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Energy-efficient real-time multi-workflow scheduling in container-based cloud

JOURNAL OF COMBINATORIAL OPTIMIZATION 2025年第2期49卷 1-21页

作者： Sun, Zaixing Huang, Hejiao Li, Zhikai Gu, Chonglin Harbin Inst Technol Sch Comp Sci & Technol Shenzhen Peoples R China

Cloud computing has a powerful capability to handle a large number of tasks. However, this capability comes with significant energy requirements. It is critical to overcome the challenge of minimizing energy consumption within cloud service platforms without compromising service quality. In this paper, we propose a heuristic energy-saving scheduling algorithm, called Real-time multi-workflow Energy-efficient scheduling (RMES), which aims to minimize the total energy consumption in a container-based cloud. RMES schedules tasks in the most parallelized way to improve the resource utilization of the running machines in the cluster, thus reducing the time of the global process and saving energy. This paper also considers the affinity constraints between containers and machines, and RMES has the ability to satisfy the resource quantity and performance requirements of containers during the scheduling process. We introduce a re-scheduling mechanism that automatically adjusts the scheduling decisions of remaining tasks to account for the dynamic system states over time. The results show that RMES outperforms other scheduling algorithms in energy consumption and success rate. In the higher arrival rate scenario, the proposed algorithm saves energy consumption by more than 19.42%. The RMES approach can enhance the reliability and efficiency of scheduling systems.

关键词： multi-workflow scheduling Real time Container cloud Energy minimization

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A multi-workflow scheduling Approach With Explicit Evolutionary multi-Objective multi-Task Optimization Algorithm in Cloud Environment

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2025年第1期37卷

作者： Zhang, Qiqi Li, Bohui Geng, Shaojin Cai, Xingjuan Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

workflow tasks in the cloud environment are the abstraction and decomposition of large-scale and complex tasks in real-world scenarios, so cloud workflow scheduling problems have important research significance. However, most of the existing cloud workflow scheduling schemes are aimed at a single workflow, and do not make reasonable use of the commonality or complementary knowledge between similar tasks. Moreover, most cloud workflow scheduling models mainly focus on a few objectives such as time or cost, which is not comprehensive enough. Therefore, this paper first proposes a multi-objective cloud workflow scheduling model, which solves the maximum completion time, execution cost and energy consumption as three objectives during task execution. Secondly, to efficiently handle multiple similar cloud workflow scheduling tasks at the same time, this paper treats various cloud workflow scheduling issues as distinct tasks, establishes a multi-task cloud workflow scheduling framework that aims for the same goal while accommodating workflows of differing scales, and a multi-objective evolutionary multi-task optimization algorithm based on elite selection (MOEMT-ES) is designed to solve the above scheduling model. Finally, through algorithm comparison experiments on the CEC2017 evolutionary multi-task optimization competition benchmark problem and multi-workflow test problem, MOEMT-ES shows superior competitiveness.

关键词： adaptive transfer strategy explicit evolutionary multi-objective multi-task optimization algorithm multi-workflow scheduling transfer solution evaluation strategy

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multi-workflow scheduling Based on Genetic Algorithm 4

Multi-Workflow Scheduling Based on Genetic Algorithm

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4th IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Deng, Fuhu Lai, Miao Geng, Ji Univ Elect Sci & Technol China Coll Informat & Software Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9781728114101

Most of the existing multi-workflow scheduling algorithms combine multi-workflow into one workflow, and then use a single workflow scheduling method to schedule the synthetic workflow. This approach of merging multi-workflow into one workflow requires the first workflow to wait for the workflow behind, which reduces the response time of workflow and user experience. To address this gap, this paper considers the arrival time of different workflows and two conflicting objectives, to proposes a multi-objective genetic algorithm for multi-workflow scheduling. This algorithm divides each of the arrival workflows into tasks, then uses the genetic algorithm to find the optimal mapping tasks to virtual machines based on task execution overhead and the virtual machine idle time slots. The algorithm makes full use of the virtual machine's resources and finds optimal trade-off solutions between the cost of tasks and the resource utilization. In the real-world scientific workflows experiment, the algorithm is superior to the exiting scheduling algorithm.

关键词： multi-workflow scheduling cloud computing genetic algorithm

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multi-workflow scheduling in Industrial Edge: A Genetic Algorithm with Heuristic Strategy 22

Multi-workflow Scheduling in Industrial Edge: A Genetic Algo...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Chen, Xiang-Ling Zhang, Zhi-Xuan Geng, Ming-Can Chen, Wei-Neng South China Univ Technol Guangzhou Peoples R China

ISBN: (纸本)9781450392686

On the industrial Internet, edge computing can effectively solve the problem of latency and data lending by sinking a portion of cloud computing to industrial park edges. However, as the edge servers are heterogeneous, how to allocate tasks in multiple workflows to the appropriate servers effectively to reduce execution time and energy consumption is a challenging problem. In this paper, we first model the multi-workflow scheduling for industrial edge computing as an optimization problem with the objectives to reduce execution time and energy consumption. Then we propose an efficient greedy algorithm to solve this problem by taking into account the competitive resource relationship between tasks. To further improve performance, we combine this heuristic with genetic algorithm (GA), resulting in a GA with heuristic strategy. In order to avoid coding redundancy, we propose a discrete coding scheme for GA. This genetic expression strategy reduces the search space and improves search efficiency. The experimental results validate the effectiveness of the proposed algorithm in finding promising solutions.

关键词： Industrial edge computing multi-workflow scheduling genetic algorithm (GA)

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Deadline-Constrained and Cost-Effective multi-workflow scheduling with Uncertainty in Cloud Control Systems

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Journal of Systems Science & Complexity 2024年第5期37卷 1861-1886页

作者： YE Lingjuan YANG Liwen XIA Yuanqing ZHAN Yufeng ZHAO Xinchao School of Science Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Mathematics and Information Networks Beijing University of Posts and TelecommunicationsMinistry of EducationBeijing 100876China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China School of Automation Beijing Institute of TechnologyBeijing 100081China

In cloud control systems,generating an efficient and economical workflow scheduling strategy for deadline-constrained workflow applications,especially in uncertain multi-workflow dynamic scheduling processes,is a crucial *** optimize the total cost of workflow scheduling,the authors propose a cost-driven heuristic scheduling algorithm F-MWSA which consists of two phases:Fuzzy deadline distribution and fuzzy task *** the fuzzy deadline distribution phase,a new workflow deadline distribution strategy with fuzziness is designed to obtain the sub-deadline constraint of each *** fuzzy task scheduling phase focuses on a cost-effective strategy to assign tasks to cloud resources,reducing multi-workflow scheduling *** evaluations on five real-world workflows demonstrate that the proposed F-MWSA outperforms the baseline policy in terms of total cost,success ratio,resource utilization,and makespan.

关键词： Cloud computing cost-effective deadline constraint multi-workflow scheduling uncertainty

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Electricity-cost-aware multi-workflow scheduling in heterogeneous cloud

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COMPUTING 2024年第6期106卷 1749-1775页

作者： Wang, Shuang Duan, Yibing Lei, Yamin Du, Peng Wang, Yamin Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Jiangsu Peoples R China China Mobile Res Inst Beijing 100053 Peoples R China

multi-workflows are commonly deployed on cloud platforms to achieve efficient computational power. Diverse task configuration requirements, the heterogeneous nature and dynamic electricity price of cloud servers impose significant challenges for economically scheduling multi-workflows. In this paper, we propose a Heuristic Electricity-cost-aware multi-workflow scheduling algorithm (HEMS) to search for an optimal scheduling plan which determines the optimal scheduling scheme for each task in each workflow, specifying the server to perform the task with determined resources in specific time. The objective is to minimize the total electricity cost of all servers while satisfying the deadline constraints of all workflows. The HEMS algorithm consists of five components: workflow scheduling Sequence Generation, Task scheduling Sequence Initialization for each workflow, Optimal scheduling Scheme Determination for each task, initial Task scheduling Sequence Optimization, and Optimal scheduling Plan Optimization. Experimental results demonstrate that HEMS consistently achieves the optimal scheduling plan with the lower total electricity cost (saving 54.5-69.1% on average) within slightly longer CPU time for various multi-workflows compared to existing three scheduling approaches.

关键词： multi-workflow scheduling Heuristic Electricity cost optimization Deadline Cloud computing

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Adaptive Cloud Bundle Provisioning and multi-workflow scheduling via Coalition Reinforcement Learning

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IEEE TRANSACTIONS ON COMPUTERS 2023年第4期72卷 1041-1054页

作者： Wang, Xiaogang Cao, Jian Buyya, Rajkumar Univ Melbourne Sch Comp & Informat Syst CLOUDS Lab Melbourne Vic 3010 Australia Shanghai Dianji Univ Sch Elect & Informat Shanghai 201306 Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

The efficient cloud resource provisioning for the execution of complex workflow applications has always been one of the important research issues. Most of the existing approaches focus on the resource provisioning of single-type virtual machine (VM) instances for the single or multiple workflows, while few consider the situation of provisioning multi-type VM instances simultaneously. As a result, the executing performance of complex workflows degrades. Different from the existing work, this paper proposes an adaptive cloud bundle provisioning and multi-workflow scheduling model to dynamically perform both the horizontal and vertical cloud resource scaling on multi-type VM instances for the execution of complex workflows. Among the model, a depth-first-search coalition reinforcement learning (DFSCRL) provisioning policy is presented to realize the resource scaling, which integrates the physical machine (PM) coalition formation with the Q-learning algorithm, then dynamically generates an optimal multi-type VM instance bundle from the PM coalition, and finally provisions these instances to the concurrent execution of multiple workflows. The theoretical proofs and various experiments with the multifaceted metrics demonstrate that the performance of the proposed algorithms is superior to that of the state-of-the-art relevant policies.

关键词： Task analysis Dynamic scheduling Cloud computing Heuristic algorithms Adaptation models scheduling algorithms Costs coalition formation depth-first-search multi -type VM instance bundle provisioning multi-workflow scheduling reinforcement learning

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Chaotic-Nondominated-Sorting Owl Search Algorithm for Energy-Aware multi-workflow scheduling in Hybrid Clouds

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2022年第3期7卷 595-608页

作者： Li, Huifang Xu, Guanghao Wang, Danjing Zhou, MengChu Yuan, Yan Alabdulwahab, Ahmed Beijing Inst Technol State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA King Abdulaziz Univ Ctr Res Excellence Renewable Energy & Power Syst Jeddah 21481 Saudi Arabia King Abdulaziz Univ Dept Elect & Comp Engn Fac Engn Jeddah 21481 Saudi Arabia King Abdulaziz Univ Ctr Res Excellence Renewable Energy & Power Syst Jeddah 21481 Saudi Arabia

Since a single private cloud cannot satisfy the increasing computational requirements for executing multiple workflows simultaneously, hybrid clouds are often adopted to perform such execution. multi-workflow scheduling is challenging as users may request various applications with different QoS requirements. This work proposes a Chaotic-nondominated-sorting Owl Search Algorithm (COSA) by combining an Owl Search Algorithm (OSA) with a Nondominated Sorting Genetic Algorithm II (NSGA-II) to schedule resource-constrained multiple workflows in hybrid clouds with makespan, cost and energy consumption minimized under the given deadline and budget constraints. First, a hierarchical evolving mechanism is designed to update the better half and worse half of population by NSGA-II and OSA, respectively to guarantee a good trade-off between exploration and exploitation. Second, a chaotic sequence is introduced to adaptively adjust OSA's step size during population evolution for better exploration. Third, we adopt a chaotic operator for searching around the resulting Non-Dominated Solutions (NDS) to improve COSA's local search ability. Experiments are conducted to compare COSA with four peers and the results show its superiority in the number of obtained NDS, diversity preservation and convergence towards the near optimal Pareto set. In particular, it can find at least 19% more NDS than its peers.

关键词： Cloud computing scheduling Processor scheduling Costs Task analysis Quality of service Particle swarm optimization multi-workflow scheduling Hybrid clouds Energy consumption Owl search algorithm Chaotic operator

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Cloud Computing: A multi-workflow scheduling Algorithm with Dynamic Reusability

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018年第2期43卷 645-660页

作者： Adhikari, Mainak Koley, Santanu Budge Budge Inst Technol Dept Comp Sci & Engn Kolkata India

Cloud computing provides a dynamic environment of well-organized deployment of hardware and software that are common in nature and the requirement for propping up heterogeneous workflow applications to realize high performance and improved throughput where the most demanding task is multiple workflow applications surrounded by their fixed deadline. These workflow applications consist of interconnected jobs and data. Nevertheless, hardly any initiations are tailored on multi-workflow scheduling exertion. These scheduling problems have been considered methodically in cloud atmosphere. Accessibility of the computing resources on the data center (DC) provides the exact time of execution of each process, whereas the execution time of every process within a workflow is pre-calculated in the majority of the existing multi-workflow scheduling problem. System overhead so far is an additional concern at the same time as dynamically generating virtual machines (VMs) with salvage them dipping the power eating. The aim of this paper is to reduce the execution time of every job and finalize the execution of all workflow within its deadline by producing VMs dynamically in DC and recycle them as necessary. We recommend a dynamic multi-workflow scheduling algorithm formally named as competent dynamic multi-workflow scheduling (CDMWS) algorithm. Simulation process describes one of the best algorithms so far in terms of performance among subsistent algorithm and moves toward a new era of multi-workflow relevance.

关键词： Heterogeneous workflow application Cloud computing multi-workflow scheduling Virtual machine Deadline Data center

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Dynamic multi-workflow scheduling Analysis of Real-time Healthcare Ecosystem: An Emerging Research Area 15

Dynamic Multi-Workflow Scheduling Analysis of Real-time Heal...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Rathi, Sugandha Nagpal, Renuka Mehrotra, Deepti Srivastava, Gautam Department of Computer Science and Engineering Amity University Noida India Department of Math and Computer Science Brandon University Canada

ISBN: (纸本)9798331300579

In today's high technology environment, most of the workflows are migrated to the cloud and related resourcing technologies like fog, mist and edge computing. Here, in this research work, we have given a detailed idea about dynamic multi-workflow scheduling (DMWS) in fog environment. The fog computing parameters that are affected are also mentioned. Various standard workflows are discussed mentioning their usage in various fields. DMWS has two major branches which are the most researched in past few years. One is multi-workflow scheduling and the other one is dynamic workflow scheduling. Both workflow scheduling have its own pros and cons. A detailed literature review of the papers published since 2019 on DMWS is discussed in detail. Also, a case study regarding DMWS in the healthcare field is conducted and discussed. A section is included that explains the various challenges and issues in DMWS. Lastly future scope and areas of implementation are discussed for DMWS. © Grenze Scientific Society, 2024.

关键词： Dynamic-workflow scheduling Fog computing multi-workflow scheduling Standard workflows workflow scheduling

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