Workflow Scheduling Algorithm Research Articles

An electricity price and energy-efficient workflow scheduling in geographically distributed cloud data centers

The cloud computing platform has become a favorable destination for running cloud workflow applications. However, they are primarily complicated and require intensive computing. Task scheduling in cloud environments, when formulated as an optimization problem, is proven to be NP-hard. Thus, efficient task scheduling plays a decisive role in minimizing energy costs. Electricity prices fluctuate depending on the vending company, time, and location. Therefore, optimizing energy costs has become a serious issue that one must consider when building workflow applications scheduling across geographically distributed cloud data centers (GD-CDCs). To tackle this issue, we have suggested a dual optimization approach called electricity price and energy-efficient (EPEE) workflow scheduling algorithm that simultaneously considers energy efficiency and fluctuating electricity prices across GD-CDCs, aims to reach the minimum electricity costs of workflow applications under the deadline constraints. This novel integration of dynamic voltage and frequency scaling (DVFS) with energy and electricity price optimization is unique compared to existing methods. Moreover, our EPEE approach, which includes task prioritization, deadline partitioning, data center selection based on energy efficiency and price diversity, and dynamic task scheduling, provides a comprehensive solution that significantly reduces electricity costs and enhances resource utilization. In addition, the inclusion of both generated and original data transmission times further differentiates our approach, offering a more realistic and practical solution for cloud service providers (CSPs). The experimental results reveal that the EPEE model produces better success rates to meet task deadlines, maximize resource utilization, cost and energy efficiencies in comparison to adapted state-of-the-art algorithms for similar problems.

Multi-priority scheduling algorithm for scientific workflows in cloud

The public cloud environment has emerged as a promising platform for exe-cuting scientific workflows. These executions involve leasing virtual machines (VMs) from public services for the duration of the workflow. The structure of the workflows significantly impacts the performance of any proposed scheduling approach. A task within a workflow cannot begin its execution before receiving all the required data from its preceding tasks. In this paper, we introduce a multi-priority scheduling approach for executing workflow tasks in the cloud. The key component of the proposed approach is a mechanism that logically or-ders and groups workflow tasks based on their data dependencies and locality. Using the proposed approach, the number of available VMs influences the num-ber of groups (partitions) obtained. Based on the locality of each group’s tasks, the priority of each group is determined to reduce the overall execution delay and improve VM utilization. As the results demonstrate, the proposed approach achieves a significant reduction in both execution costs and time in most scenar-ios

Secure workflow scheduling algorithm utilizing hybrid optimization in mobile edge computing environments

The rapid advancement of mobile communication technology and devices has greatly improved our way of life. It also presents a new possibility that data sources can be used to accomplish computing tasks at nearby locations. Mobile Edge Computing (MEC) is a computing model that provides computer resources specifically designed to handle mobile tasks. Nevertheless, there are certain obstacles that must be carefully tackled, specifically regarding the security and quality of services in the workflow scheduling over MEC. This research proposes a new method called Feedback Artificial Remora Optimization (FARO)-based workflow scheduling method to address the issues of scheduling processes with improved security in MEC. In this context, the fitness functions that are taken into account include multi-objective, such as CPU utilization, memory utilization, encryption cost, and execution time. These functions are used to enhance the scheduling of workflow tasks based on security considerations. The FARO algorithm is a combination of the Feedback Artificial Tree (FAT) and the Remora Optimization Algorithm (ROA). The experimental findings have demonstrated that the developed approach surpassed current methods by a large margin in terms of CPU use, memory consumption, encryption cost, and execution time, with values of 0.012, 0.010, 0.017, and 0.036, respectively.

An exploration of online-simulation-driven portfolio scheduling in Workflow Management Systems

Workflow Management Systems used to automate the execution of scientific workflow applications on parallel and distributed computing platforms must make scheduling decisions at runtime. A large number of workflow scheduling algorithms have been proposed in the literature, but often these algorithms are evaluated based on simplifying assumptions that may not hold in practice. Furthermore, published algorithm evaluation and/or comparison results are necessarily only for a subset of all possible scenarios, and thus may not include scenarios relevant to particular use-cases. Consequently, it is difficult for Workflow Management Systems (WMSs) developers to decide which scheduling algorithm should be implemented. To obviate this difficulty, one possible approach is to implement a portfolio of scheduling algorithms and select the most effective algorithm at runtime. One method for performing this selection is to run an online simulation for each algorithm in the portfolio. The algorithm that leads to the best performance, in simulation, is selected for future use.The above simulation-driven portfolio scheduling (SDPS) approach has been proposed in a few parallel and distributed computing contexts. The main objective of this work is to evaluate the feasibility and potential merit of SDPS if implemented in WMSs. We perform this evaluation using simulated WMS executions, where the simulations are instantiated from real-world platform and workflow configurations. Our main finding is that SDPS is on par with or outperforms an approach in which a single algorithm is used, where this algorithm is the one that performs best on average across all our experimental scenarios. Furthermore, we find that SDPS remains an attractive proposition even in the presence of high levels of simulation error and for simulators with relatively low levels of sophistication. In many of our experimental scenarios we find that mitigating simulation error at runtime can further improve performance. Finally, we show that simulation overhead can be made sufficiently low for SDPS to be feasible in practice.

Multi-strategy improved sand cat optimization algorithm-based workflow scheduling mechanism for heterogeneous edge computing environment

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

Cost-aware quantum-inspired genetic algorithm for workflow scheduling in hybrid clouds

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

CPTF–a new heuristic based branch and bound algorithm for workflow scheduling in heterogeneous distributed computing systems

CPTF–a new heuristic based branch and bound algorithm for workflow scheduling in heterogeneous distributed computing systems

Makespan minimization for workflows with multiple privacy levels

With the advanced development of Metaverse, various service requests are required to be processed as soon as possible which contains a series of tasks with topology structure (workflow) and different privacy constraints. Due to the diversity and complex hierarchical structure of resources, how to rent suitable resources from cloud, edge, and local nodes to minimize the makespan of workflows is currently one of the key issues in service computing scenarios. Due to the limited computing resources of local devices, tasks need to be offloaded to edge or cloud servers. Based on task privacy constraints, it is challenging to determine the appropriate offloading nodes for each task in the workflow. In this paper, for workflows with multi-privacy level tasks, a privacy-preserving workflow scheduling algorithm based on clustering (PWCSA) is proposed to minimize the workflow makespan with limited bandwidth. Multiple tasks of different privacy levels are clustered into one task pipeline to reduce the data transmission time caused by limited bandwidth, thereby reducing workflow makespan. After statistically calibrating algorithm parameters over a comprehensive set of workflow instances, the proposed algorithms are compared with the state-of-the-art algorithms over five workflow-type instances. The results indicate that the proposed PWCSA algorithm outperforms other algorithms with acceptable computational time. Through a number of workflow instances, the system parameters are verified by analysis of variance methods.

A Survey on Multiple Workflow Scheduling Algorithms in Cloud Environment

The workflow approach is a standard for displaying processes and their implementation process. With the advent of electrical sciences, more cumbersome workflows were created with more processing requirements. New distributed systems, such as grid systems and computing clouds, allow users to access heterogeneous resources that are geographically located at different points and execute their workflow tasks. Therefore, the simultaneous receipt and execution of several workflows is obvious. As a result of discussing scheduling algorithms, it is necessary to consider arrangements for implementing multiple workflows on a shared resource set. Improving the execution of multiple workflows can accelerate the process of obtaining results when sending processes to the cloud. In this paper, we first discuss the classification of multiple workflow scheduling algorithms, and then briefly describe the scheduling algorithms for the cloud environment, andfinally, the algorithms of papers were compared with each other

Cloud-edge-end workflow scheduling with multiple privacy levels

The cloud-edge-end architecture satisfies the execution requirements of various workflow applications. However, owing to the diversity of resources, the complex hierarchical structure, and different privacy requirements for users, determining how to lease suitable cloud-edge-end resources, schedule multi-privacy-level workflow tasks, and optimize leasing costs is currently one of the key challenges in cloud computing. In this paper, we address the scheduling optimization problem of workflow applications containing tasks with multiple privacy levels. To tackle this problem, we propose a heuristic privacy-preserving workflow scheduling algorithm (PWHSA) designed to minimize rental costs which includes time parameter estimation, task sub-deadline division, scheduling sequence generation, task scheduling, and task adjustment, with candidate strategies developed for each component. These candidate strategies in each step undergo statistical calibration across a comprehensive set of workflow instances. We compare the proposed algorithm with modified classical algorithms that target similar problems. The experimental results demonstrate that the PWHSA algorithm outperforms the comparison algorithms while maintaining acceptable execution times.

Designing heuristic driven hybrid optimization algorithms for efficient workflow scheduling in IaaS cloud

<p class="Abstract">Each one of the real resources or hardware like workstations, work ranges, joins, switches, switches, server ranches, and capacity contraptions are fundamental for the establishment. In disseminated computing, all the establishment is virtualized and given to infrastructure as a service. Usually called IaaS. IaaS quickly increments or down with ask and avoids the got to procure genuine servers and distinctive server cultivate establishment; each resource is displayed as a specific help portion. A conveyed computing pro-organization bargains with the establishment, whereas the client presents, plans, and manages programming, counting applications, middleware, and working systems. IaaS dispersed computing offers clients induction to figuring resources like servers, stockpiling, and frameworks organization. Affiliations utilize their possess establishment and applications interior a master organization’s system. For cloud service providers, cloud schedulers automate IT procedures. Schedulers are used by end users to automate jobs, or tasks, that support everything from big data pipelines to machine learning processes to cloud infrastructure. Infrastructure as a service, or IaaS, is a type of cloud computing that uses the internet to provide virtualized computing resources. IaaS is one of the three main types of cloud computing services, along with Platform as a Service (PaaS) and Software as a Service (SaaS). The precise task is assigned to the CPU, the network, and the storage by resource scheduling. The point behind this is the outrageous use of assets. However, both cloud providers and users require well-organized scheduling.</p>

SpotDAG: An RL-based algorithm for DAG workflow scheduling in heterogeneous cloud environments

SpotDAG: An RL-based algorithm for DAG workflow scheduling in heterogeneous cloud environments

Transfer Learning Based Multi-Objective Evolutionary Algorithm for Dynamic Workflow Scheduling in the Cloud

Transfer Learning Based Multi-Objective Evolutionary Algorithm for Dynamic Workflow Scheduling in the Cloud

The Application of Optimization Algorithms for Workflow Scheduling Based on Cloud Computing IaaS Environment in Industry Multi-Cloud Scenarios

The Application of Optimization Algorithms for Workflow Scheduling Based on Cloud Computing IaaS Environment in Industry Multi-Cloud Scenarios

Classification-Based Diverse Workflows Scheduling in Clouds

Cloud workflow scheduling is a typical combinatorial optimization problem and becomes more challenging due to the increasing diversity of workflows. However, current research employs the same scheduling strategy on diverse workflows. In fact, a scheduling strategy may perform well on one workflow but poorly on other workflows owning to the unique characteristics of each workflow. Therefore, in practical applications, selecting suitable scheduling strategies for diverse workflows is a critical issue. To solve it, this paper investigates a diverse workflows scheduling problem and presents a classification-based workflow scheduling framework, which includes workflow parser, workflow classifier, workflow scheduler, resource manager and workflow status tracker, to manage and schedule diverse workflows using suitable strategies. Based on the framework, we propose a classification-based workflow scheduling algorithm (CWSA) to optimize the economic cost of workflow execution under deadline constraints. We conduct the experiments using diverse workflow instances randomly generated from five types of real-world workflows to evaluate the proposed CWSA approach. The results demonstrate the superiority of CWSA compared with the state-of-the-art approaches. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Diverse workflows (i.e., many workflows with various types, such as Montage, LIGO and Cybernetics) in clouds are widespread. How to efficiently schedule them in cloud is very important. This paper formulates the diverse workflows scheduling problem and proposes a CWSA to solve it. The basic idea of CWSA is to select a suitable scheduling strategy for each workflow. Specifically, in CWSA, we design a classification neural network architecture that consists of a graph neural network and a fully connected neural network to classify each workflow to its suitable deadline distribute strategy by its characteristics and deadline constraint. Then CWSA obtains the sub-deadlines of tasks and assigns tasks to appropriate VMs (Virtual Machines). Furthermore, as an important factor in workflow scheduling, the transmission time between dependent tasks is introduced into the graph neural network, which improves the classification accuracy.

A survey on energy‐efficient workflow scheduling algorithms in cloud computing

AbstractThe 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.

Multi-objective secure aware workflow scheduling algorithm in cloud computing based on hybrid optimization algorithm

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

Efficient Prediction of Makespan Matrix Workflow Scheduling Algorithm for Heterogeneous Cloud Environments

Efficient Prediction of Makespan Matrix Workflow Scheduling Algorithm for Heterogeneous Cloud Environments

A multi-hierarchy particle swarm optimization-based algorithm for cloud workflow scheduling

In this paper, we consider the market-driven workflow scheduling problem on heterogeneous cloud resources with deadline constraints. The transmission delay between dependent tasks on different virtual machines (VMs) is considered. The objective is to minimize the total monetary cost, which is computed based on the on-demand price structure. Inspired by the heuristic and meta-heuristic algorithms, a multi-hierarchy particle swarm optimization (MHPSO) algorithm is proposed, which mainly consists of three components: (1) the workflow aggregation method to pretreat the workflow structure considering the transmission cost and the VM utilization; (2) the initial population generating method to create a set of initial particles with the given encoding method and the solution generation method; and (3) the hierarchical evolving process where particles are divided into multiple groups and evolved iteratively. We calibrate the parameters and components of the proposal statistically over five well-known workflows with different deadline settings. A comparison of the proposed algorithm to existing methods for the considered problem is carried out. Experimental results demonstrate the proposal is effective for the problem under study.

HEPGA: A new effective hybrid algorithm for scientific workflow scheduling in cloud computing environment

Cloud data center comprises various physical and virtual machines, alongside storage datacenter services provided by cloud providers. Effectively mapping tasks to optimize resource utilization and load balance is essential for efficient task scheduling. This process, referred to as scheduling constraints, can significantly enhance overall efficiency. However, to harness the benefits of this scheduling, one must address the challenges arising during task execution. The interdependencies between tasks and the diverse resources available in the datacenter pose significant hurdles to efficient resource allocation. To address these challenges, this paper introduces the Hybrid HEFT-PSO-GA algorithm (HEPGA), aiming to efficiently allocate tasks to available resources across the datacenter. The HEPGA algorithm builds upon prior research by integrating the strengths of PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) to optimize task scheduling in cloud computing environments. Through the fusion of PSO, GA, and HEFT-based Initialization, the algorithm strives to efficiently allocate tasks to processors, thereby minimizing the makespan. This approach capitalizes on parallel processing capabilities to further enhance resource utilization in the cloud environment. By varying the weights of the fitness function and considering the resources within the datacenter, we meticulously analyze the algorithm's performance concerning both makespan and Resource Utilization (RU). The results of these tests underscore the algorithm's consistent and robust resource utilization across diverse weight configurations, highlighting its adaptability to varying priorities. Moreover, the observed variations in makespan performance based on different weights emphasize the algorithm's potential for excellence when tailored to specific optimization goals.