检索结果-内蒙古大学图书馆

Deadline and budget-constrained archimedes optimization algorithm for workflow scheduling in cloud

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第2期28卷 1-27页

作者： Kushwaha, Shweta Singh, Ravi Shankar BHU Dept Comp Sci & Engn Indian Inst Technol Varanasi 221005 Uttar Pradesh India

Cloud computing has revolutionized various domains over the past decade, providing accessible computational and storage resources at reduced costs. The exponential growth in data volumes and processing complexity, particularly due to the proliferation of IoT devices and applications across fields such as business, education, and agriculture, requires scalable computing resources and efficient processing. workflow scheduling in cloud computing, an NP-hard optimization problem, involves allocating resources to tasks within a workflow and determining their execution sequence. Despite numerous heuristic, metaheuristic, and hybrid approaches, there remains a need for scheduling algorithms with lesser computational complexity to optimize makespan and cost efficiency, as well as ensure SLA compliance. This paper introduces a novel multi-objective metaheuristic solution, the Deadline and Budget constrained Archimedes Optimization Algorithm (ADB), which addresses workflow scheduling by optimizing makespan and cost while adhering to deadline and budget constraints. Extensive experiments on a well-known cloud simulation tool, workflowsim, using scientific workflows demonstrate significant improvements in makespan (20%), cost (5%), resource utilization (15%), and energy consumption (9%). Performance observations on Pareto optimality metrics show that our approach has a higher hypervolume for 80% cases, it dominates state of the art by at least 83%, and the s-metric value of our approach is lower for 95% cases, alongside statistical validation using t-tests and ANOVA, confirming the efficacy of our method compared to state-of-the-art approaches.

关键词： Optimization techniques workflow scheduling Metaheuristics Pareto-optimality Deadline Budget Cost-efficiency Archimedes optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Deadline-constrained security-aware workflow scheduling in hybrid cloud architecture

引用

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 162卷

作者： Abdi, Somayeh Ashjaei, Mohammad Mubeen, Saad Malardalen Univ Dept Networked & Embedded Syst Vasteras Sweden Islamic Azad Univ Dept Comp Engn Eslam Abad E Gharb Branch Eslam Abad E Gharb Iran

A hybrid cloud is an efficient solution to deal with the problem of insufficient resources of a private cloud when computing demands increase beyond its resource capacities. Cost-efficient workflow scheduling, considering security requirements and data dependency among tasks, is a prominent issue in the hybrid cloud. To address this problem, we propose a mathematical model that minimizes the monetary cost of executing a workflow and satisfies the security requirements of tasks under a deadline. The proposed model fulfills data dependency among tasks, and data transmission time is formulated with exact mathematical expressions. The derived model is a Mixed-integer linear programming problem. We evaluate the proposed model with real-world workflows over changes in the input variables of the model, such as the deadline and security requirements. This paper also presents a post-optimality analysis that investigates the stability of the assignment problem. The experimental results show that the proposed model minimizes the cost by decreasing inter-cloud communications for dependent tasks. However, the optimal solutions are affected by the limitations that are imposed by the problem constraints.

关键词： Hybrid cloud workflow scheduling Mixed integer linear programming Cost minimization Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

A Budget-Constrained workflow scheduling Approach With Priority Adjustment and Critical Task Optimizing in Clouds

引用

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 6907-6921页

作者： Fan, Mingjie Zhao, Xinchao Zuo, Xingquan Ye, Lingjuan Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China Minist Educ Key Lab Math & Informat Networks Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China Minist Educ Key Lab Trustworthy Distributed Comp & Serv Beijing 100876 Peoples R China

In the rapidly evolving landscape of cloud computing, scheduling complex scientific workflows poses significant challenges, particularly with constraints like budget considerations. While recent years have seen considerable research focus on budget-constrained cloud workflow scheduling, existing studies primarily concentrate on constructing solutions without delving into potential enhancements. To address this gap, our paper introduces PACP-HEFT, a modified HEFT algorithm integrating priority adjustment and task optimization to minimize makespan. Leveraging the Heterogeneous Earliest-Finish-Time (HEFT) algorithm as its foundation, PACP-HEFT incorporates two priority adjusters informed by task characteristics gleaned from task dependency topology and real-time scheduling data. Additionally, a critical task optimizer conducts thorough analyses and optimizations to reduce overall makespan. Extensive experimentation with real-world workflows underscores PACP-HEFT's superior performance compared to contemporary algorithms.

关键词： scheduling Cloud computing Costs Heuristic algorithms scheduling algorithms Feature extraction Resource management workflow scheduling budget constraint priority adjustment critical task optimizing

来源：评论

学校读者我要写书评

暂无评论

Scientific workflow scheduling algorithms in cloud environments: a comprehensive taxonomy, survey, and future directions

引用

JOURNAL OF scheduling 2025年第1期28卷 1-63页

作者： Saeedizade, Ehsan Ashtiani, Mehrdad Iran Univ Sci & Technol Sch Comp Engn Hengam StResalat Sq Tehran *** Iran Univ Nevada Dept Comp Sci & Engn Reno NV 89557 USA

Scientific workflows are large applications that consist of smaller computational units called tasks that have data dependency on each other. The tasks of a workflow can be scheduled and executed on distributed resources in a parallel manner. Cloud computing offers distributed, scalable, virtualized, cost-effective computing environments making them ideal platforms to execute scientific workflows. Cloud services provide their users with a vision of an unlimited amount of computing resources. However, considering different types of resources and QoS requirements, the problem of workflow scheduling lies in the NP-complete class. Thus, numerous types of research have been conducted in this area during the past years. In this paper, we aim to provide a comprehensive study of the workflow scheduling problem, existing solutions, and available tools that can be used by researchers in this domain. First, we present a taxonomy on scheduling algorithms and examine the existing works from different perspectives from application and resource models to algorithms' objectives and their nature. We also have presented a taxonomy of evaluation data sets as well as simulation tools and their architecture since the evaluation of an algorithm is important and must be performed accurately. Next, we survey some of the most recent works in the context of the proposed taxonomy with a focus on emerging cloud services like serverless computing or workflow as a service platform and state-of-the-art scheduling approaches. Moreover, we discuss some of the existing gaps in the literature and identify possible research directions that can be seen as potential contributions to future developments.

关键词： Scientific workflow workflow scheduling Cloud computing Serverless computing workflow as a service

来源：评论

学校读者我要写书评

暂无评论

Efficient workflow scheduling in Cloud Computing Using Hybrid Algorithm

引用

IETE JOURNAL OF RESEARCH 2025年第3期71卷 817-828页

作者： Tewari, Aakanksha Goyal, Namisha Awasthi, Lalit Kumar Priyanka Natl Inst Technol Comp Sci & Engn Hamirpur India Natl Inst Technol Hamirpur India Natl Inst Technol Dept Comp Sci & Engn Hamirpur India

In recent years, cloud computing has increasingly embraced a pay-per-use model, offering dynamic, virtualized resources via the internet. The central challenge in this domain is efficiently scheduling workflows, considering deadlines and budgets while optimizing task allocation to virtual machines (VMs). Our study hypothesizes that improved scheduling can reduce energy consumption, streamline process execution, and lower operational costs. To test this hypothesis, we conducted a comparative analysis of two optimization techniques: Genetic Algorithm with Multiple Particle Swarm Optimization (GA + MPSO) and Genetic Algorithm with Bat Algorithm (GA + BAT). The analysis reveals that the combination of Genetic Algorithm and Bat Algorithm (GA + BAT) excels in optimizing cloud computing workflow scheduling. GA + BAT demonstrated superior performance by significantly reducing energy consumption, shortening process execution times, and decreasing operational costs. These findings validate our hypothesis, underscoring that optimizing cloud computing workflow scheduling can deliver substantial benefits. Consequently, by adopting GA + BAT, cloud service providers can enhance efficiency, reduce costs, and foster a more sustainable and responsive cloud infrastructure.

关键词： Bat Algorithm Cloud computing Genetic Algorithm Optimization techniques Pay-per-use model workflow scheduling

来源：评论

学校读者我要写书评

暂无评论

Microservice workflow scheduling with a Resource Configuration Model Under Deadline and Reliability Constraints

引用

SENSORS 2025年第4期25卷 1253-1253页

作者： Li, Wenzheng Li, Xiaoping Chen, Long Wang, Mingjing Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China Wenzhou Univ Technol Sch Data Sci & Artificial Intelligence Wenzhou 325000 Peoples R China

With the continuous evolution of microservice architecture and containerization technology, the challenge of efficiently and reliably scheduling large-scale cloud services has become increasingly prominent. In this paper, we present a cost-optimized scheduling approach with resource configuration for microservice workflows in container environments, taking into account deadline and reliability constraints. We introduce a graph deep learning model (DeepMCC) that automatically configures containers to meet various service quality (QoS) requirements. Additionally, we propose a reliability microservice workflow scheduling algorithm (RMWS), which incorporates heuristic leasing and deployment strategies to ensure reliability while reducing cloud resource leasing cost. Experiments on four scientific workflow datasets show that the proposed approach achieves an average cost reduction of 44.59% compared to existing reliability scheduling algorithms, with improvements of 26.63% in the worst case and 73.72% in the best case.

关键词： workflow scheduling microservice workflow container environment cost-optimized scheduling

来源：评论

学校读者我要写书评

暂无评论

Real-time workflow scheduling in hybrid clouds with privacy and security constraints: A deep reinforcement learning approach

引用

EXPERT SYSTEMS WITH APPLICATIONS 2025年 278卷

作者： He, Haoyang Gu, Yan Hu, Yang Fang, Fang Ning, Xin Chen, Xiaomin Cheng, Long North China Elect Power Univ Sch Control & Comp Engn State Key Lab Alternate Elect Power Syst Renewable Beijing Peoples R China Chinese Acad Sci Inst Semicond Beijing Peoples R China Univ Reading Dept Comp Sci Reading England

Hybrid clouds offer more capacity and flexibility than public or private clouds alone, making them popular in business settings. However, they also complicate the scheduling of workflow applications due to their complex computing resources. Furthermore, significant concerns about exposing sensitive data on public clouds and securing data transmission add additional challenges to the scheduling process. Although various workflow scheduling strategies for hybrid clouds that address privacy and security concerns have been proposed, they have inherent limitations. Many of these methods, including heuristic and metaheuristic approaches, are tailored for batch processing and are not suitable for real-time scenarios where workflows can arrive unpredictably. Additionally, they often prioritize security over reducing execution time and costs, compromising overall efficiency. To tackle these challenges, we introduce a scheduling system in Hybrid clOuds that considers Privacy and Security constraints, called HOPS. HOPS utilizes Deep Reinforcement Learning to dynamically assign workflows to virtual machines in real-time, and aims to minimize both makespan and operational costs while adhering to privacy and security standards. We provide a comprehensive overview of the design of HOPS, and our experimental results clearly show its advantages over existing methods in terms of makespan, cost efficiency, and the success rate, with privacy and security compliance.

关键词： Hybrid cloud workflow scheduling Deep reinforcement learning Privacy preserving Data security

来源：评论

学校读者我要写书评

暂无评论

Real Relative Encoding Genetic Algorithm for workflow scheduling in Heterogeneous Distributed Computing Systems

引用

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2025年第1期36卷 1-14页

作者： Jiang, Junqiang Sun, Zhifang Lu, Ruiqi Pan, Li Peng, Zebo Hunan Inst Sci & Technol Sch Informat Sci & Engn Yueyang 414006 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Key Lab Embedded & Cyber Phys Syst Hunan Prov Changsha 410082 Peoples R China Linkoping Univ Dept Comp & Informat Sci IDA Embedded Syst Lab ESLAB SE-58183 Linkoping Sweden

This paper introduces a novel Real Relative encoding Genetic Algorithm (R(2)GA) to tackle the workflow scheduling problem in heterogeneous distributed computing systems (HDCS). R(2)GA employs a unique encoding mechanism, using real numbers to represent the relative positions of tasks in the schedulable task set. Decoding is performed by interpreting these real numbers in relation to the directed acyclic graph (DAG) of the workflow. This approach ensures that any sequence of randomly generated real numbers, produced by cross-over and mutation operations, can always be decoded into a valid solution, as the precedence constraints between tasks are explicitly defined by the DAG. The proposed encoding and decoding mechanism simplifies genetic operations and facilitates efficient exploration of the solution space. This inherent flexibility also allows R(2)GA to be easily adapted to various optimization scenarios in workflow scheduling within HDCS. Additionally, R(2)GA overcomes several issues associated with traditional genetic algorithms (GAs) and existing real-number encoding GAs, such as the generation of chromosomes that violate task precedence constraints and the strict limitations on gene value ranges. Experimental results show that R(2)GA consistently delivers superior performance in terms of solution quality and efficiency compared to existing techniques.

关键词： Genetic algorithms Encoding scheduling Processor scheduling Biological cells Metaheuristics Quality of service Heuristic algorithms Genetic operators Distributed computing Candidate task set directed acyclic graph (DAG) genetic algorithm real encoding workflow scheduling

来源：评论

学校读者我要写书评

暂无评论

An optimal workflow scheduling in IoT-fog-cloud system for minimizing time and energy

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-31页

作者： Rateb, Roqia Hadi, Ahmed Adnan Tamanampudi, Venkata Mohit Abualigah, Laith Ezugwu, Absalom E. Alzahrani, Ahmed Ibrahim Alblehai, Fahad Jia, Heming Al Ahliyya Amman Univ Coll Informat Technol Dept Comp Sci Amman Jordan Al Mustaqbal Univ Coll Sci Artificial Intelligence Sci Dept Babil 51001 Iraq JPMorgan Chase New York NY USA Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan North West Univ Unit Data Sci & Comp 11 Hofman St ZA-2520 Potchefstroom South Africa King Saud Univ Community Coll Comp Sci Dept Riyadh 11437 Saudi Arabia Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India

Today, with the increasing use of the Internet of Things (IoT) in the world, various workflows that need to be stored and processed on the computing platforms. But this issue, causes an increase in costs for computing resources providers, and as a result, system Energy Consumption (EC) is also reduced. Therefore, this paper examines the workflow scheduling problem of IoT devices in the fog-cloud environment, where reducing the EC of the computing system and reducing the MakeSpan Time (MST) of workflows as main objectives, under the constraints of priority, deadline and reliability. Therefore, in order to achieve these objectives, the combination of Aquila and Salp Swarm Algorithms (ASSA) is used to select the best Virtual Machines (VMs) for the execution of workflows. So, in each iteration of ASSA execution, a number of VMs are selected by the ASSA. Then by using the Reducing MakeSpan Time (RMST) technique, the MST of the workflow on selected VMs is reduced, while maintaining reliability and deadline. Then, using VM merging and Dynamic Voltage Frequency Scaling (DVFS) technique on the output from RMST, the static and dynamic EC is reduced, respectively. Experimental results show the effectiveness of the proposed method compared to previous methods.

关键词： Fog-cloud computing workflow scheduling Dynamic Voltage Frequency Scaling Aquila-Salp Swarm Algorithm

来源：评论

学校读者我要写书评

暂无评论

An intelligent secure and efficient workflow scheduling (SEWS) model for heterogeneous cloud computing environment

引用

KNOWLEDGE AND INFORMATION SYSTEMS 2025年 1-47页

作者： Pasha, Fairoz Natarajan, Jayapandian Christ Univ Sch Engn & Technol Dept Comp Sci & Engn Kengeri Campus Bangalore 560074 Karnataka India

This study recognizes the critical role of the cloud computing platform in scientific workflow applications yet identifies vulnerabilities in existing cloud workflow systems, such as information leaks, unauthorized access, and compromised data integrity during task scheduling. Mainly, attackers exploit the lack of security for intermediate-level task information. To address these security threats, this work introduces the secure and efficient workflow scheduling (SEWS) model for heterogeneous cloud computing environments. The SEWS model identifies malicious attacks on all workflow tasks and focuses explicitly on safeguarding intermediary data. The SEWS model employs intelligent techniques to enhance security and introduces a comprehensive metric to measure the security of workflow tasks, considering factors like integrity, confidentiality, and availability. Beyond security improvements, the SEWS model aims to elevate the overall quality of service (QoS) in workflow scheduling applications. This includes reducing simulation time, enhancing overall power efficiency, and minimizing average energy consumption. Results: Results from the SEWS model demonstrate substantial improvements over the energy-minimized scheduling (EMS) model, with a reduction of 79.41% in average simulation time, 87.92% in average power sum, 41.35% in average power average, and 89.62% in average energy consumption. These findings underscore the SEWS model's effectiveness in providing enhanced security and improved QoS in cloud workflow scheduling. The overarching goal of this work is to contribute to developing a more secure and efficient cloud workflow scheduling system, aligning with the increasing demands for robust security measures and optimized performance in heterogeneous cloud environments. Findings: Compared to the energy-minimized scheduling (EMS) model, the findings of this study demonstrate that the secure and efficient workflow scheduling (SEWS) model yields superior outcomes across k

关键词： Cloud computing Data security Montage Virtual machine workflow scheduling

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：