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workflow scheduling using Jaya algorithm in cloud

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2019年第17期31卷 e5251-e5251页

作者： Gupta, Swati Agarwal, Isha Singh, Ravi Shankar Indian Inst Technol BHU Dept Comp Sci & Engn Varanasi 221005 Uttar Pradesh India

Cloud computing is an on-demand service that can be accessed by a user according to his requirements through the Internet. Multiple users can request any amount of services, so scheduling of those services is a crucial task in cloud computing. scheduling is a way of assigning the work to a computer resource. We have multiple tasks at a time that are waiting to be allotted to multiple computer resources. Various optimization algorithms have been used to do task scheduling so that total execution cost is minimized. In this paper, we have implemented Jaya optimization algorithm for workflow scheduling and have compared it with four nature-inspired algorithms, namely, particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), honey bee, and cat swarm optimization (CSO), keeping the fitness function same for all of them using CloudSim. Previously, work has been done on PSO, GA, ACO, honey bee, and CSO using different criteria. The results are compared on the basis of execution cost and makespan of the algorithm on both an independent set of tasks and a set of tasks that follow a workflow schedule. Benchmark functions such as Montage, CyberShake, Inspiral, and Sipht are used for workflow scheduling. It has been observed that Jaya outperforms the other algorithms as it produces similar results in the least amount of time as it converges very quickly.

关键词： cloud computing execution cost independent task Jaya makespan workflow scheduling

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workflow scheduling in the Cloud With Weighted Upward-Rank Priority Scheme Using Random Walk and Uniform Spare Budget Splitting

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IEEE ACCESS 2019年 7卷 60359-60375页

作者： Zhang, Hang Zheng, Xiaoying Xia, Ye Li, Mingqi Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Chinese Acad Sci Shanghai Adv Res Inst Shanghai 201210 Peoples R China Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA

We study a difficult problem of how to schedule complex workfiows 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 the Markovian chain stationary probabilities as a measure of the 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 that splits the spare budget uniformly across all the jobs. We test our algorithms on a variety of workfiows, including the Fast Fourier transform (FFT), the Gaussian elimination, typical scientific workfiows, randomly generated workfiows, and workfiows from an in-production cluster of an online streaming service company. We compare our algorithms with state-of-the-art algorithms. The empirical results show that the uniform spare budget splitting scheme outperforms the splitting scheme in proportion to extra demand on average for most cases, and the Markovian-based prioritization further improves the workfiow makespan.

关键词： workflow scheduling heterogeneous clouds budget constraints precedence constraints schedule length

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workflow scheduling in cloud computing environment with classification ordinal optimisation using SVM

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2021年第6期24卷 563-571页

作者： Samandi, Vahab Mukhopadhyay, Debajyoti Univ Birmingham Sch Comp Sci Birmingham W Midlands England Bennett Univ Dept Comp Sci & Engn WIDiCoReL Res Lab Greater Noida India

Every day, civilisation generates more and more data. The processing cost and performance issues of this massive set of data have become a challenge in distributed computing. Processing multitasks workloads for big-data in a dynamic environment requires real-time scheduling and the additional complication of generating optimal schedules in a large search space with high overhead. In this paper, we propose an adaptive workflow management system that uses ordinal optimisation to acquire suboptimal schedules in much less time. We then introduce a prediction-based workflow scheduler model that predicts the execution time of the next coming workflow by using a support vector machine (SVM). We used a real application Montage workflow for large-volume image data, and the experimental results show that our classification ordinal optimisation (COO) outperforms other existing methods.

关键词： cloud computing workflow scheduling classification ordinal optimisation COO support vector machine SVM ordinal optimisation Montage workflow and big data

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workflow scheduling in Serverless Edge Computing for the Industrial Internet of Things: A Learning Approach

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第7期19卷 8242-8252页

作者： Xie, Renchao Gu, Dier Tang, Qinqin Huang, Tao Yu, Fei Richard Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Shenzhen Univ Guangdong Lab Artificial Intelligence & Digital Ec Shenzhen 518107 Peoples R China Carleton Univ Sch Informat Technol Ottawa ON K1S 5B6 Canada

Serverless edge computing is seen as a promising enabler to execute differentiated Industrial Internet of Things (IIoT) applications without managing the underlying servers and clusters. In IIoT serverless edge computing, IIoT workflow scheduling for cloud-edge collaborative processing is closely related to the service quality of users. However, serverless functions decomposed by IIoT applications are limited in their deployment at the edge due to the resource-constrained nature of edge infrastructures. In addition, the scheduling of complex IIoT applications supported by serverless computing is more challenging. Therefore, considering the limited function deployment and the complex dependencies of serverless workflows, we model the workflow application as directed acyclic graph and formulate the scheduling problem as a multiobjective optimization problem. A dueling double deep Q-network-based solution is proposed to make scheduling decisions under dynamically changing systems. Extensive simulation experiments are conducted to validate the superiority of the proposed scheme.

关键词： Deep reinforcement learning (DRL) multi-objective optimization serverless edge computing workflow scheduling

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workflow scheduling and reliability improvement by hybrid intelligence optimization approach with task ranking

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2020年第24期7卷 1-10页

作者： Khurana, S. Singh, R. K. IK Gujral Punjab Tech Univ Jalandhar Punjab India SUS Inst Comp Mohali Punjab India

workflow scheduling is one of the most challenging tasks in cloud computing. It uses different workflows and quality of service requirements based on the deadline and cost of the tasks. The main goal of workflow scheduling algorithm is to optimize the time and cost by using virtual machine migration. This algorithm computes the subset problem and decision problem in NP time. It works on the decision-making process to reduce the time and cost of computation on the server side. This paper proposes hybrid optimization to optimize the virtual machine locally and globally. The PEFT algorithm is used for initialization and worked as a heuristic algorithm. This algorithm reduces the error of random initialization of optimization. The proposed algorithm based upon Flower pollination with Grey Wolf Optimization using hybrid approach shows significant end effective results than flower pollination with genetic algorithm. The proposed approach also considered the reliability parameter on different workflows.

关键词： workflow scheduling Reliability Cloud Virtualization Hybrid Optimization

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workflow scheduling method under unbalanced conditions based on dynamic allocation algorithm

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International Journal of Reasoning-based Intelligent Systems 2023年第3-4期15卷 290-296页

作者： Zhao, Wenyan Hou, Lixia Zhao, Jie Information Engineering Department Hebei Tangshan Polytechnic College Tangshan063299 China Department of Modern Services Hebei Women's Vocational College Shijiazhuang05000 China Information Engineering Department Hebei Institute of Mechanical and Electrical Technology Xing Tai054000 China

In order to improve the efficiency and fairness of workflow scheduling, consider the data correlation between task nodes, and adapt to different scenarios, an unbalanced workflow scheduling method based on dynamic allocation algorithm is proposed. The workflow structure is transformed to calculate the earliest and the latest start time of the node task. The workflow stage is divided, and the input parameters of the task are dynamically adjusted according to the output data amount of each stage of the workflow. The workflow scheduling model is established. Taking the total length of workflow scheduling as the solution goal, the global optimal solution of the whole workflow scheduling process is calculated and obtained. The experimental results show that the unfairness coefficient of this method is less than 0.15%, and the time span can be controlled within 150 s, which shows that this method is more effective and can ensure the running ability of workflow. © 2023 Inderscience Publishers. All rights reserved.

关键词： dynamic allocation algorithm workflow stage non-equilibrium condition workflow scheduling

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workflow scheduling with data transfer optimisation and enhancement of reliability in cloud data centres

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INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS 2020年第3期24卷 262-283页

作者： Oukfif, Karima Oulebsir-Boumghar, Fatima Bouzefrane, Samia Banerjee, Soumya Univ Mouloud Mammeri Tizi Ouzou Dept Comp Sci Tizi Ouzou Algeria USTHB LRPE Lab Algiers Algeria USTHB LRPE Lab Fac Elect & Comp Sci FEI Algiers Algeria Conservatoire Natl Arts & Metiers CEDRIC Lab Paris France Birla Inst Technol Ranchi Bihar India

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.

关键词： cloud computing workflow scheduling data transfer reliability discrete particle swarm optimisation

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workflow scheduling Algorithms in Cloud Environment: a Review, Taxonomy, and Challenges 4

Workflow Scheduling Algorithms in Cloud Environment: a Revie...

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4th IEEE International Conference on Parallel, Distributed and Grid Computing (IEEE PDGC)

作者： Choudhary, Anita Govil, M. C. Singh, Girdhari Awasthi, Lalit K. MNIT Dept Comp Sci & Engn Jaipur Rajasthan India NIT Dept Comp Sci & Engn Hamirpur India

ISBN: (纸本)9781509036691

Cloud computing offers Information and Communication Technologies (ICT) user's new aspects of ease of services through the Internet. Cloud is a gradual evolution of distributed, cluster, grid and utility computing. Cloud environment attracts the myriads of service providers for development and deployment of their application because it provides ubiquitous and on-demand access to shared resources. The majority of cloud applications like scientific applications are represented as workflow. workflow is related to the automation of a process that represented by task and data/control information is passed between dependent tasks based on defined set of principles, execution of these tasks require different resources with different Quality of Service parameters. workflow scheduling is most important concerned in a cloud computing environment because it depends on optimization objectives (like makespan, cost, reliability, security, energy consumption and energy cost, load balancing etc.) of scientific applications. In this review paper, we describe various scientific applications, taxonomy and comprehensive survey of existing workflow scheduling algorithms. We also, classify these algorithms based on nature of the algorithm, optimization model used, optimization objectives and tool used.

关键词： cloud computing scientific workflow workflow scheduling optimization model optimization objectives

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workflow scheduling IN HETEROGENEOUS COMPUTING SYSTEMS : A SURVEY

WORKFLOW SCHEDULING IN HETEROGENEOUS COMPUTING SYSTEMS : A S...

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International Conference on Computing and Communication Technologies for Smart Nation (IC3TSN)

作者： Gupta, Ashish Garg, Ritu Natl Inst Technol Dept Comp Engn Kurukshetra Haryana India

ISBN: (纸本)9781538606278

In the world of managing and processing large data sets significantly and executing such complex applications on heterogeneous systems, efficient scheduling is of prime concern. The workflow scheduling deals with mapping of tasks on the available resources while maintaining the precedence constraints and guaranteeing Quality-of-Service requirements and fairness in execution of workflow. Numerous workflow scheduling schemes have been presented by authors in the past. In this paper, we are providing the comprehensive survey of workflow scheduling techniques by classifying them based upon the workflow structures, scheduling criteria's, scheduling strategies in heterogeneous systems. We classified workflow-scheduling structures as DAG and Non-DAG structures, scheduling criteria's on the basis of performance driven, market driven and trust driven, workflow-scheduling strategies into Heuristic, Meta Heuristic and Hybrid scheduling We also considered the scheduling issues in various other fields of engineering;Hadoop Based, Map-Reduce Based, Agent Based. A comparative analysis of each scheduling algorithm is highlighted through their objectives, algorithm proposed, properties and environment in tabular form. The survey results are used to identity workflow scheduling approaches and the areas that need further research.

关键词： workflow scheduling Task scheduling. Meta Heuristic BAG Ileuristic,Heterogeneous computing system

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workflow scheduling in e-Science Networks

Workflow Scheduling in e-Science Networks

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16th IEEE Symposium on Computers and Communications (lSCC)

作者： Jung, Eun-Sung Ranka, Sanjay Sahni, Sartaj Samsung Elect ICL Lab Samsung Adv Inst Technol Yongin 446712 South Korea Univ Florida Dept CISE Gainesville FL 32611 USA

ISBN: (纸本)9781457706783

We solve workflow scheduling problems in e-Science networks, whose goal is minimizing either makespan or network resource consumption by jointly scheduling heterogeneous resources such as compute and network resources. We formulate the workflow scheduling problem incorporating multiple paths as a mixed integer linear programming (MILP) and develop several linear programming relaxation heuristics based on this formulation. Our algorithms allow dynamic multiple paths for data transfer between tasks and more flexible resource allocation that may vary over time. We evaluate our algorithms against a well-known list scheduling algorithm in e-Science networks whose size is relatively small. Our simulation results show that our heuristics are fast and work well when communication-to-computation ratios (CCRs) are small. Also, these results show that use of dynamic multiple paths and malleable resource allocation is useful for data intensive applications.

关键词： workflow scheduling e-Science dynamic path

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