workflows with competing objectives: Pareto dominance, multi-criteria decision-making (MCDM), linear normalization I, linear normalization II, and weighted aggregated sum product assessment (WASPAS). MWO outperforms these strategies according to the results of this study.

The workflow scheduling on edge computing platforms in industrial scenarios aims to efficiently utilize the computing resources of edge platforms to meet user service requirements. Compared to ordinary task scheduling, tasks in workflow scheduling come with predecessor and successor constraints. The solutions to scheduling problems typically include traditional heuristic methods and modern deep reinforcement learning approaches. For heuristic methods, an increase in constraints complicates the design of scheduling rules, making it challenging to devise suitable algorithms. Additionally, whenever the environment undergoes updates, it necessitates the redesign of the scheduling algorithms. For existing deep reinforcement learning-based scheduling methods, there are often challenges related to training difficulty and computation time. The addition of constraints makes it challenging for neural networks to make decisions while satisfying those constraints. Furthermore, previous methods mainly relied on RNN and its variants to construct neural network models, lacking a computation time advantage. In response to these issues, this paper introduces a novel workflow scheduling method based on reinforcement learning, which utilizes neural networks for direct decision-making. On the one hand, this approach leverages deep reinforcement learning, eliminating the need for researchers to define complex scheduling rules. On the other hand, it separates the parsing of the workflow and constraint handling from the scheduling decisions, allowing the neural network model to focus on learning how to schedule without the necessity of learning how to handle workflow definitions and constraints among sub-tasks. The method optimizes resource utilization and response time, as its objectives and the network are trained using the PPO algorithm combined with Self-Critic, and the parameter transfer strategy is utilized to find the balance point for multi-objective optimization. Leveraging the advantages of reinforcement learning, the network can be trained and tested using randomly generated datasets. The experimental results indicate that the proposed method can generate different scheduling outcomes to meet various scenario requirements without modifying the neural network. Furthermore, when compared to other deep reinforcement learning methods, the proposed approach demonstrates certain advantages in scheduling performance and computation time.