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Fog and cloud computing are emerging paradigms that enable distributed and scalable data processing and analysis. However, these paradigms also pose significant challenges for workflow scheduling and assigning related tasks or jobs to available resources. Resources in fog and cloud environments are heterogeneous, dynamic, and uncertain, requiring efficient scheduling algorithms to optimize costs and latency and to handle faults for better performance. This paper aims to comprehensively survey existing workflow scheduling techniques for fog and cloud environments and their essential challenges. We analyzed 82 related papers published recently in reputable journals. We propose a subjective taxonomy that categorizes the critical difficulties in existing work to achieve this goal. Then, we present a systematic overview of existing workflow scheduling techniques for fog and cloud environments, along with their benefits and drawbacks. We also analyze different workflow scheduling techniques for various criteria, such as performance, costs, reliability, scalability, and security. The outcomes reveal that 25% of the scheduling algorithms use heuristic-based mechanisms, and 75% use different Artificial Intelligence (AI) based and parametric modelling methods. Makespan is the most significant parameter addressed in most articles. This survey article highlights potentials and limitations that can pave the way for further processing or enhancing existing techniques for interested researchers. [ABSTRACT FROM AUTHOR]

The fog computing environment expands the capabilities of cloud computing by moving computing, storage, and networking services closer to IoT devices. These resource‐constrained IoT devices often face challenges like high task failure rates and extended execution latency due to data traffic congestion. Distributing IoT services through task offloading across different layers of computing paradigms enhances QoS (Quality of Service) parameters. This endeavor aims to allocate custom workflow‐based real‐time tasks or jobs for processing across various cloud/fog/edge layers, optimizing QoS factors like makespan, energy consumption, and cost. In the fog computing environment, challenges arise due to uncertainties related to job execution locations and the ability to predict future user requirements. Fuzzy logic offers low‐complexity solutions for handling unpredictable and rapidly changing conditions. This paper proposes a hybrid fog‐cloud‐based computing architecture and an intelligent fuzzy logic‐based computation offloading approach. This approach effectively allocates workloads among edge, fog, and cloud layers, resulting in improvements in makespan time (7.51%), energy consumption (4.63%), and cost (13.60%). The proposed method selects suitable processing units or compute nodes for job execution, utilizing heterogeneous resources. Simulation results demonstrate that the proposed methodology outperforms current state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

The recent surge in Internet of Things (IoT) applications and smart devices has led to a substantial rise in the data generation. One of the major issues involved is to meet strict quality of service (QoS) requirements for computing these applications in terms of execution time, cost and in an energy-efficient manner. To extract useful information, fast processing and analysis of data is needed. Consequently, moving all the data to centralized cloud data centers would lead to high processing times, increased cost and energy consumption and more bandwidth usage; thus, processing of applications with strict latency requirements becomes challenging. The addition of fog layer between cloud and IoT devices has provided promising solutions to such issues. However, efficient employment of computing resources in the hybrid infrastructure of fog and cloud nodes is of great significance and demands an optimal scheduling strategy. Toward this direction, a novel Pareto-based algorithm in fog computing, namely energy-efficient time and cost (ETC) constraint scheduling algorithm, is introduced in this paper for scheduling workflow applications. ETC attempts to optimize monetary cost along with time and energy objectives. Improved multi-objective differential evolution (I-MODE) meta-heuristic is introduced and incorporated with deadline-aware stepwise frequency scaling approach that is based on our previously proposed energy makespan multi-objective optimization (EM-MOO) algorithm. Synthetic and real-world application workflows are used to conduct evaluation of the proposed work with existing well-known algorithms from the literature. The experimental results for synthetic workflows reveal that the proposed algorithm lessens energy utilization by 14–21%, execution time by almost 25% and cost consumption by 22–27%, while for real-world application workflows, energy consumption is reduced by 12–24%, execution time by 14–16% and cost consumption by 23–29%. [ABSTRACT FROM AUTHOR]

Abstract Cloud computing has evolved into an indispensable tool for facilitating scientific research due to its ability to efficiently distribute and process workloads in a virtual environment. Scientific tasks that involve complicated task dependencies and user-defined constraints related to quality of service (QoS) and time constraints require the efficient use of cloud resources. Planning these scientific workflow tasks represents an NP-complete problem, prompting researchers to explore various solutions, including conventional planners and evolutionary optimization algorithms. In this study, we present a novel, multistage algorithm specifically designed to schedule scientific workflows in cloud computing contexts. This approach addresses the challenges of efficiently mapping complex workflows onto distributed cloud resources while considering factors like resource heterogeneity, dynamic workloads, and stringent performance requirements. The algorithm uses the whale optimization algorithm (WOA) with a two-phase approach to shorten execution time, minimize financial costs, and effectively maintain load balancing.

In response to the demand for collaborative computation of multi-cryptographic jobs in cryptographic on-demand services within a cloud environment, a multi-cryptographic job collaborative scheduling algorithm was proposed. This algorithm effectively addressed new challenges in cloud environments, such as a variety of cryptographic algorithm types, high concurrency demands, random cross-job interactions, and sudden increases in workloads. Considering the dependencies among jobs, makespan of jobs and computational power of computing units, the scheduling problem for multi-cryptographic job collaborative service was modeled as a multi-objective optimization workflow scheduling problem. A two-stage “select-sort” scheduling algorithm was proposed. In the selection stage, the improved NSGA-III algorithm was employed to select computing units for cryptographic computing jobs, and in the sorting stage, the execution order was determined based on the urgency of jobs. Simulation results demonstrate that the proposed algorithm outperforms traditional scheduling algorithms in terms of energy consumption, migration costs, and adaptability to transient surges in loads.

Cloud computing has evolved into an indispensable tool for facilitating scientific research due to its ability to efficiently distribute and process workloads in a virtual environment. Scientific tasks that involve complicated task dependencies and user-defined constraints related to quality of service (QoS) and time constraints require the efficient use of cloud resources. Planning these scientific workflow tasks represents an NP-complete problem, prompting researchers to explore various solutions, including conventional planners and evolutionary optimization algorithms. In this study, we present a novel, multistage algorithm specifically designed to schedule scientific workflows in cloud computing contexts. This approach addresses the challenges of efficiently mapping complex workflows onto distributed cloud resources while considering factors like resource heterogeneity, dynamic workloads, and stringent performance requirements. The algorithm uses the whale optimization algorithm (WOA) with a two-phase approach to shorten execution time, minimize financial costs, and effectively maintain load balancing. [ABSTRACT FROM AUTHOR]

Serverless computing is a promising paradigm that greatly simplifies cloud programming. With serverless computing, developers simply provide event-driven functions to a serverless platform, and these functions can be orchestrated as serverless workflows to accomplish complex tasks. Due to the lightweight limitation of functions, serverless workflows not only suffer from existing vulnerability-based threats but also face new security threats from the function compiling phase. In this paper, we present SMWE, a secure and makespan-oriented workflow execution framework in serverless computing. SMWE enables all life cycle protection for functions by adopting compiler shifting and running environment replacement in the serverless workflow. Furthermore, SMWE balances the tradeoff between security and makespan by carefully scheduling functions to running environments and selectively applying the secure techniques to functions. Extensive evaluations show that SMWE significantly increases the security of serverless workflows with small makespan cost. [ABSTRACT FROM AUTHOR]

Providing scalable and affordable computing resources has become possible thanks to the development of the cloud computing concept. In cloud environments, efficient task scheduling is essential for maximizing resource usage and enhancing the overall performance of cloud services. This research offers a more effective method for using optimization techniques to improve the efficiency of cloud computing task scheduling. Data centers, hosts, and virtual machines (VMs) comprise cloud infrastructures, and work scheduling is crucial to achieving peak performance. To save time, money, energy, and reaction times, scheduling must be done effectively; the primary objective of this research is to develop and evaluate optimization techniques for task scheduling in cloud environments. The following goals are prioritized in the proposed work: (i) reducing the Total Execution Cost (TEC) of the scheduling process; (ii) reducing the Total Execution Time (TET) during mapping; (iii) achieving appropriate task-to-VM mapping to reduce Energy Consumption (EC); and (iv) reducing the overall Response Time (RT) of the cloud scheduling system. To accomplish these objectives, we offer a method based on the use of three optimization techniques: Tabu Search (T), Bayesian Classification (B), and Whale Optimization (W). Our experimental findings show that, in terms of accomplishing the targeted objectives, the suggested TBW optimization methodology outperforms more well-known approaches like GA-PSO and Whale Optimization. By offering insights into efficient resource usage techniques and overall system effectiveness by 95% for the range of 8 to 14 VMs, this work helps ongoing attempts to improve the performance of cloud computing. [ABSTRACT FROM AUTHOR]

AbstractScientific workflows are a common and critical part of scientific computing, involving complex computations and oversized and distributed computing resources. Efficient workflow execution requires scheduling algorithms considering task dependencies, resource requirements, and deadlines. Cloud computing provides an innovative architecture for extensive heterogeneous computing services. However, scheduling hybrid cloud resources with deadline restrictions while observing QoS standards is an NP-complete task. Mapping workflow tasks to virtual machines and determining the optimal schedule order is a challenging aspect of cloud computing. By executing task requests on the most advantageous virtual machine in the resource pool, energy consumption, overall execution time, and computing costs can be reduced. This research aims to identify the best location to process applications using user’s demand and priority. A multi-objective genetic algorithm is proposed to achieve this objective, which considers conflicting objectives such as time, energy, cost, and deadline. The algorithm initializes an efficient ranking heuristic approach and predicts the earliest finish time (PEFT) using the Bayesian approach to improve the Pareto fronts. This approach enhances the VM migration of cloud-based tasks and optimizes the search space for conflicting objectives. Experimental findings show that the proposed approach reduces cost by 5–6% and time delay by 8% compared to existing approaches. The proposed approach offers an effective solution for scheduling scientific workflows on cloud computing resources while considering various QoS standards. The results demonstrate the potential of multi-objective genetic algorithms for optimizing workflow scheduling in cloud computing environments.

The advancements in computing and storage capabilities of machines and their fusion with new technologies like the Internet of Thing (IoT), 5G networks, and artificial intelligence, to name a few, has resulted in a paradigm shift in the way computing is done in a cloud environment. In addition, the ever‐increasing user demand for cloud services and resources has resulted in cloud service providers (CSPs) expanding the scale of their data center facilities. This has increased energy consumption leading to more carbon dioxide emission levels. Hence, it becomes all the more important to design scheduling algorithms that optimize the use of cloud resources with minimum energy consumption. This paper surveys state‐of‐the‐art algorithms for scheduling workflow tasks to cloud resources with a focus on reducing energy consumption. For this, we categorize different workflow scheduling algorithms based on the scheduling approaches used and provide an analytical discussion of the algorithms covered in the paper. Further, we provide a detailed classification of different energy‐efficient strategies used by CSPs for energy saving in data centers. Finally, we describe some of the popular real‐world workflow applications as well as highlight important emerging trends and open issues in cloud computing for future research directions. [ABSTRACT FROM AUTHOR]

Workflow scheduling problems have been widely studied in cloud computing and edge computing, which aim to exploit cloud-edge resources to execute workflow tasks considering several constraints and optimization goals. However, in the era of Internet of things, the load of each computing task and the amount of data transferred between computing tasks will fluctuate, which changes the original workflow and needs for a new scheduling plan correspondingly. Existing methods are difficult to quickly cope with these dynamic changes and there are few studies applying neural networks to solve problems in workflow scheduling. To bridge the gap, we propose an innovative supervised learning method which leverages function-fitting strategy of neural networks to link the workflow environment and its optimal scheduling plan. Specifically, our approach can be divided into two steps, the first one is to generate dataset and train a seq2seq-based prediction models. In this step, we develop an algorithm for generating a significant amount of workflow instances while ensuring dataset diversity based on complexity estimation. Then we apply GA, NSGA, NSGA-NN three different types GA-based optimization methods to search optimal solutions. Finally, we construct dataset which includes {workflow, environment configurations → obtained optimal solution} and train a seq2seq-based model. The other part is real-time generation of scheduling plans based on trained seq2seq-based model. Simulation experiments have confirmed that our method is both effective and efficient, demonstrating its ability to adapt to changes in the execution environment, workflow task load, and task data transmission, and effectively schedule tasks in real-time. The simulation results show that the seq2seq-based prediction method can approach 90% of the optimal scheme. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing, numerous large-scale workflow are executed in the cloud environment. Therefore, the workflow scheduling in cloud environment has become an emerging topic. This paper focuses on a reliability constrained multi-objective workflow scheduling problem (RCMOWSP) with the objectives of minimum execution cost and time. To solve the RCMOWSP, this paper proposes a knowledge-based multi-objective estimation of distribution algorithm (KMOEDA) with several problem-specific operators. First, an idle time-based decoding scheme is applied to sort the permutation of tasks greedily. In the global search strategy, a probability model is constructed to improve the diversity of population. Based on the problem-specific knowledge, a reliability-aware local search strategy is designed to performs local search around the solutions that violate reliability constraint. An elite enhancement strategy with a task perturbation operator and a resource perturbation operator is introduced to further improve the elite non-dominated solutions in the external archive. A comprehensive experiment is conducted to verify the performance of KMOEDA. The comparative results show that the KMOEDA significantly outperforms several relative multi-objective workflow scheduling approaches in solving the RCMOWSP. [ABSTRACT FROM AUTHOR]

The demand for a quick response from cloud services is rapidly increasing day-by-day. Fog computing is a trending solution to fulfil the demands. When integrated with the cloud, this technology can tremendously improve the performance. Like any other technology, Fog also has the shortcoming of limited resources. The difficulty of efficient scheduling of tasks among limited resources to minimize makespan and energy consumption, while still guaranteeing appropriate execution cost, continues to be a significant issue for research. Hence, this study introduces a Differential Evolution-Grey Wolf Optimization (DE-GWO) technique to enhance the scheduling of scientific workflows under cloud-fog settings. The objective of the proposed DE-GWO algorithm is to mitigate the issue of slow convergence and low accuracy that is often seen in the classical GWO algorithm. The DE method is chosen as the evolutionary pattern of wolves to speed up convergence and enhance GWO's accuracy. This study further formulates a weighted sum based objective function which incorporates three criteria, namely makespan, cost and energy consumption. In this study, the DE-GWO technique is evaluated and compared with many conventional and hybrid optimization algorithms. The simulations use five scientific workflows datasets which includes Montage, Cybershake, Epigenomics, LIGO and SIPHT. The DE-GWO algorithm demonstrates superior performance compared to all conventional algorithms across several scientific workflows and performance criteria. The methodology has a commendable level of competitiveness when compared to other methods, since DE incorporates evolution and elimination mechanisms in GWO and GWO retains a good balance between exploration and exploitation. [ABSTRACT FROM AUTHOR]

Workflow Scheduling is a crucial challenge in cloud computing as different task dependencies involved in workflows which makes task scheduling process more complex and incurs huge energy consumption for Cloud Service Provider (CSP). Inefficient scheduling of workflows onto VMs without considering runtime capacity of tasks leads to increase in consumption of energy, makespan and operational costs for CSP. It is also important to consider the cooling costs for CSP as and when the temperature increases at the datacenter then there is a definite overhead on CSP to invest in cooling costs which in turn leads to increase of costs for both CSP and cloud users. Therefore, to mitigate these operational costs and to reduce energy consumption in cloud computing an energy-temperature aware workflow scheduler based on deep reinforcement learning technique DQN is proposed which calculates priorities of tasks based on their computational capacity and calculates priorities of datacenters based on temperature. After calculation of task, datacenter priorities these are given as input to scheduler induced with DQN model to schedule tasks according to the evaluated priorities. Proposed energy- temperature aware workflow scheduler based on Deep Reinforcement learning (ETAWSDRL) is implemented on workflowsim. Proposed ETAWSDRL is evaluated using scientific workflows(Epigenomics, LIGO, Cybershake, Montage). It is evaluated using existing approaches FCFS, PSO, ACO. Results of proposed ETAWSDRL shown significant improvement over compared approaches while minimizing makespan, utilization of resources, energy consumption, scheduling overhead by 86.7%, 78.32%, 87.1%, 36.2% respectively.

Scheduling of workflows is a critical issue in cloud computing paradigm as dynamic workflows with variable dependencies arises from heterogeneous resources which comes onto cloud application console. Mapping all these interdependent tasks to precise virtual resources is still a challenge for cloud service provider (CSP) as workflows arises to cloud application console varied from time to time with different running time capacities, dependencies, length of tasks. Ineffective scheduling of workflows creates challenges for CSP, cloud users by delaying execution of tasks, increase in rate of failures, underutilization of resources. Many earlier authors proposed various workflow scheduling mechanisms with different metaheuristic optimization approaches but generating schedules in cloud computing is a NP-hard problem and they may suffer when huge workloads coming onto cloud paradigm. Therefore, to handle the above said concerns in cloud paradigm, authors in this research proposed a prioritized workflow scheduler by Asynchronous Advantage Actor Critic (PWSA3C) algorithm by considering priorities of interdependent tasks, number of dependencies to schedule workflows to precise VMs. Extensive simulations for proposed PWSA3C algorithm conducted using workflowsim. Input to proposed approach was given with scientific workflows named Epigenomics, LIGO. For evaluating robustness of proposed PWSA3C, we compared our approach with DQN, A2C, MOABCQ algorithms. From simulated results, it is evident that PWSA3C outperformed over DQN, A2C, MOABCQ algorithms for makespan, rate of failures, utilization of resources, scalability efficiency with Epigenomics, LIGO workflows.

Workflow Scheduling is a huge challenge in cloud paradigm as many number of workflows dynamically generated from various heterogeneous resources and task dependencies in each workflow varies from each other. Therefore, if a workflow with more number of dependencies is not scheduled onto an appropriate Virtual Machine i.e. with low processing capacity which leads to delay in executing workflows and it results in increase of makespan, cost, energy consumption. In order to effectively schedule complex workflows i.e. with more task dependencies, we propose a novel multi objective workflow scheduling algorithm using Deep reinforcement Learning. Initially, priorities of all workflows calculated based on their dependencies and then calculated priorities of VMs based on electricity cost at datacenters to map workflows onto precise VMs. These priorities are fed to scheduler which uses Deep Q-Network model to dynamically schedule tasks by considering both priorities of tasks and VMs. Extensive simulations carried out on workflowsim by considering realtime scientific workflows (Montage, cybershake, Epigenomics, LIGO). Our proposed MOPWSDRL compared against existing state of art approaches i.e. Heterogeneous Earliest First Deadline, Cat Swarm Optimization, Ant Colony Optimization. Results revealed that our proposed MOPDSWRL outperforms existing state of art algorithms by minimizing makespan, energy consumption.

Benefiting from cloud computing's elasticity, scalability, and pay-per-use model, more and more scientific applications are deployed in or migrated to the cloud. Workflow scheduling still faces many challenges due to the growing scales of workflows and the diversified user QoS requirements. In this work, we propose a Mutation-driven and population Grouping Poor and Rich Optimization algorithm (MG-PRO) for scheduling workflows in the cloud to minimize makespan while satisfying the budget constraints. Specifically, we first adopt the middle-class sub-population into the original Poor and Rich Optimization algorithm (PRO), and develop the update strategies for rich and middle-class sub-populations to increase the randomness and search diversity. Secondly, the update mechanism for rich individuals is enriched, and the middle-class sub-population is guided by elite rich individuals, which enhances the information exchange and sharing among sub-populations. Finally, an evolution-aware mutation strategy is designed, where the mutation probability is adjusted adaptively as the dynamic monitoring of the population update process, and the two-point and triangular crossover-based mutations are used alternately to intervene the evolution trajectory according to the degree of objective optimization, resulting in a better balance between exploration and exploration. Extensive experiments are conducted on well-known scientific workflows with different types and scales through WorkflowSim. The experimental results show that, in most cases, MG-PRO outperforms existing algorithms in terms of constraint satisfiability, solution quality and stability. It can generate near-optimal solutions with the different budget constraints satisfied in a relatively short time, for example, the makespan resulting from MG-PRO is at most 59.95% shorter than other meta-heuristic algorithms, and at least 7.33% shorter than all its peers. [ABSTRACT FROM AUTHOR]

In today’s high technology environment, most of the workflows are migrated to the cloud and related resourcing technologies like fog, mist and edge computing. Here, in this research work, we have given a detailed idea about dynamic multi-workflow scheduling (DMWS) in fog environment. The fog computing parameters that are affected are also mentioned. Various standard workflows are discussed mentioning their usage in various fields. DMWS has two major branches which are the most researched in past few years. One is multi-workflow scheduling and the other one is dynamic workflow scheduling. Both workflow scheduling have its own pros and cons. A detailed literature review of the papers published since 2019 on DMWS is discussed in detail. Also, a case study regarding DMWS in the healthcare field is conducted and discussed. A section is included that explains the various challenges and issues in DMWS. Lastly future scope and areas of implementation are discussed for DMWS. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is constantly evolving. The variety of IoT applications has caused new demands to emerge on users' part and competition between computing service providers. On the one hand, an IoT application may exhibit several important criteria, such as deadline and runtime simultaneously, and it is confronted with resource limitations and high energy consumption on the other hand. This has turned to adopting a computing environment and scheduling as a fundamental challenge. To resolve the issue, IoT applications are considered in this paper as a workflow composed of a series of interdependent tasks. The tasks in the same workflow (at the same level) are subject to priorities and deadlines for execution, making the problem far more complex and closer to the real world. In this paper, a hybrid Particle Swarm Optimization and Simulated Annealing algorithm (PSO–SA) is used for prioritizing tasks and improving fitness function. Our proposed method managed the task allocation and optimized energy consumption and makespan at the fog-cloud environment nodes. The simulation results indicated that the PSO–SA enhanced energy and makespan by 5% and 9% respectively on average compared with the baseline algorithm (IKH-EFT). [ABSTRACT FROM AUTHOR]

Scientific workflows are a common and critical part of scientific computing, involving complex computations and oversized and distributed computing resources. Efficient workflow execution requires scheduling algorithms considering task dependencies, resource requirements, and deadlines. Cloud computing provides an innovative architecture for extensive heterogeneous computing services. However, scheduling hybrid cloud resources with deadline restrictions while observing QoS standards is an NP-complete task. Mapping workflow tasks to virtual machines and determining the optimal schedule order is a challenging aspect of cloud computing. By executing task requests on the most advantageous virtual machine in the resource pool, energy consumption, overall execution time, and computing costs can be reduced. This research aims to identify the best location to process applications using user's demand and priority. A multi-objective genetic algorithm is proposed to achieve this objective, which considers conflicting objectives such as time, energy, cost, and deadline. The algorithm initializes an efficient ranking heuristic approach and predicts the earliest finish time (PEFT) using the Bayesian approach to improve the Pareto fronts. This approach enhances the VM migration of cloud-based tasks and optimizes the search space for conflicting objectives. Experimental findings show that the proposed approach reduces cost by 5-6% and time delay by 8% compared to existing approaches. The proposed approach offers an effective solution for scheduling scientific workflows on cloud computing resources while considering various QoS standards. The results demonstrate the potential of multi-objective genetic algorithms for optimizing workflow scheduling in cloud computing environments. [ABSTRACT FROM AUTHOR]

Task scheduling in scientific workflows represents an NP-hard problem due to the number of interdependent tasks, data transfers, and the possible execution infrastructure assignments in cloud computing. For this reason, metaheuristics are one of the most widely applied optimisation techniques. Makespan is one of the main objectives in this problem. However, this metric needs to be complemented with a quality measure with respect to the actual execution time in order to avoid incurring more costs than expected by using an over-optimistic approximation. This research applies a new enhanced disk-network-computing evaluation model, that takes into account the communication among the storage devices involved, which plays an important role in actual schedules. The model is implemented in a genetic algorithm and the well-known heuristic HEFT. We propose different hybridisation metaheuristics in conjunction with a new accuracy metric to measure the difference between the makespan approximations and the real one. The new evaluation model is able to improve accuracy with respect to the standard model, and the proposed hybrid methods significantly improve makespan in the case of heterogeneous infrastructures. [ABSTRACT FROM AUTHOR]

Workflow scheduling is vital to simultaneously minimize execution cost and makespan for cloud platforms since data dependencies among large-scale workflow tasks and cloud workflow scheduling problem involve large-scale interactive decision variables. So far, the cooperative coevolution approach poses competitive superiority in resolving large-scale problems by transforming the original problems into a series of small-scale subproblems. However, the static transformation mechanisms cannot separate interactive decision variables, whereas the random transformation mechanisms encounter low efficiency. To tackle these issues, this paper suggests a decision-variable-contribution-based adaptive evolutionary cloud workflow scheduling approach (VCAES for short). To be specific, the VCAES includes a new estimation method to quantify the contribution of each decision variable to the population advancement in terms of both convergence and diversity, and dynamically classifies the decision variables according to their contributions during the previous iterations. Moreover, the VCAES includes a mechanism to adaptively allocate evolution opportunities to each constructed group of decision variables. Thus, the decision variables with a strong impact on population advancement are assigned more evolution opportunities to accelerate population to approximate the Pareto-optimal fronts. To verify the effectiveness of the proposed VCAES, we carry out extensive numerical experiments on real-world workflows and cloud platforms to compare it with four representative algorithms. The numerical results demonstrate the superiority of the VCAES in resolving cloud workflow scheduling problems. [ABSTRACT FROM AUTHOR]

Leveraging a cloud computing environment for executing workflow applications offers high flexibility and strong scalability, thereby significantly improving resource utilization. Current scholarly discussions heavily focus on effectively reducing the scheduling length (makespan) of parallel task sets and improving the efficiency of large workflow applications in cloud computing environments. Effectively managing task dependencies and execution sequences plays a crucial role in designing efficient workflow scheduling algorithms. This study forwards a high-efficiency workflow scheduling algorithm based on predict makespan matrix (PMMS) for heterogeneous cloud computing environments. First, PMMS calculates the priority of each task based on the predict makespan (PM) matrix and obtains the task scheduling list. Second, the optimistic scheduling length (OSL) value of each task is calculated based on the PM matrix and the earliest finish time. Third, the best virtual machine is selected for each task according to the minimum OSL value. A large number of substantial experiments show that the scheduling length of workflow for PMMS, compared with state-of-the-art HEFT, PEFT, and PPTS algorithms, is reduced by 6.84%–15.17%, 5.47%–11.39%, and 4.74%–17.27%, respectively. This hinges on the premise of ensuring priority constraints and not increasing the time complexity. [ABSTRACT FROM AUTHOR]

Abstract Workflow scheduling is vital to simultaneously minimize execution cost and makespan for cloud platforms since data dependencies among large-scale workflow tasks and cloud workflow scheduling problem involve large-scale interactive decision variables. So far, the cooperative coevolution approach poses competitive superiority in resolving large-scale problems by transforming the original problems into a series of small-scale subproblems. However, the static transformation mechanisms cannot separate interactive decision variables, whereas the random transformation mechanisms encounter low efficiency. To tackle these issues, this paper suggests a decision-variable-contribution-based adaptive evolutionary cloud workflow scheduling approach (VCAES for short). To be specific, the VCAES includes a new estimation method to quantify the contribution of each decision variable to the population advancement in terms of both convergence and diversity, and dynamically classifies the decision variables according to their contributions during the previous iterations. Moreover, the VCAES includes a mechanism to adaptively allocate evolution opportunities to each constructed group of decision variables. Thus, the decision variables with a strong impact on population advancement are assigned more evolution opportunities to accelerate population to approximate the Pareto-optimal fronts. To verify the effectiveness of the proposed VCAES, we carry out extensive numerical experiments on real-world workflows and cloud platforms to compare it with four representative algorithms. The numerical results demonstrate the superiority of the VCAES in resolving cloud workflow scheduling problems.

Fog computing paradigm attempts to provide diverse processing at the edge of IoT networks. Energy usage being one of the important elements that may have a direct influence on the performance of fog environment. Effective scheduling systems, in which activities are mapped on the greatest feasible resources to meet various competing priorities, can reduce energy use. Consequently, a hybrid discrete optimization method called HDSOS-GOA, which uses the Dynamic voltage and frequency scaling (DVFS) approach, is proposed to handle scientific workflow scheduling challenges in the fog computing environment. HDSOS-GOA combines the search qualities of Symbiotic Organisms Search (SOS) and the Grasshopper Optimization Algorithm (GOA) algorithms and the selection of these algorithms for performing workflow scheduling is based on the probability calculated by the learning automata. The HEFT method is used to determine the task sequence. Our solution focuses on reducing the energy consumption of the scheduling process by reducing the number of Virtual Machines required for workflow execution in addition to optimizing the makespan. Comprehensive experiments are carried out on four different scientific workflows with different sizes with and without deadline constraints to evaluate the performance of the suggested scheduling strategy. The results of the experiments show that scheduling with the suggested approach outperforms other well-known metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

To address the issues of long computation time and difficulty in parallel acceleration when using the original Kriging algorithm for interpolation of carbon satellite data at a large regional scale, the Kriging algorithm and its key parts are restructured and optimized. The whole interpolation process is broken up into several fine-grained operations and then organized into a distributed DAG workflow based on dependency relationship and data features. Finally, a distributed computing framework based on the double-tier scheduling structure is designed to accelerate the interpolation workflow on the distributed computing cluster. Experiments show that methods and framework described above can perform Kriging interpolation of different regional scales with high efficiency, and the efficiency advantages are more significantly than Spark at the large regional scale. [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. [ABSTRACT FROM AUTHOR]

The security of workflow scheduling is a significant concern and even is one of the most important metrics of QoS (Quality of Service). This paper presents two approaches to provide a secure connection between users and servers and handle large and medium task size problems. Firstly, a multi-objective scheduling (MO-Ring-IC-NCCLA) algorithm for scientific workflow in the cloud environment is proposed. It tries to minimize workflow makespan and cost as well as increase the cost of attack from an invader. The proposed multi-objective is based on the New Caledonian Crow Learning Algorithm (NCCLA). However, this algorithm has a few drawbacks, including poor exploration activity and inability to balance exploration and exploitation. The social and asocial learning part of standard NCCLA has been modified to tackle these limitations, then a concept of ring topology is used to better Pareto optimal can be found. Secondly, the structure of virtual machines is modified so that the cost of attack from invaders increases. Experimental results based on various real-world workflows indicate the performance improvement of MO-Ring-IC-NCCLA over SBDE, NSGA-II, and MOHFHB algorithms in terms of FS-metric. According to the delta metric (i.e., diversity measures), the proposed algorithm is superior to 85% of the compared metaheuristics. In terms of Inverted Generational Distance (IGD) metric, it outperforms NSGAII and Multi-Objective Artificial Hummingbird Algorithm (MOAHA) for 95% and 80% of the cases, respectively. Based on experiments, makespan and cost improved by 23.12% and 18.43% over existing workflow algorithms. Compared to Multi-Objective Hybrid Fuzzy Hitchcock Bird (MOHFHB), Simulated-annealing Based Differential Evolution (SBDE), and non-dominated sorting genetic algorithm (NSGAII), it improves the FS-metric by 23.35% on average. [ABSTRACT FROM AUTHOR]

Summary: Teaching‐learning‐based optimization (TLBO) algorithm is a population‐based meta‐heuristic algorithm that was created to solve single‐objective optimization problems. The teaching‐learning mechanism of a classroom inspires it. TLBO suffers from weak exploration. As a result, its performance is not good for solving multimodal problems. To turn TLBO into a tool for solving multimodal problems and maintaining good diversity, we made significant modifications into the learning process of the fundamental TLBO. The proposed algorithm produces more diverse solutions and works better for solving multimodal problems. This newly created variant of TLBO is called "Intelligent‐Teaching‐Learning‐Based Optimization (I‐TLBO) algorithm." I‐TLBO's performance is evaluated against the most recent standard benchmark function, CEC‐06, 2019, and it is discovered that I‐TLBO outperforms the other algorithms. After that, I‐TLBO was applied for flowtime‐aware‐cost minimization of the workflow executions in cloud datacenter. To solve these scheduling problems, I‐TLBO and other metaheuristic algorithms are simulated in CloudSim and tested over scientific workflows such as Inspiral, Montage, SIPHT, sample, Cybershake, and Epigenomics workflows. Finally, it is found that I‐TLBO reduces flowtime and cost both by 28.48%, 11.30%, 17.64%, 13.22%, 11.45%, and 14.71% in comparison to the second best performing algorithm while executing the standard workflow in cloud. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]