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A workflow consists of a set of tasks that are dependent on each other and scheduling these dependent tasks to the virtual machines is one of the complex problems in cloud computing. Moreover, workflow scheduling becomes more complex with the increasing number of tasks and virtual machines and considered to be an NP-hard problem. Therefore, the meta-heuristic approaches have been used to find out optimal scheduling of workflow schedules. The proposed algorithm named PSO–GWO is the combination of two well-known meta-heuristic algorithms Particle Swarm Optimization and Grey Wolf Optimization. The experiment result shows that the PSO–GWO algorithm decreases the average total execution cost and average total execution time in comparison to standard Particle Swarm Optimization and Grey Wolf Optimization algorithm.

In recent years, cloud workflow task scheduling has always been an important research topic in the business world. Cloud workflow task scheduling means that the workflow tasks submitted by users are allocated to appropriate computing resources for execution, and the corresponding fees are paid in real time according to the usage of resources. For most ordinary users, they are mainly concerned with the two service quality indicators of workflow task completion time and execution cost. Therefore, how cloud service providers design a scheduling algorithm to optimize task completion time and cost is a very important issue. This paper proposes research on workflow scheduling based on mobile cloud computing machine learning, and this paper conducts research by using literature research methods, experimental analysis methods, and other methods. This article has deeply studied mobile cloud computing, machine learning, task scheduling, and other related theories, and a workflow task scheduling system model was established based on mobile cloud computing machine learning from different algorithms used in processing task completion time, task service costs, task scheduling, and resource usage The situation and the influence of different tasks on the experimental results are analyzed in many aspects. The algorithm in this paper speeds up the scheduling time by about 7% under a different number of tasks and reduces the scheduling cost by about 2% compared with other algorithms. The algorithm in this paper has been obviously optimized in time scheduling and task scheduling.

Workflow scheduling is crucial to the efficient operation of cloud platforms, and has attracted a lot of attention. Up to now, many algorithms have been reported to schedule workflows with budget constraints, so as to optimize workflows' makespan on cloud resources. Nevertheless, the hourly-based billing model in cloud computing is an ongoing challenge for workflow scheduling that easily results in higher makespan or even infeasible solutions. Besides, due to data constraints among workflow tasks, there must be a lot of idle slots in cloud resources. Few works adequately exploit these idle slots to duplicate tasks' predecessors to shorten their completion time, thereby minimizing workflow's makespan while ensuring its budget constraint. Motivated by these, we propose a task duplication based scheduling algorithm, namely TDSA, to optimize makespan for budget-constrained workflows in cloud platforms. In TDSA, two novel mechanisms are devised: 1) a dynamic sub-budget allocation mechanism, it is responsible for recovering unused budget of scheduled workflow tasks and redistributing remaining budget, which is conducive to using more expensive/powerful cloud resources to accelerate completion time of unscheduled tasks; and 2) a duplication-based task scheduling mechanism, which strives to exploit idle slots on resources to selectively duplicate tasks' predecessors, such advancing these tasks' completion time while trying to ensuring their sub-budget constraints. At last, we carry out four groups of experiments, three groups on randomly generated workflows and another one on actual workflows, to compare the proposed TDSA with four baseline algorithms. Experimental results confirm that the TDSA has an overwhelming superiority in advancing the workflows' makespan (up to 17.4%) and improving the utilization of cloud computing resources (up to 31.6%).

Recently, mobile edge computing (MEC) is widely believed to be a promising and powerful paradigm for bringing enterprise applications closer to data sources such as IoT devices or local edge servers. It is capable of energizing novel mobile applications, especially the ultra-latency-sensitive ones, by providing powerful local computing capabilities and lower end-to-end delays. Nevertheless, various challenges, especially the reliability-guaranteed scheduling of multitask business processes in terms of, e.g., workflows, upon distributed edge resources and servers, are yet to be carefully addressed. In this paper, we propose a novel edge-environment-based multi-workflow scheduling method, which incorporates a reliability estimation model for edge-workflows and a coevolutionary algorithm for yielding scheduling decisions. The proposed approach aims at maximizing the reliability, in terms of success rates, of services deployed upon edge infrastructures while minimizing service invocation cost for users. We conduct simulative experimental case studies based on multiple well-known scientific workflow templates and a well-known dataset of edge resource locations as well. Simulative results clearly suggest that our proposed approach outperforms traditional ones in terms of workflow success rate and monetary cost.

A workflow is a group of tasks that are processed in a particular order to complete an application. Also, it is a popular paradigm used to model complex big-data applications. Executing complex applications in a distributed system such as cloud or cluster implicates optimization of several conflicting objectives such as monetary cost, energy consumption, total execution time of the application (makespan). Regardless of this trend, most of the workflow scheduling approaches focused on single or bi-objective optimization problem. In this paper, we considered the problem of scheduling workflows in a cloud environment as a multi-objective optimization problem, and hence proposed a multi-objective workflow-scheduling algorithm based on decomposition. The proposed algorithm is capable of finding optimal solutions with a single run. Our evaluation results show that, by a single run, the proposed approach manages to obtain the Pareto Front solutions which are at least as good as schedules produced by running a single-objective scheduling algorithm with constraints for multiple times.

Workflow is composed of some interdependent tasks and workflow scheduling in the cloud environment that refers to sorting the workflow tasks on virtual machines on the cloud platform. We will encounter many sorting modes with an increase in virtual machines and the variety in task size. Reaching an order with the least makespan is an NP-hard problem. The hardness of this problem increases even more with several contradictory goals. Hence, a meta-heuristic algorithm is what required in reaching the optimal response. Thus, the algorithm is a hybridization of the ant lion optimizer (ALO) algorithm with a Sine Cosine Algorithm (SCA) algorithm and used it multi-objectively to solve the problem of scheduling scientific workflows. The novelty of the proposed algorithm was to enhance search performance by making algorithms greedy and using random numbers according to Chaos Theory on the green cloud computing environment. The purpose was to minimize the makespan and cost of performing tasks, to reduce energy consumption to have a green cloud environment, and to increase throughput. WorkflowSim simulator was used for implementation, and the results were compared with the SPEA2 workflow scheduling algorithm. The results show a decrease in the energy consumed and makespan.

Efficient task and workflow scheduling are very important for improving resource management and reducing power consumption in cloud computing data centers (DCs). However, regarding numerous tasks, virtual machines, and several objectives which should be taken into account, scheduling is considered to be an NP-Hard problem. Multi-objective optimization is an interesting technique to deal with multiple conflicting goals which have been utilized by various schemes to solve the task and workflow scheduling problems. This paper focuses on the metaheuristic multi-objective optimization context and presents a comprehensive survey and overview of the multi-objective scheduling approaches designed for various cloud computing environments. It classifies the scheduling schemes regarding their applied multi-objective optimization algorithms and describes how they have adapted the optimization algorithms to solve scheduling problems. Furthermore, a comparison of the multi-objective scheduling schemes is provided, which illuminates future research directions, and finally concluding remarks are presented.

The predilection of scientific applications toward a high-performance computing system is attained through the emergence of the cloud. Large-scale scientific applications can be modeled as workflows and are scheduled on the cloud. However, such scheduling becomes even more onerous due to the dynamic and heterogeneous nature of cloud and therefore considered as a problem of NP-Complete. The scheduling of workflows is always constrained to QoS parameters. Most of the applications are bound to time and cost, which is observed to be the most crucial parameter. Therefore, in this paper, a heuristic-based budget and deadline constrained workflow scheduling algorithm (HBDCWS) has been proposed to utilize those applications that have the budget and deadline constraints. The novelty of the proposed work is to provide a simple budget and deadline distribution strategy where budget and deadline of workflow are converted to level budget and level deadline. Additionally, the level budget is again transferred to each task. This strategy not only satisfies the given constraints but also proves to be efficient for minimizing the makespan and reducing the cost of execution. Experimental results on several workflows demonstrate that the proposed HBDCWS algorithm finds a feasible solution that accomplishes the given constraints with a higher success rate in most cases.

Cloud services are used to achieve diverse computing needs such as cost, security, scalability, and availability. Acceleration evolution in the distributed and cloud domains is common for large and dynamic workflows deployment. Resources and task mapping depend on the user’s objectives such as reduction in cost or execution completion within the stipulated time in consideration with certain quality of services. Multiple virtual machine instances can be launched by defining different configurations such as operating system, server types, and applications. Though workflow scheduling is an NP-Hard problem, variety of decision-making techniques are available for optimum resource allocation. In this research paper, different algorithms are studied and compared with evolutionary approaches. Workflow scheduling using genetic algorithm is implemented and discussed. This paper aims to design a decision-making technique to optimize resources of cloud. It is an adaptive scheduling to maximize profit by reducing execution time. The approach implemented is useful to cloud service providers to maximize profit and resource efficiency in their services.

The phenomenal advancement of technology paved the way for the execution of complex scientific applications. The emergence of the cloud provides a distributed heterogeneous environment for the execution of large and complex workflows. Due to the dynamic and heterogeneous nature of the cloud, scheduling workflows become a challenging problem. Mapping and assignment of heterogeneous instances for each task while minimizing execution time and cost is a NP-complete problem. For efficient scheduling, it is required to consider various QoS parameters such as time, cost, security, and reliability. Among these, computation time and cost are the two notable parameters. In order to preserve the functionalities of these two parameters in heterogeneous cloud environments, in this paper, a fair budget-constrained workflow scheduling algorithm (FBCWS) is proposed. The novelty of the proposed algorithm is to minimize the makespan while satisfying budget constraints and a fair means of schedule for every task. FBCWS also provides a mechanism to save budget by adjusting the cost-time efficient factor of the minimization problem. The inclusion of a cost-time efficient factor in the algorithm provides flexibility to minimize the makespan or save budget. In order to validate the effectiveness of the proposed approach, several real scientific workflows are simulated, and experimental results are compared with other existing approaches, namely; Heterogeneous Budget Constrained Scheduling (HBCS), Minimizing Schedule Length using Budget Level (MSBL) and Pareto Optimal Scheduling Heuristic (POSH) algorithms. Experimental results prove that the proposed algorithm behaves outstandingly for compute-intensive workflows such as Epigenomic and Sipht. Also, FBCWS outperforms the existing HBCS in most of the cases. Moreover, FBCWS proves to be more time-efficient than POSH and more cost-efficient than MSBL. The effectiveness of the proposed algorithm is illustrated through the popular ANOVA test.

In recent years, with the rapid development of information transfer technology, the load balancing scheduling of server cluster system must be optimized. In order to optimize the load of server, a load balancing scheduling strategy for server cluster system was proposed in this paper. A new scheduling weighted method was proposed, and the optimization of load allocation was realized. In order to verify the feasibility of the scheduling strategy, the results were tested from several aspects. Through a comprehensive test, the research of load balancing scheduling strategy optimization for server cluster system is feasible, which can further improve the server’s load capacity.

The provision of resources and services for scientific workflow applications using a multi-cloud architecture and a pay-per-use rule has recently gained popularity within the cloud computing research domain. This is because workflow applications are computation intensive. Most of the existing studies on workflow scheduling in the cloud mainly focus on finding an ideal makespan or cost. Nevertheless, there are other important quality of service metrics that are of critical concern in workflow scheduling such as reliability and resource utilization. In this respect, this paper proposes a new multi-objective scheduling algorithm with Fuzzy resource utilization (FR-MOS) for scheduling scientific workflow based on particle swarm optimization (PSO) method. The algorithm minimizes cost and makespan while considering reliability constraint. The coding scheme jointly considers task execution location and data transportation order. Simulation experiments reveal that FR-MOS outperforms the basic MOS over the PSO algorithm.

Cloud Computing is becoming more and more popular for solving problems that need high concurrency and a lot of resources. Many traditional areas of research choose to solve their problems through the cloud, and workflow scheduling is one of them. Cloud computing brings many benefits, meanwhile, due to the almost “infinite” amount of resources for users, it also brings new challenges for scheduling and optimization, in which cost and makespan are the most concerned issues for workflow scheduling. Users want to obtain a low cost and fast makespan solution. This paper focuses on how to find an optimized solution to achieve better cost-makespan at the same time under the constraint of deadline. In order to solve this problem, an immune particle swarm optimization algorithm (IMPSO) is proposed, which effectively improves the quality and speed of the optimization. The proposed IMPSO overcomes the problem of slow convergence of PSO, which is easy to fall into local optimization. Experiments show the efficiency and effectiveness of the proposed approach.

The emerging multi-cloud environments (MCEs) empower the execution of large-scale scientific workflows (SWs) with sufficient resource provisioning. However, due to complex task dependencies in SWs and various cost-performance of cloud resources, the SW scheduling in MCEs faces huge challenges. To address these challenges, we propose an Online Workflow Scheduling algorithm based on Adaptive resource Allocation and Consolidation (OWS-A2C). In OWS-A2C, the deadline reassignment is first executed for SW tasks based on the execution performance of instance resources, which enhances resource utilization from a local perspective when executing an SW. Next, the execution instances are allocated and consolidated according to the performance requirements of multiple SWs, which improves resource utilization and reduces the total costs of executing multiple SWs from a global perspective. Finally, the SW tasks are dynamically scheduled to the execution instances with the earliest-deadline-first (EDF) discipline and completed before their sub-deadlines. The extensive simulation experiments are conducted to demonstrate the effectiveness of the proposed OWS-A2C on SW scheduling in MCEs, which outperforms three baseline scheduling methods with higher resource utilization and lower execution costs under deadline constraints.

Cloud computing, specifically its elastic, on demand, and pay per use instances, provide an ideal model for resourcing large scale state-of-the-art scientific analyses. Such scientific work is typically represented as workflows — the most common model for characterizing e-Science experiments and data analysis. Hosting and managing scientific applications on the cloud poses new challenges in terms of workflow scheduling which is key in leveraging its inherent cost and performance benefits. Prior research has studied static scheduling when the number of workflows is known in advance and all are submitted at the same time. However, in practice, a scheduler may have to schedule an unpredictable stream of workflows, for example, recent workflow management systems — such as Parsl, do not construct complete workflows at any stage during their execution, rather they generate partial workflows dynamically during execution — somewhat akin to lazy evaluation. This change in the way in which scientific data and workflows are created and processed represents a disruptive change to the way in which scheduling needs to occur. This paper represents a first and necessary step towards addressing scheduling problems of this nature, in which we present a new algorithm, Dynamic Workload Scheduler (DWS) that handles the dynamics of multiple deadline constrained workflows arriving randomly and scheduling these workflows with reducing cost in mind. Our results show that the DWS algorithm achieves an average 10% higher success rate in terms of fulfilling deadlines for different workloads and reduces the overall cost by an average 23% when compared to the most recent comparable algorithm.

Clouds are becoming an effective platform for scientific workflow applications. In the meantime, Cloud computing structures are moving towards being more heterogeneous. In heterogeneous service-oriented systems, managing the reliability of resources (e.g., processors and communication networks) is widely identified as a critical issue due to processor and communication failures affecting user quality of service requirements. Therefore, these types of failures should be taken into account when scheduling algorithms. The present paper proposes a scheduling approach which includes four algorithms for minimizing the workflow execution cost while also meeting the user-specified deadline and reliability. To meet the application’s requirements, the first algorithm partitions the workflow into several clusters based on a critical parent called CbCP. After that, the resource assignment algorithm, consisting of reliability and deadline distribution methods, satisfies the application’s constraints. Experimental outcomes on various workflows, generated at different scales in real and random fashion, demonstrate that the proposed heuristics meet the deadline and reliability. This ensures the minimal cost when performing a similar quality of service as opposed to the performance of the state-of-the-art DRR and QFEC+ algorithms.

Cloud computing is emerging with growing popularity in workflow scheduling, especially for scientific workflow. Deploying data-intensive workflows in the cloud brings new factors to be considered during specification and scheduling. Failure to establish intermediate data security may cause information leakage or data alteration in the cloud environment. Existing scheduling algorithms for the cloud disregard the interaction among tasks and its effects on application security requirements. To address this issue, we design a new systematic method that considers both tasks security demands and interactions in secure tasks placement in the cloud. In order to respect security and performance, we formulate a model for task scheduling and propose a heuristic algorithm which is based on task’s completion time and security requirements. In addition, we present a new attack response approach to reduce certain security threats in the cloud. To do so, we introduce task security sensitivity measurement to quantify tasks security requirements. We conduct extensive experiments to quantitatively evaluate the performance of our approach, using WorkflowSim, a well-known cloud simulation tool. Experimental results based on real-world workflows show that compared with existing algorithms, our proposed solution can improved the overall system security in terms of quality of security and security risk under a wide range of workload characteristics. Additionally, our results demonstrate that the proposed attack response algorithm can effectively reduce cloud environment threats.

Workflows are a set of tasks and the dependency among them, which are divided into scientific and business categories. To avoid problems of centralized execution of workflows, they are broken into segments that is known as fragmentation. To fragment the workflow, it is highly important to consider the dependency among tasks and runtime conditions. The cooperation between the scheduler and fragmentor must be such that the latter generates appropriate tasks with optimized communication cost, delay time, response time, and throughput. To this end, in the present study, a framework is proposed for scheduling and fragmentation of tasks in scientific workflows that are conducted in fragmentation and scheduling phases. In the fragmentation phase, the fragments are generated with regard to the number of virtual machines available during runtime. In the scheduling phase, the virtual machines are selected with the aim of reducing bandwidth usage. The experiments are performed with three Configurations during both phases of fragmentation and scheduling. Response time, throughput, and cost (BW and RAM) were improved compared to the baseline studies on Sipht, Inspiral, Epigenomics, Montage, and CyberShake workflows as datasets.

Workflow scheduling is one of the significant issues for scientific applications among virtual machine migration, database management, security, performance, fault tolerance, server consolidation, etc. In this paper, existing time-based scheduling algorithms, such as first come first serve (FCFS), min–min, max–min, and minimum completion time (MCT), along with dependency-based scheduling algorithm MaxChild have been considered. These time-based scheduling algorithms only compare the burst time of tasks. Based on the burst time, these schedulers, schedule the sub-tasks of the application on suitable virtual machines according to the scheduling criteria. During this process, not much attention was given to the proper utilization of the resources. A novel dependency and time-based scheduling algorithm is proposed that considers the parent to child (P2C) node dependencies, child to parent node dependencies, and the time of different tasks in the workflows. The proposed P2C algorithm emphasizes proper utilization of the resources and overcomes the limitations of these time-based schedulers. The scientific applications, such as CyberShake, Montage, Epigenomics, Inspiral, and SIPHT, are represented in terms of the workflow. The tasks can be represented as the nodes, and relationships between the tasks can be represented as the dependencies in the workflows. All the results have been validated by using the simulation-based environment created with the help of the WorkflowSim simulator for the cloud environment. It has been observed that the proposed approach outperforms the mentioned time and dependency-based scheduling algorithms in terms of the total execution time by efficiently utilizing the resources.

International audience; Many scientific experiments today are performed using scientific workflows, which become more and more data-intensive. We consider the efficient execution of such workflows in a multisite cloud, leveraging heterogeneous resources available at multiple geo-distributed data centers. Since it is common for workflow users to reuse code or data from previous workflows, a promising approach for efficient workflow execution is to cache intermediate data in order to avoid re-executing entire workflows. However, caching intermediate data and scheduling workflows to exploit such caching in a multisite cloud is complex. In particular, workflow scheduling must be cache-aware, in order to decide whether reusing cache data or re-executing workflows entirely. In this paper, we propose a solution for cache-aware scheduling of scientific workflows in a multisite cloud. Our solution includes a distributed and parallel architecture and new algorithms for adaptive caching, cache site selection, and dynamic workflow scheduling. We implemented our solution in the OpenAlea workflow system, together with cache-aware distributed scheduling algorithms. Our experimental evaluation in a three-site cloud with a real application in plant phenotyping shows that our solution can yield major performance gains, reducing total time up to 42% with 60% of the same input data for each new execution.

Task scheduling is a crucial key component for the efficient execution of data-intensive applications on distributed environments, by which many machines must be coordinated to reduce execution times and bandwidth consumption. This paper presents ADAGE, a data-aware scheduler designed to efficiently execute data-intensive workflows in large-scale computers. The proposed scheduler is based on three key features: $i$ ) critical path analysis, for discovering the critical tasks of a workflow and reducing data transferring between nodes; $ii$ ) work giving, a new dynamic planning strategy for migrating tasks from overloaded to unloaded nodes; and $iii$ ) task replication, which executes task replicas on different nodes for improving both execution time and fault tolerance. Experiments performed on a distributed computing environment composed of up to 1,024 processing nodes show that ADAGE achieves better performances than existing scheduling systems, obtaining an average reduction of up to 66% in execution time.

Recently, the Cloud Computing paradigm is becoming increasingly popular in supporting large-scale and complex workflow applications. The workflow scheduling problem, which refers to finding the most suitable resource for each task of the workflow to meet user defined quality of service (QoS), attracts considerable research attention. Multi-objective optimization algorithms in workflow scheduling have many limitations, e.g., the encoding schemes in most existing heuristic-based scheduling algorithms require prior experts' knowledge and thus they can be ineffective when scheduling workflows upon dynamic cloud infrastructures with real-time. To address this problem, we propose a novel Reinforcement-Learning-Based algorithm to multi-workflow scheduling over IaaS clouds. The proposed algorithm aims at optimizing make-span and dwell time and is to achieve a unique set of correlated equilibrium solution. In the experiment, our algorithm is evaluated for famous scientific workflow templates and real-world industrial IaaS cloud platforms by a simulation process and we compare our algorithm to the current state-of-the-art heuristic algorithms, e.g., NSGA-II, MOPSO, GTBGA. The result shows that our algorithm performs better than compared algorithm.

Cloud computing is an effective technology that delivers interesting services to customers over the Internet. It is beneficial for scientific workflow systems in view of its powerful characteristics. However, scheduling workflow system over a cloud platform has become a challenging problem. In this paper, we propose a novel workflow scheduling strategy for the hybrid cloud environments which consists of an economical distribution of tasks between the various cloud service providers, in order to provide customers with high security services. Then, we study the impact of that security services on the total cost and deadline generated by the workflow. This problem is a major gap in the workflow scheduling field and it is still insufficiently explored in the literature. Our proposed scheduling system is composed for three modules. The first module is the Pre-Scheduler, the second is the Security Enhancement Module and the third one is the Post-Scheduler. The system evaluation and the extensive simulations are performed using an extension of Cloudsim simulation tool. The results show that our strategy preserves the same cost however it affects the deadline.

With the rapid development of cloud computing, virtualization technology represented by container technology is gradually replacing the original virtual machine technology. Introducing the container cloud and its main research direction. Starting from task scheduling, combs and summarizes the current research status of the container cloud task scheduling algorithm, and introduces the current four mainstream container scheduling platforms. According to the independence of scheduling task, it is divided into independent task scheduling and non independent workflow task scheduling. According to the different task scheduling objectives, it is divided into single objective optimization task scheduling algorithm and multi-objective task scheduling algorithm. After a detailed analysis of the advantages and disadvantages of several representative algorithms, the existing improved methods are summarized.

Efficient workflow scheduling algorithms are crucial for attaining high performance in large-scale heterogeneous distributed infrastructures, such as cloud computing. List scheduling algorithms are one of the most efficient heuristic methods for assigning task graphs to fully connected heterogeneous systems. However, most existing list-based scheduling algorithms do not consider the applications’ security requirements and the security services offered by cloud providers. In this paper, we extend four list scheduling algorithms for security-aware workflow scheduling in the IaaS cloud. The idea of the extension is to consider the security overheads in both tasks prioritizing phase and virtual machine selection phase of the four original algorithms. Based on real-world applications, we evaluate the performance of the proposed algorithms in terms of scheduling length, speedup and efficiency.

Cloud computing, a multipurpose and high-performance internet-based computing, can model and transform a large range of application requirements into a set of workflow tasks. It allows users to represent their computational needs conveniently for data retrieval, reformatting, and analysis. However, workflow applications are big data applications and often take long hours to finish executing due to their nature and data size. In this paper, we study the cost optimised scheduling algorithms in cloud and proposed a novel task splitting algorithm named Cost optimised Heuristic Algorithm (COHA) for the cloud scheduler to optimise the execution cost. In this algorithm, the large tasks are split into sub-tasks to reduce their execution time. The design purpose is to enable all tasks to adequately meet their deadlines. We have carefully tested the performance of the COHA with a list of workflow inputs. The simulation results have convincingly demonstrated that COHA can effectively perform VM allocation and deployment, and well handle randomly arrived tasks. It can efficiently reduce execution costs while also allowing all tasks to properly finish before their deadlines. Overall, the improvements in our algorithm have remarkably reduced the execution cost by 32.5% for Sipht, 3.9% for Montage, and 1.2% for CyberShake workflows when compared to the state of art work.

International audience; Infrastructure as a service (IaaS) clouds offer huge opportunities to solve large-scale scientific problems. Executing workflows in such environments can be expensive in time if not scheduled rightly. Although scheduling workflows in the cloud is widely studied, most approaches focus on two user's quality of service requirements namely makespan (i.e., completion time) and costs. Other important features of cloud computing such as the heterogeneity of resources and reliability must be considered. In this paper, we present a reliability-aware method based on discrete particle swarm optimisation (RDPSO) for workflow scheduling in multiple and heterogeneous cloud data centres. Our aim is to optimise data transfer time while minimising makespan and enhancing reliability. Based on simulation, our results show a significant improvement in terms of makespan, transferred data and reliability relative to reliability-aware HEFT method (heterogeneous earliest finish time), for the real-world workflows.

Workflow scheduling in the cloud is the process of allocating tasks to limited cloud resources to maximise resource utilization and minimise makespan. This is often achieved by adopting an effective scheduling heuristic. Most existing heuristics rely on a small number of features when making scheduling decisions, ignoring many impacting factors that are important to workflow scheduling. For example, the MINMIN algorithm only considers the size of the tasks when making scheduling decisions. Meanwhile, many existing works focused on scheduling a static set of workflow tasks, neglecting the dynamic nature of cloud computing. In this paper, we introduce a new and more realistic workflow scheduling problem that considers different kinds of workflows, cloud resources, and impacting features. We propose a Dynamic Workflow Scheduling Genetic Programming (DSGP) algorithm to automatically design scheduling heuristics for workflow scheduling to minimise the overall makespan of executing a long sequence of dynamically arriving workflows. Our proposed DSGP algorithm can work consistently well regardless of the size of workflows, the number of available resources, or the pattern of workflows. It is evaluated on a well-known benchmark dataset by using the popular WorkflowSim simulator. Our experiments show that scheduling heuristics designed by DSGP can significantly outperform several manually designed and widely used workflow scheduling heuristics.

Virtualization is one of the key features of cloud computing, where the physical machines are virtually divided into several virtual machines in the cloud. The user’s tasks are run on these virtual resources as per the requirements. When the user requests the services to the cloud, the user’s tasks are allotted to the virtual resources depending on their needs. An efficient scheduling mechanism is required for optimizing the involved parameters. Scientific workflows deals with a large amount of data with dependency constraints and is used to simplify the applications in the diverse scientific domains. Scheduling the workflow in cloud computing is a well-known NP-hard problem. Deploying such data- and compute-intensive workflow on the cloud needs an efficient scheduling algorithm. In this paper, we have proposed a multi-objective model based hybrid algorithm (HPSOGWO), which combines the desirable characteristics of two well-known algorithms, particle swarm optimization (PSO), and grey wolf optimization (GWO). The results are analyzed under complex real-world scientific workflows such as Montage, CyberShake, Inspiral, and Sipht. We have considered the two essential parameters: total execution time and total execution cost while working in the cloud environment. The simulation results show that the proposed algorithm performs well compared to other state-of-the-art algorithms such as round-robin (RR), ant colony optimization (ACO), heterogeneous earliest time first (HEFT), and particle swarm optimization (PSO).

Workflow scheduling in cloud computing is said to be the most challenging problem in the cloud environment. The problem deals with the scheduling of dependent tasks in the cloud environment for improving the performance of the system. The system comprises heterogeneous resources and varying performance matrices. Use of trust models provides a multi-objective solution to this problem. This chapter introduces workflow scheduling, its performance parameters, and parameters affecting trust-based scheduling. The work also proposes a few workflow scheduling algorithms using the trust model. At the end of this chapter, a comparative study of existing literature and the proposed model is discussed which shows that trust-based algorithm performs better than the existing algorithm.

The edge computing paradigm is featured by the ability to off-load computing tasks from mobile devices to edge clouds and provide high cost-efficient computing resources, storage and network services closer to the edge. A key question for workflow scheduling in the edge computing environment is how to guarantee user-perceived quality of services when the supporting edge services and resources are with unstable, time-variant, and fluctuant performance. In this work, we study the workflow scheduling problem in the multi-user edge computing environment and propose a Deep-Q-Network (DQN) -based multi-workflow scheduling approach which is capable of handling time-varying performance of edge services. To validate our proposed approach, we conduct a simulative case study and compare ours with other existing methods. Results clearly demonstrate that our proposed method beats its peers in terms of convergence speed and workflow completion time.

The cloud IaaS easily offers to have homogeneous multi-core machines (whether they are "bare metal" machines or virtual machines). On each of these machines, there can be high-performance input-output SSD disks. That allows to distribute the files produced during the execution of the workflow to different machines in order to minimize the additional costs associated with transferring these files. In this paper, we propose a scheduling algorithm called WSRDT (Workflow Scheduling Reducing Data Transfers) whose purpose is to minimize the makespan (execution time) of data-intensive workflows by reducing transfers data between dependent tasks on the network. Intermediate files produced by tasks are stored locally on the disk of the machine where the tasks were executed. We experimentally verify that the increase in the number of cores per machine reduces the additional cost due to data transfers on the network. Experiences with a veritable workflow show those advantages of the algorithms presented. Data-driven scheduling significantly reduces the execution time and the volume of data transferred on the network, our approach outperforms one of the best state-of-the-art algorithms that we have adapted with our hypotheses.

Workflow scheduling concerns the mapping of complex tasks to cloud resources by taking into account various Quality of Service requirements. In virtue of continuous proliferation in the exploration of cloud computing, it has become stringent to find the proper scheduling scheme for the execution of workflow under user specifications. Moreover, till date, there exists no systematic review of the existing numerous techniques for this NP-complete problem in the cloud. Taking this into account, the present study seeks to address this gap and spotlights the comprehensive taxonomy of various scheduling schemes as well as extensively compares them by illuminating their objectives, features, merits, and demerits. This paper also highlights the future research challenges with an aim to foster more research in the realm of workflow scheduling as an optimization task.

In recent days most of the enterprises and communities adopt cloud services to deploy their workflow-based applications due to the inherent benefits of cloud-based services. These workflow-based applications are mainly compute-intensive. The major issues of workflow deployment in a cloud environment are minimizing execution time (makespan) and monetary cost. As cloud service providers maintain adequate infrastructural resources, workflow scheduling in the cloud environment becomes a non-trivial task. Hence, in this paper, we propose a scheduling technique where monetary cost is reduced, while workflow gets completed within its minimum makespan. To analyze the performance of the proposed algorithm, the experiment is carried out in WorkflowSim and compares the results with the existing well-known algorithms, Heterogeneous Earliest Finish Time (HEFT) and Dynamic Heterogeneous Earliest Finish Time (DHEFT). In all the experiments, the proposed algorithm outperforms the existing ones.

In various time-slots, the real-time reasoning tasks generated by autonomous vehicles are scheduled within tolerance time, which is the key problem to be solved in autonomous driving. Tasks are traditionally scheduled on the on-board unit (OBU), which leads to long completion time of tasks. Heuristic algorithms are widely used in task scheduling problems, which usually causes to premature convergence. Scheduling tasks in edge environment can effectively reduce completion time of tasks. In this paper, a workflow scheduling strategy was designed in edge environment according to the difference of reasoning tasks and the changes of edge nodes in various time-slots. Firstly, the model of Markov decision process (MDP) was built to describe the problem scenario, and the completion time of reasoning tasks was calculated by the workflow scheduling algorithm. Secondly, the Q-learning algorithm based on simulated anealing (SA-QL) was proposed to optimize the completion time of reasoning tasks. Finally, the performance of reinforcement learning algorithms based on simulated annealing (SA-RL) and particle swarm optimization (PSO) algorithm were comprehensively displayed from four perspectives: effectiveness, feasibility, exploration and convergence. The experimental results show that both SA-RL algorithms and PSO algorithm have good performance in feasibility and effectiveness. TD(0) algorithms have better performance of exploration and TD() algorithms have that of convergence.

In cloud computing, Service rendering is achieved through virtualization and the use of very large scalable resources where users access services by paying for the share of resources used by them. Several workflow scheduling techniques have been proposed for efficient scheduling. Max-min approach is prominent among them, its features includes efficiency and the ability to guarantee optimal or near optimal solution because of its heuristic nature. Several enhancement to its functionalities have been proposed in the literature and works are still ongoing for improving it. This paper proposed a solution by minimizing total execution time where workflow tasks length are invariably considered before proper scheduling. Our proposed work is compared with standard Max-Min scheduling scheme. The proposed scheme outperforms the standard scheme; two realistic scientific workflows were used as test data for the simulation.

Cloud computing is a new computing paradigm which is gaining wide acceptance among scientific fraternity in the recent years. The services of cloud could be effectively used for running large-scale...

Estimation of the execution time is an important part of the workflow scheduling problem. The aim of this paper is to highlight common problems in estimating the workflow execution time and propose a solution that takes into account the complexity and the stochastic aspects of the workflow components as well as their runtime. The solution proposed in this paper addresses the problems at different levels from a task to a workflow, including the error measurement and the theory behind the estimation algorithm. The proposed makespan estimation algorithm can be integrated easily into a wide class of schedulers as a separate module. We use a dual stochastic representation, characteristic/distribution function, in order to combine task estimates into the overall workflow makespan. Additionally, we propose the workflow reductions—operations on a workflow graph that do not decrease the accuracy of the estimates but simplify the graph structure, hence increasing the performance of the algorithm. Another very important feature of our work is that we integrate the described estimation schema into earlier developed scheduling algorithm GAHEFT and experimentally evaluate the performance of the enhanced solution in the real environment using the CLAVIRE platform.

Recent studies focus primarily on low energy consumption or execution time for task scheduling with precedence constraints in heterogeneous computing systems. In most cases, system reliability is more important than other performance metrics. In addition, energy consumption and system reliability are two conflicting objectives. A novel bi-objective genetic algorithm (BOGA) to pursue low energy consumption and high system reliability for workflow scheduling is presented in this paper. The proposed BOGA offers users more flexibility when jobs are submitted to a data center. On the basis of real-world and randomly generated application graphs, numerous experiments are conducted to evaluate the performance of the proposed algorithm. In comparison with excellent algorithms such as multi-objective heterogeneous earliest finish time (MOHEFT) and multi-objective differential evolution (MODE), BOGA performs significantly better in terms of finding the spread of compromise solutions.

Work process is used to speak to variety of use which requires huge data figuring and limit. To vanquish this need of data computation and limit dispersed processing has created as one of the best responses for on asks for resource provider. Nevertheless, on occasion the benefits open to us may not be satisfactory, so the need develops to gather more sources from various fogs. This is done by using the Hybrid cloud. Half and half cloud is mix of open and private cloud. The private cloud is guaranteed by the customer consequently there are no extra charges for using the benefits available in it, however open cloud is controlled by others so we have to pay for the using the advantage as indicated by the businesses. The USAge of the cream cloud offers adaptability to the customer. While using the half breed cloud, two most basic inquiries rises. The first is the best approach to segment the work procedure. In addition, the second one is the thing that benefit we need to get from individuals as a rule cloud so it can meet our essential inside the foreordained due date. The changed booking organization work handle for hybrid cloud give the less make span for the DAG than the main figuring and give us the best resources that we need to secure from open cloud to have enough planning vitality to arrange the work procedure inside given due date. We have gone through with two different approaches after scheduling has been performed to check the successful transaction between dynamic and static data center. We contemplated a hybrid approach which will reduce the complexity of the network but at the same time it also perform successful transaction between dynamic datacenters .Each time the transaction has been performed between different centers or clusters the consequences are successful.

The last decades have seen a considerable progress on workflow scheduling in heterogeneous computing environments. However, existing methods still need to be improved on the performance in the makespan-based metrics. This paper proposes a novel workflow scheduling algorithm named Greedy-Ant to minimize total execution time of an application in heterogeneous environments. First, the ant colony system is applied to scheduling from a new standpoint by guiding ants to explore task priorities and simultaneously assign tasks to machines. Second, forward/backward dependence is defined to indicate the global significance of each node, based on which, a new heuristic factor is proposed to help ants search for task sequences. Finally, a greedy machine allocating strategy is presented. Experimental results demonstrate that Greedy-Ant outperforms the state of the art up to 18% in the metric of speedup.

In a multicluster environment that allocates resources dynamically, clusters' local tasks also arrive dynamically, making it difficult to schedule and respond to the demands on computing resources. When scheduling a complex scientific workflow, there are problems with available resource uncertainty and dynamics. Here, we show how to predict local tasks' running time in a cluster with accuracy, using a flexible system-prediction method for an environment undergoing dynamic resource changes. This method—Waste-Aware Heterogeneous Earliest Finish Time (WA-HEFT)—formulates a resource waste-perception scheduling strategy, reduces resource competition, and reduces the overall completion time for a complex scientific workflow. In addition, when a significant deviation exists between the actual operation of workflow tasks and the initial scheduling, we rearrange the target workflow tasks by formulating an adaptive rescheduling strategy, so that we can complete the target workflow as soon as possible. Simulation results show that our algorithm is superior to existing algorithms in the makespan.

The workflow, in edge computing, is capable of orderly managing a set of computing tasks, via setting up a structured calculation process and flow. However in running time, the resources at edge nodes are often released by other workflows, which results in additional uncertainties on top of those caused by performance degradation and service failure. In addressing these new uncertainties, we propose in this paper an uncertainty-aware resource provisioning (UARP) method for scheduling workflow in the software-defined network-based(SDN-based) edge computing environment. The proposed UARP ensures optimal in that the non-dominated sorting genetic algorithm-III (NSGA-III) is employed to elaborate the workflow scheduling strategy. Extensive experiments are carried out for method evaluations and the results show that the UARP method is effective in reducing uncertainty, shortening processing time and bringing down energy consumption.

Cloud computing is a pattern of distributed computing, involved in a large number of resources and requests, which aims to share the resources as services over the Internet. Since the optimal use of these services is an endless challenge, the problem of task and workflow scheduling in cloud computing is important. Scheduling algorithms try to provide optimal scheduling to increase resource efficiency and, as a result, increase computational power. In this paper, various types of scheduling algorithms have been categorised. Then, several scheduling methods in the cloud computing environment are reviewed. Ultimately, the algorithms are tabulated presenting their advantages and disadvantages along with their scheduling tools and factors.

Cloud computing can provide powerful and reliable services for the increasing complex applications. The application workflows can be migrated to the cloud resources for processing. Most existing meta-heuristic and heuristic algorithms concentrate on orchestrating workflows under users’ Quality of Service parameters, nevertheless, the user priority is usually neglected. In this paper, we design a new scheduling algorithm named User Priority Aware and Cost Constrained Workflow Scheduling (UPCWS) to execute multiple applications arriving at the same instant in heterogeneous distributed systems, which takes both user priority and QoS parameters into consideration. The algorithm decomposes the global workflow demands into sub-constraints of tasks and filters all available processors to obtain the suitable resources which satisfy the cost constraints. The algorithm of UPCWS can automatically determines the execution order of tasks by pre-scheduling and real time adjustment according to the priority of workflows. Experimental results indicate that the UPCWS algorithm can obtain higher planning success rate comparing against several state-of-the-art algorithms with a smaller monetary cost. It’s also proved that the algorithm yields the best performance for the high priority users in the situation of changing the number of workflows.

A scientific workflow is a set of interdependent compute tasks orchestrating large scale data analyses or in-silico experiments. Workflows often comprise thousands of tasks with heterogeneous resource requirements that need to be executed on distributed resources. Many workflow engines solve parallelization by submitting tasks to a batch scheduling system, which requires resource usage estimates that have to be provided by users. We investigate the possibility to improve upon inaccurate user estimates by incorporating an online feedback loop between workflow scheduling, resource usage prediction, and measurement.Our approach can learn resource usage of arbitrary type; in this paper, we demonstrate its effectiveness by predicting peak memory usage of tasks, as it is an especially sensitive resource type that leads to task termination if underestimated and leads to decreased throughput if overestimated.We compare online versions of standard machine learning models for peak memory usage prediction and analyze their interactions with different workflow scheduling strategies. By means of extensive simulation experiments, we found that the proposed feedback mechanism improves resource utilization and execution times compared to typical user estimates.

We study a difficult problem of how to schedule complex workflows with precedence constraints under a limited budget in the cloud environment. We first formulate the scheduling problem as an integer programming problem, which can be optimized and used as the baseline of performance. We then consider the traditional approach of scheduling jobs in a prioritized order based on the upward-rank of each job. For those jobs with no precedence constraints among themselves, the plain upward-rank priority scheme assigns priorities in an arbitrary way. We propose a job prioritization scheme that uses Markovian chain stationary probabilities as a measure of importance of jobs. The scheme keeps the precedence order for the jobs that have precedence constraints between each other, and assigns priorities according to the jobs' importance for the jobs without precedence constraints. We finally design a uniform spare budget splitting strategy, which splits the spare budget uniformly across all the jobs. We test our algorithms on a variety of workflows, including FFT, Gaussian elimination, typical scientific workflows, randomly generated workflows and workflows from an in-production cluster of an online streaming service company. We compare our algorithms with the-state-of-art algorithms. The empirical results show that the uniform spare budget splitting scheme outperforms the splitting scheme in proportion to extra demand in average for most cases, and the Markovian based prioritization further improves the workflow makespan., 16 pages, submitted to IEEE ACCESS journal on Feb. 28

Workflow scheduling is one of the important issues in implementing workflows in the cloud environment. Workflow scheduling means how to allocate workflow resources to tasks based on requirements and features of the tasks. The problem of workflow scheduling in cloud computing is a very important issue and is an NP problem. The relevant scheduling algorithms try to find optimal scheduling of tasks on the available processing resources in such a way some qualitative criteria when executing the entire workflow are satisfied. In this paper, we proposed a new scheduling algorithm for workflows in the cloud environment using Cuckoo Optimization Algorithm (COA). The aims of the proposed algorithm are reducing the processing and transmission costs as well as maintaining a desirable load balance among the processing resources. The proposed algorithm is implemented in MATLAB and its performance is compared with Cat Swarm Optimization (CSO). The results of the comparisons showed that the proposed algorithm is superior to CSO in discovering optimal solutions.

Cloud computing is based on sharing of resources to make a service economical same as a public utility. Nowadays, the most prominent application of cloud computing is workflow scheduling. Workflow consists of a repeatable pattern of business activity that needs to be executed sequentially in a diagram or checklist, whether with the help of a colleague, some tool, or using another process. Scheduling means mapping of these business activities to available resources keeping in mind the QoS constraints to achieve. A various number of workflow scheduling algorithms have been proposed till now for scheduling workflows on cloud environment. This paper provides a comprehensive classification of some of the recently used algorithms in this sector. Besides the algorithm’s illustration, the paper also states comparison in these algorithms for better clarification of their objectives and limitations as well as scope and research problems in this sector.

Workflow scheduling is one of the key issues in the management of workflow execution. Typically, a workflow application can be modeled as a Directed-Acyclic Graph (DAG). In this paper, we present GoDAG, an approach that can learn to well schedule workflows on multi-resource clusters. GoDAG directly learns the scheduling policy from experience through deep reinforcement learning. In order to adapt deep reinforcement learning methods, we propose a novel state representation, a practical action space and a corresponding reward definition for workflow scheduling problem. We implement a GoDAG prototype and a simulator to simulate task running on multiresource clusters. In the evaluation, we compare the GoDAG with three state-of-the-art heuristics. The results show that GoDAG outperforms the baseline heuristics, leading to less average makespan to different workflow structures.