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Scoring and Dynamic Hierarchy-Based NSGA-II for Multiobjective workflow scheduling in the Cloud

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2022年第2期19卷 982-993页

作者： Li, Huifang Wang, Binyang Yuan, Yan Zhou, MengChu Fan, Yushun Xia, Yuanqing Beijing Inst Technol Key Lab Complex Syst Intelligent Control & Decis Beijing 100081 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Cloud computing becomes a promising technology to reduce computation cost by providing users with elastic resources and application-deploying environments as a pay-per-use model. More scientific workflow applications have been moved or are being migrated to the cloud. scheduling workflows turns to the main bottleneck for increasing resource utilization and quality of service (QoS) for users. This work formulates workflow scheduling as multiobjective optimization problems and proposes a Scoring and Dynamic Hierarchy-based NSGA-II (Nondominated Sorting Genetic Algorithm II), called SDHN for short, to minimize both makespan and cost of workflow execution. First, a scoring criterion is developed to calculate the total score for each individual during population updating, which is used as a quantitative index to evaluate the dominance degree of individuals among the whole population. Hence, SDHN can distinguish individuals within the same dominance level and target its search toward the directions of elite solutions as their different dominance degrees and accordingly improve search efficiency. Second, a population-based dynamic hierarchical structure (HS) and its evolutionary rules are presented to update HS by comparing each child with all parental individuals from bottom to up until finding a proper dominant level. Since traversing all HS levels is not needed in most cases, the number of individual comparisons is reduced and SDHN's updating efficiency is greatly improved, especially for large-scale and complex applications. Third, to guarantee its converging to the near-optimal solutions, adaptive adjustment strategies (AASs) are designed to prevent the search from falling into local optima or diverging by checking the number of individuals at the highest HS level and then modifying the relevant genetic operations to guide the evolutionary process to approach the global Pareto Front. Extensive experiments are conducted to verify SDHN, and the results show that it outp

关键词： Task analysis Optimization Cloud computing Dynamic scheduling Quality of service Heuristic algorithms Statistics Cloud computing metaheuristics multiobjective optimization workflow scheduling

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A novel cost-efficient approach for deadline-constrained workflow scheduling by dynamic provisioning of resources

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年第Part1期79卷 95-110页

作者： Singh, Vishakha Gupta, Indrajeet Jana, Prasanta K. Indian Inst Technol ISM Dept Comp Sci & Engn Dhanbad 826004 Bihar India

workflow scheduling is a crucial aspect of cloud computing that should be performed in an efficient manner for optimal utilization of resources. The development of a cost-efficient algorithm has always been an important topic of research in this regard. In this paper, we propose a novel workflow scheduling algorithm, which is cost-efficient and deadline-constrained. The proposed algorithm is consolidated by dynamic provisioning of the resources, using k-means clustering technique and a variant of the Subset Sum problem. In the algorithm, we consider level based scheduling using the concept of Bag of Tasks (bots) and develop a new technique for associating deadlines with each bot. Through extensive simulation runs, we show that the proposed algorithm outperforms the existing algorithms like Dynamic Provisioning Dynamic scheduling (DPDS) and Infrastructure as a Service (IaaS) Cloud-Partial Critical Path (IC-PCP). The effectiveness of our algorithm over these two algorithms is also illustrated through the popular statistical test ANOVA and its subsequent post-hoc analysis. (c) 2017 Elsevier B.V. All rights reserved.

关键词： workflow scheduling Dynamic provisioning Partition problem k-means clustering Cost Deadline

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A reactive search optimization algorithm for scientific workflow scheduling using clustering techniques

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第2期12卷 3199-3207页

作者： Karpagam, M. Geetha, K. Rajan, C. Excel Engn Coll Dept CSE Salem Tamil Nadu India KS Rangasamy Coll Technol Dept IT Namakkal Tamil Nadu India

Cloud computing is the style that can give plenty of shared pool resources such as hardware or software to clients based on requests from the internet. These resources are then scaled up automatically based on the specifications of the clients. workflow scheduling optimization is an area of research activities in infrastructure as a service (IaaS) of the cloud. This problem is NP-complete. Thus, building a workflow scheduler that is optimum, having a reasonable level of performance and speed of computation, can be quite challenging in a distributed cloud environment. Metaheuristic algorithms may be improved in terms of their solution and its quality and speed of convergence utilizing combining it with other metaheuristic algorithms or any other algorithms that are metaheuristic based on local search. Shuffled frog leaping algorithm (SFLA) was acknowledged a metaheuristic performing heuristic search with a heuristic function (mathematical function) seeking solutions to combinatorial optimization problems. An optimization ratio on makespan %, resource utilization and computational cost performs better for SFLA-RSO with clustering when the number of tasks are increased.

关键词： Cloud computing workflow scheduling NP-complete problem Metaheuristic algorithm Shuffled frog leaping algorithm (SFLA)

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Energy-Efficient workflow scheduling Using Container-Based Virtualization in Software-Defined Data Centers

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第12期16卷 7646-7657页

作者： Ranjan, Rohit Thakur, Ishan Singh Aujla, Gagangeet Singh Kumar, Neeraj Zomaya, Albert Y. Innovaccer Comp & Network Secur Div Noida 110001 India Thapar Inst Engn & Technol Comp Sci & Engn Dept Patiala 147004 Punjab India Newcastle Univ Sch Comp Newcastle Upon Tyne NE4 5TG Tyne & Wear England Chandigarh Univ Comp Sci & Engn Dept Chandigarh 140413 India King Abdulaziz Univ Jeddah 21589 Saudi Arabia Asia Univ Dept Comp Sci & Informat Engn Taichung 41354 Taiwan Univ Sydney Sch Informat Technol J12 Sydney NSW 2006 Australia

workflow scheduling is one of the most difficult tasks due to the variation in the traffic flows generated from diverse cloud applications. Hence, in this article, a container-based virtualization is used to design an energy-efficient workflow scheduling in software-defined data centers. The containers provide the flexibility to the applications to access the underlying resource as per their requirements. Moreover, a runtime scheduler is responsible to handle all the scheduling decisions in the proposed workflow scheduling scheme. Even more, a doubly linked list-based access mechanism is used to provide access to the servers and virtual machines by traversing both ways. Finally, a hashing scheme is used to select an ideal location for the allocation of the containers. The proposed scheme is evaluated with respect to different performance metrics (makespan, execution time, fault tolerance, energy consumption, etc.) on the real data traces. The results obtained depict the superiority of the proposed scheme in comparison to the other existing schemes of its category.

关键词： Containers Task analysis Servers Cloud computing Virtualization Job shop scheduling Data centers Cloud computing containers software-defined data centers (SDDCs) virtual machine workflow scheduling

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Reliability and energy efficient workflow scheduling in cloud environment

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4期22卷 1283-1297页

作者： Garg, Ritu Mittal, Mamta Le Hoang Son Natl Inst Technol Comp Engn Dept Kurukshetra Haryana India GB Pant Engn Coll Dept Comp Sci & Engn New Delhi India Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Vietnam Natl Univ VNU Informat Technol Inst 144 Xuan Thuy Hanoi Vietnam

Cloud data centers consume huge amounts of electrical energy which results in an increased operational cost, decreased system reliability and carbon dioxide footprints. Thus, it is highly important to develop scheduling strategy to reduce energy consumption. Dynamic voltage and frequency scaling (DVFS) has been recognized as an efficient technique for reducing energy consumption. However, there is negative impact of DVFS on the reliability of system as it increases the transient faults during the application execution. Hence, it is essential to address the issue of reliability for mission critical applications. Recent studies on workflow scheduling in distributed environment have not considered reliability while minimizing the energy consumption. In this paper, we propose a new scheduling algorithm called the reliability and energy efficient workflow scheduling algorithm which jointly optimizes lifetime reliability of application and energy consumption and guarantees the user specified QoS constraint. The proposed algorithm works in four phases: priority calculation, clustering of tasks, distribution of target time and assigning the cluster to processing element with appropriate voltage/frequency levels. The simulation results obtained by using randomly generated task graphs and Gaussian Elimination task graphs shows that the proposed approach is effective in joint optimization of lifetime reliability of system and energy consumption compared to existing algorithms.

关键词： workflow scheduling Cloud environments Reliability Energy consumption

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Different aspects of workflow scheduling in large-scale distributed systems

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SIMULATION MODELLING PRACTICE AND THEORY 2017年 70卷 120-134页

作者： Stavrinides, Georgios L. Rodrigo Duro, Francisco Karatza, Helen D. Garcia Blas, Javier Carretero, Jesus Aristotle Univ Thessaloniki Dept Informat Thessaloniki 54124 Greece Univ Carlos III Madrid Comp Architecture & Commun Area Avda Univ 30 Madrid 28911 Spain

As large-scale distributed systems gain momentum, the scheduling of workflow applications with multiple requirements in such computing platforms has become a crucial area of research. In this paper, we investigate the workflow scheduling problem in large-scale distributed systems, from the Quality of Service (QoS) and data locality perspectives. We present a scheduling approach, considering two models of synchronization for the tasks in a workflow application: (a) communication through the network and (b) communication through temporary files. Specifically, we investigate via simulation the performance of a heterogeneous distributed system, where multiple soft real-time workflow applications arrive dynamically. The applications are scheduled under various tardiness bounds, taking into account the communication cost in the first case study and the I/O cost and data locality in the second. The simulation results provide useful insights into the impact of tardiness, bound and data locality on the system performance. (C) 2016 Elsevier B.V. All rights reserved.

关键词： workflow scheduling Large-scale distributed systems Ultrascale systems Quality of Service Data locality

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The Application of Hybrid Krill Herd Artificial Hummingbird Algorithm for Scientific workflow scheduling in Fog Computing

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Journal of Bionic Engineering 2023年第5期20卷 2443-2464页

作者： Aveen Othman Abdalrahman Daniel Pilevarzadeh Shafi Ghafouri Ali Ghaffari Department of Civil Engineering Garmian UniversityBardasureKalarIraq Department of Computer Engineering Tabriz BranchIslamic Azad UniversityTabriz***Iran Computer Engineering Department Faculty of Engineering and Natural SciencesIstinye UniversityIstanbulTurkey

Fog Computing(FC)provides processing and storage resources at the edge of the Internet of Things(IoT).By doing so,FC can help reduce latency and improve reliability of IoT *** energy consumption of servers and computing resources is one of the factors that directly affect conservation costs in fog *** consumption can be reduced by efficacious scheduling methods so that tasks are offloaded on the best possible *** deal with this problem,a binary model based on the combination of the Krill Herd Algorithm(KHA)and the Artificial Hummingbird Algorithm(AHA)is introduced as Binary KHA-AHA(BAHA-KHA).KHA is used to improve ***,the BAHA-KHA local optimal problem for task scheduling in FC environments is solved using the dynamic voltage and frequency scaling(DVFS)*** Heterogeneous Earliest Finish Time(HEFT)method is used to discover the order of task flow *** goal of the BAHA-KHA model is to minimize the number of resources,the communication between dependent tasks,and reduce energy *** this paper,the FC environment is considered to address the workflow scheduling issue to reduce energy consumption and minimize makespan on fog *** results were tested on five different workflows(Montage,CyberShake,LIGO,SIPHT,and Epigenomics).The evaluations show that the BAHA-KHA model has the best performance in comparison with the AHA,KHA,PSO and GA *** BAHA-KHA model has reduced the makespan rate by about 18%and the energy consumption by about 24%in comparison with *** is a preview of subscription content,log in via an institution to check access.

关键词： workflow scheduling Fog Computing Internet of Things Hummingbird Algorithm Krill Algorithm

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workflow scheduling in Cloud Computing Environment Using Cat Swarm Optimization

Workflow Scheduling in Cloud Computing Environment Using Cat...

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4th IEEE International Advance Computing Conference (IACC)

作者： Bilgaiyan, Saurabh Sagnika, Santwana Das, Madhabananda Sch Comp Engn Bhubaneswar Odisha India

ISBN: (纸本)9781479925711

Cloud computing is new era of network based computing, where resources are distributed over the network and shared among its users. Any user can use these resources through internet on the basis of Pay-As-Per-Use system. A service used by any user can produce a very large amount of data. So in this case, the data transfer cost between two dependent resources will be very high. In addition, a complex application can have a large number of tasks which may cause an increase in total cost of execution of that application, if not scheduled in an optimized way. So to overcome these problems, the authors present a Cat Swarm Optimization (CSO) - based heuristic scheduling algorithm to schedule the tasks of an application onto available resources. The CSO heuristic algorithm considers both data transmission cost between two dependent resources and execution cost of tasks on different resources. The authors experiment with the proposed CSO algorithm using a hypothetical workflow and compare the workflow scheduling results with the existing Particle Swarm Optimization (PSO) algorithm. The experimental results show -(1) CSO gives an optimal task-to-resource (TOR) scheduling scheme that minimizes the total cost, (2) CSO shows an improvement over existing PSO in terms of number of iterations, and (3) CSO ensures fair load distribution on the available resources.

关键词： cat swarm optimization (CSO) cloud computing cost minimization particle swarm optimization (PSO) workflow scheduling

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Adaptive multiple-workflow scheduling with task rearrangement

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JOURNAL OF SUPERCOMPUTING 2015年第4期71卷 1297-1317页

作者： Chen, Wei Lee, Young Choon Fekete, Alan Zomaya, Albert Y. Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia

Large-scale distributed computing systems like grids and more recently clouds are a platform of choice for many resource-intensive applications. workflow applications account for the majority of these applications, particularly in science and engineering. A workflow application consists of multiple precedence-constrained tasks with data dependencies. Since resources in those systems are shared by many users and applications deployed there are very diverse, scheduling is complicated. Often, the actual execution of applications differs from the original schedule following delays such as those caused by resource contention and other issues in performance prediction. These delays have further impact when running multiple workflow applications due to inter-task dependencies. In this paper, we investigate the problem of scheduling multiple workflow applications concurrently, explicitly taking into account scheduling robustness. We present a dynamic task rearrangement and rescheduling algorithm that exploits the scheduling flexibility from precedence constraints among tasks. The algorithm optimizes resource allocation among multiple workflows, and it often stops the influence of delayed execution passing to subsequent tasks. The experimental results demonstrate that our approach can significantly improve performance in multiple-workflow scheduling.

关键词： scheduling workflow applications workflow scheduling Rescheduling

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An intelligent water drops-based workflow scheduling for IaaS cloud

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APPLIED SOFT COMPUTING 2019年 77卷 547-566页

作者： Adhikari, Mainak Amgoth, Tarachand Indian Sch Mines Indian Inst Technol Dept Comp Sci & Engn Dhanbad Bihar India

Cloud computing is an emerging technology in a distributed environment with a collection of large-scale heterogeneous systems. One of the challenging issues in the cloud data center is to select the minimum number of virtual machine (VM) instances to execute the tasks of a workflow within a time limit. The objectives of such a strategy are to minimize the total execution time of a workflow and improve resource utilization. However, the existing algorithms do not guarantee to achieve high resource utilization although they have abilities to achieve high execution efficiency. The higher resource utilization depends on the reusability of VM instances. In this work, we propose a new intelligent water drops based workflow scheduling algorithm for Infrastructure-as-a-Service (IaaS) cloud. The objectives of the proposed algorithm are to achieve higher resource utilization and minimize the makespan within the given deadline and budget constraints. The first contribution of the algorithm is to find multiple partial critical paths (PCPs) of a workflow which helps in finding suitable VM instances. The second contribution is a scheduling strategy for PCP-VM assignment for assigning the VM instances. The proposed algorithm is evaluated through various simulation runs using synthetic datasets and various performance metrics. Through comparison, we show the superior performance of the proposed algorithm over the existing ones. (C) 2019 Elsevier B.V. All rights reserved.

关键词： IaaS Cloud workflow scheduling Intelligent water drops Partial critical paths Makespan Deadline

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