The DOI matches its Crossref record; 47 of the 49 references it cites were found in a registry or an index.
Biobjective Task Scheduling for Distributed Green Data Centers
Abstract
The industry of data centers is the fifth largest energy consumer in the world. Distributed green data centers (DGDCs) consume 300 billion kWh per year to provide different types of heterogeneous services to global users. Users around the world bring revenue to DGDC providers according to actual quality of service (QoS) of their tasks. Their tasks are delivered to DGDCs through multiple Internet service providers (ISPs) with different bandwidth capacities and unit bandwidth price. In addition, prices of power grid, wind, and solar energy in different GDCs vary with their geographical locations. Therefore, it is highly challenging to schedule tasks among DGDCs in a high-profit and high-QoS way. This work designs a multiobjective optimization method for DGDCs to maximize the profit of DGDC providers and minimize the average task loss possibility of all applications by jointly determining the split of tasks among multiple ISPs and task service rates of each GDC. A problem is formulated and solved with a simulated-annealing-based biobjective differential evolution (SBDE) algorithm to obtain an approximate Pareto-optimal set. The method of minimum Manhattan distance is adopted to select a knee solution that specifies the Pareto-optimal task service rates and task split among ISPs for DGDCs in each time slot. Real-life data-based experiments demonstrate that the proposed method achieves lower task loss of all applications and larger profit than several existing scheduling algorithms. Note to Practitioners-This work aims to maximize the profit and minimize the task loss for DGDCs powered by renewable energy and smart grid by jointly determining the split of tasks among multiple ISPs. Existing task scheduling algorithms fail to jointly consider and optimize the profit of DGDC providers and QoS of tasks. Therefore, they fail to intelligently schedule tasks of heterogeneous applications and allocate infrastructure resources within their response time bounds. In this work, a new method that tackles drawbacks of existing algorithms is proposed. It is achieved by adopting the proposed SBDE algorithm that solves a multiobjective optimization problem. Simulation experiments demonstrate that compared with three typical task scheduling approaches, it increases profit and decreases task loss. It can be readily and easily integrated and implemented in real-life industrial DGDCs. The future work needs to investigate the real-time green energy prediction with historical data and further combine prediction and task scheduling together to achieve greener and even net-zero-energy data centers.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Zhang, Y., Gong, D., & Cheng, J. (2015) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2015.2476796 · Cited by 381
Jiang, S., & Yang, S. (2017) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2016.2592479 · Cited by 374
Multi-Objective Workflow Scheduling With Deep-Q-Network-Based Multi-Agent Reinforcement Learning
Wang, Y., Liu, H., Zheng, W., Xia, Y., Li, Y., Chen, P., Guo, K., & Xie, H. L. (2019) · IEEE Access · doi:10.1109/access.2019.2902846 · Cited by 297
Lyden, S., & Haque, M. E. (2015) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2015.2468592 · Cited by 252
Survey of Techniques and Architectures for Designing Energy-Efficient Data Centers
Shuja, J., Bilal, K., Madani, S. A., Othman, M., Ranjan, R. K., Balaji, P., & Khan, S. U. (2014) · IEEE Systems Journal · doi:10.1109/jsyst.2014.2315823 · Cited by 218
Baccarelli, E., Cordeschi, N., Mei, A., Panella, M., Shojafar, M., & Stefa, J. (2016) · IEEE Network · doi:10.1109/mnet.2016.7437025 · Cited by 200
Chiu, W.-Y., Yen, G. G., & Juan, T.-K. (2016) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2016.2564158 · Cited by 197
Population topologies for particle swarm optimization and differential evolution
Lynn, N., Ali, M. Z., & Suganthan, P. N. (2017) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2017.11.002 · Cited by 187
Robust Energy Management System for a Microgrid Based on a Fuzzy Prediction Interval Model
Valencia, F., Collado, J., Sáez, D. A., & Marín, L. G. (2015) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2015.2463079 · Cited by 175
Beloglazov, A., & Buyya, R. (2014) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.3314 · Cited by 172
Research works printed but not linked: 1
- juarez, 2018, Energy-aware scheduler for HPC parallel task base applications in cloud computing, Int J Combinat Optim Problems Inform, 9, 54
Cited by
Sorted by citations
Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774
“analysis, task scheduling, and virtual machine consolidation have also been studied to improve power efficiency in data centers [267], [268].”
BackgroundIterated Greedy Algorithms for Flow-Shop Scheduling Problems: A Tutorial
Zhao, Z., Zhou, M., & Liu, S. (2021) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2021.3062994 · Cited by 235
“…FSPs, designing hybrid algorithms that combine IGAs with existing multiobjective algorithms, such as multiobjective GAs [166]–[168], evolutionary algorithms [169]–[171], scatter search [172], [173], and DE [174], [175], for solving multiobjective FSPs is a potential research direction.”
BackgroundSelf-citationZhao, Z., Liu, S., Zhou, M., & Abusorrah, A. M. (2020) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2020.1003539 · Cited by 206
“A single machine scheduling problem with a time-dependent learning effect is studied in [18] to minimize Manuscript received September 12, 2020; revised October 23, 2020; accepted November 18, 2020.”
BackgroundSelf-citationLu, C., Gao, L., Yi, J., & Li, X. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3043734 · Cited by 194
Zhao, Z., Liu, S., Zhou, M., You, D., & Guo, X. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.3027532 · Cited by 190
Self-citationGuo, X., Zhang, Z., Qi, L., Liu, S., Tang, Y. G., & Zhao, Z. (2021) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2021.3133601 · Cited by 170
“directions: 1) we will develop more powerful intelligent optimization algorithms [51]–[61] and machine learning”
BackgroundHuang, J., Pan, Q.-K., & Gao, L. (2020) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2020.100742 · Cited by 139
An Effective Cloud Workflow Scheduling Approach Combining PSO and Idle Time Slot-Aware Rules
Wang, Y., & Zuo, X. (2021) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2021.1003982 · Cited by 135
Islam, M. T., Karunasekera, S. A., & Buyya, R. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3124670 · Cited by 133
“[25] proposed a biobjective task scheduling algorithm for distributed green data centers (DGDC).”
BackgroundZhang, Z., Zhao, M., Wang, H., Cui, Z., & Zhang, W. (2021) · Information Sciences · doi:10.1016/j.ins.2021.11.027 · Cited by 112
Fu, Y., Zhou, M., Guo, X., & Qi, L. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.123364 · Cited by 100
Self-citationRecent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm
Chen, S., Li, Q., Zhou, M., & Abusorrah, A. M. (2021) · Sensors · doi:10.3390/s21030779 · Cited by 97
“In intelligent manufacturing, a four-layer computing system supporting the operation of artificial intelligence tasks from the perspective of a network is proposed in [37].”
Self-citationLiu, W., Yan, Y., Sun, Y., Mao, H., Cheng, M., Wang, P., & Ding, Z. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120918 · Cited by 90
An Intelligent Optimization Method for Optimal Virtual Machine Allocation in Cloud Data Centers
Zhang, P., Zhou, M., & Wang, X. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.2975225 · Cited by 85
Self-citationTowards Metaheuristic Scheduling Techniques in Cloud and Fog: An Extensive Taxonomic Review
Singh, R. M., Awasthi, L. K., & Sikka, G. (2022) · ACM Computing Surveys · doi:10.1145/3494520 · Cited by 75
“In [44] the authors developed a bi-objective SA based Differential Evolution (DE) algorithm for maximizing profit in distributed green data centers.”
BackgroundAn evolutionary fuzzy scheduler for multi-objective resource allocation in fog computing
Wu, C., Li, W., Wang, L., & Zomaya, A. Y. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.12.019 · Cited by 68
Cui, M., Li, L., Zhou, M., Li, J., Abusorrah, A. M., & Sedraoui, K. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.105425 · Cited by 63
Self-citationMoving-Distance-Minimized PSO for Mobile Robot Swarm
Zhang, J., Lu, Y., Che, L., & Zhou, M. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3079346 · Cited by 56
Self-citationExamining the developments in scheduling algorithms research: A bibliometric approach
Omotehinwa, T. O. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e09510 · Cited by 59
“(Yuan et al., 2021) Be objective Task Scheduling for Distributed Green Data Centers Simulated-annealing-based Bioobjective Differential Evolution (SBDE) Algorithm IEEE Transactions on Automation Science and Engineering 31 Virtual Machine | Data Center | Cloud Computing”
MethodWang, J., Liu, C., & Zhou, M. (2020) · IEEE Systems Journal · doi:10.1109/jsyst.2019.2963222 · Cited by 55
Self-citationEnergy-Efficient Hybrid Framework for Green Cloud Computing
Alarifi, A., Dubey, K., Amoon, M., Altameem, T. A., El‐Samie, F. E. A., Altameem, A., Sharma, S. C., & Nasr, A. A. (2020) · IEEE Access · doi:10.1109/access.2020.3002184 · Cited by 55
“In [22], H. Yuan et al. have proposed an optimization method for minimizing the possibility of task loss and maximizing the profit for cloud data centers.”
MethodJoint Deployment and Request Routing for Microservice Call Graphs in Data Centers
Hu, Y., Wang, H., Wang, L., Hu, M., Peng, K., & Veeravalli, B. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3311767 · Cited by 52
RAMBO: Resource Allocation for Microservices Using Bayesian Optimization
Li, Q., Li, B., Mercati, P., Illikkal, R., Tai, C., Kishinevsky, M., & Kozyrakis, C. (2021) · IEEE Computer Architecture Letters · doi:10.1109/lca.2021.3066142 · Cited by 51
“Our framework can potentially support more services, resources (e.g., network bandwidth allocation), and algorithms [6], [7], [18]; we plan to explore them in future work.”
BackgroundGad, A. G., Houssein, E. H., Zhou, M., Suganthan, P. N., & Wazery, Y. M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3291367 · Cited by 47
“Third, while tightly enforcing deadline constraints, a typical CCP must execute energy-efficient and cost-time-effective scheduling of many IIoT heterogeneous tasks [9].”
BackgroundSelf-citationEnergy-Efficient and QoS-Optimized Adaptive Task Scheduling and Management in Clouds
Yuan, H., Bi, J., & Zhou, M. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.3042409 · Cited by 47
Self-citationZhao, Z., Jiang, Q., Liu, S., Zhou, M., Yang, X., & Guo, X. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108477 · Cited by 45
Self-citationMulti-Mobile-Robot Transport and Production Integrated System Optimization
Zhao, Z., Li, X., Liu, S., Zhou, M., & Yang, X. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3421889 · Cited by 44
“Furthermore, the high performance of a CP model revealed in this work motivates us to use constraint programming to solve other optimization problems [46], [47], [48], [49].”
Self-citationZeedan, M., Attiya, G., & El-Fishawy, N. A. (2022) · Computing · doi:10.1007/s00607-022-01116-y · Cited by 43
“In [28], a simulated-annealing-based bi-objective differential evolution (SBDE) algorithm is designed to obtain a pareto optimal set for distributed green data centers (DGDCs) tomaximize the profit of the providers andminimize the average task loss possibility.”
MethodLi, H., Xu, G., Wang, D., Zhou, M., Yan, Y., & Alabdulwahab, A. (2022) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2022.3144357 · Cited by 43
“One of the most active studies is to combine swarm intelligence algorithms and genetic process-based approaches, e.g., [25], [43].”
BackgroundSelf-citationA Deep Reinforcement Learning Approach to the Optimization of Data Center Task Scheduling
Che, H., Bai, Z., Zuo, R., & Li, H. (2020) · Complexity · doi:10.1155/2020/3046769 · Cited by 40
Xu, X., Li, J., Zhou, M., & Yu, X. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3070143 · Cited by 39
Self-citationAmmari, A. C., Labidi, W., Mnif, F., Yuan, H., Zhou, M., & Sarrab, M. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.052 · Cited by 38
Self-citationYuan, G., Yang, Y., Tian, G., & Fathollahi‐Fard, A. M. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-18883-y · Cited by 38
“Disassembly represents an environmentally friendly and economical way of obtaining parts or subassemblies from end-of-life (EOL) products with necessary sorting operations (Feng et al. 2019; Yuan et al. 2021).”
BackgroundRenewable Energy in Data Centers: The Dilemma of Electrical Grid Dependency and Autonomy Costs
Gnibga, W. E., Blavette, A., & Orgerie, A. (2023) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2023.3307790 · Cited by 37
“Yuan et al. [13] formulated a bi-objective optimization problem for distributed green data centers to maximize the profit of service providers and minimize the average task loss possibility of all applications by jointly determining the split of tasks and service rates among multiple data centers.”
BackgroundA Differential Evolution-Enhanced Position-Transitional Approach to Latent Factor Analysis
Chen, J., Wang, R., Wu, D., & Luo, X. (2022) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2022.3186673 · Cited by 35
Dynamic Population Structures-Based Differential Evolution Algorithm
Yang, J., Wang, K., Wang, Y., Wang, J., Lei, Z., & Gao, S. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3367809 · Cited by 35
“While some enhancement methods have shown promise in specific scenarios, they often introduce a trade-off dilemma across various facets of the problem [16], [17].”
BackgroundLearning-Based Genetic Algorithm to Schedule an Extended Flexible Job Shop
Cao, Z., Lin, C., Zhou, M., & Wen, X. (2024) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2024.3413054 · Cited by 32
“Subsequently, a differential evolution algorithm and the heuristic rule in [42] and [43] are employed to generate a group of superior individuals.”
MethodSelf-citationZhang, Z., Guo, X., Zhou, M., Liu, S., & Qi, L. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9283184 · Cited by 31
Self-citationCarbon Management of Multi-Datacenter Based On Spatio-Temporal Task Migration
Yang, T., Jiang, H., Hou, Y., & Geng, Y. (2021) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2021.3130644 · Cited by 30
“…between revenue and energy cost in [26], and design a multi-objective optimization method for profit maximization and task loss minimization in [27]; Xu et al. [28] propose a contracts-based resource sharing model for federated geo-distributed clouds achieving resource allocation efficiency and…”
MethodA systematic review of green-aware management techniques for sustainable data center
Lin, W., Lin, J., Peng, Z., Huang, H., Lin, W., & Li, K. (2024) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2024.100989 · Cited by 37
Cao, Z., Zhou, L., Lin, C., & Zhou, M. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2022.100414 · Cited by 29
Self-citationEnergy-aware scheduling for spark job based on deep reinforcement learning in cloud
Li, H., Lu, L., Shi, W., Tan, G., & Luo, H. (2023) · Computing · doi:10.1007/s00607-023-01171-z · Cited by 27
“Yuan [14] proposed a dual objective task scheduling algorithm in distributed green data center to maximize supplier profit and minimize the probability of average task loss of all workloads.”
BackgroundZade, B. M. H., Mansouri, N., & Javidi, M. M. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104372 · Cited by 27
An improved grey wolf optimizer for welding shop inverse scheduling
Wang, C., Zhao, L., Li, X., & Li, Y. (2021) · Computers & Industrial Engineering · doi:10.1016/j.cie.2021.107809 · Cited by 27
Li, Y., Li, X., & Gao, L. (2022) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2022.3187082 · Cited by 27
“The scheduling optimization problem exists in almost every field, such as business management, transportation, aerospace, health care, energy power, and network communication [2], [3].”
BackgroundXie, H., Ding, D., Zhao, L., Kang, K., & Liu, Q. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122009 · Cited by 27
Self-citationImproved Jaya Algorithm for Flexible Job Shop Rescheduling Problem
Gao, K., Yang, F., Li, J., Sang, H., & Luo, J. (2020) · IEEE Access · doi:10.1109/access.2020.2992478 · Cited by 26
“INTRODUCTION Production scheduling and task scheduling are extremely crucial in industrial manufacturing [43], [46] and data center [47], [48].”
BackgroundGreen scheduling to achieve green manufacturing: Pursuing a research agenda by mapping science
Álvarez-Meaza, I., Bilbao, E. Z., Belver, R. M. R., & Garechana, G. (2021) · Technology in Society · doi:10.1016/j.techsoc.2021.101758 · Cited by 26
CPU Scheduling in Data Centers Using Asynchronous Finite-Time Distributed Coordination Mechanisms
Grammenos, A., Charalambous, T., & Kalyvianaki, E. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3236214 · Cited by 28
“some centralized schedulers that tackle utilisation optimization focusing on energy efficiency [26], [27], [28].”
BackgroundRajashekar, K. J., Channakrishnaraju, C., Gowda, P. C., & Jayachandra, A. B. (2023) · Applied Sciences · doi:10.3390/app131910850 · Cited by 25
A Hybrid of Interactive Learning and Predictive Modeling for Occupancy Estimation in Smart Buildings
Guo, J., Amayri, M., Bouguila, N., & Fan, W. (2021) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2021.3131943 · Cited by 24
Ijaz, S., Ahmad, S. G., Ayyub, K., Munir, E. U., & Ramzan, N. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06550-7 · Cited by 28
“A simulated annealing based bi-objective differential evolution (SADE) al-gorithm is designed in [28].”
Bi, J., Zhai, J., Yuan, H., Wang, Z., Qiao, J., Zhang, J., & Zhou, M. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161299 · Cited by 22
“Thus, EAs have been widely applied in robotics and automation [3], [4], industrial scheduling [5], resource allocation [6], and other fields [7]-[9].”
BackgroundSelf-citationMonitoring file integrity using blockchain and smart contracts
Pinheiro, A., Canedo, E. D., De Sousa, R. T., & De Oliveira Albuquerque, R. (2020) · IEEE Access · doi:10.1109/access.2020.3035271 · Cited by 21
“…techniques and technologies to both solve problems and improve the results of CC solutions, including the following examples: i) Yuan et al. [4] propose an algorithm, based on machine learning techniques, to improve the distribution schedule of tasks from CC services between geographically…”
MethodInfluentialShukla, P. K., Pandey, S., Hatwar, P., & Pant, A. (2023) · Proceedings of the National Academy of Sciences India Section A Physical Sciences · doi:10.1007/s40010-023-00809-z · Cited by 20
“In [18], delay limitations are implemented in order to find viable solutions.”
BackgroundMethodBi, J., Wang, Z., Yuan, H., Qiao, J., Zhang, J., & Zhou, M. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160442 · Cited by 20
“For example, zero penalty means all the constraints are strictly met [37], [38].”
BackgroundSelf-citationWei, T., Guo, X., Zhou, M., Wang, J., Liu, S., Qin, S., & Tang, Y. G. (2025) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2025.3528629 · Cited by 19
“In a multiobjective optimization problem, direct comparisons are difficult due to the differences in the dimensions of the objective functions [49].”
Self-citationEnergy-Minimized Scheduling of Intermittent Real-Time Tasks in a CPU-GPU Cloud Computing Platform
Hu, B., Yang, X., & Zhao, M. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3288702 · Cited by 19
“For example, in [4], a multi-objective optimization method for DGDCs is presented to maximize the profit of DGDC providers and minimize the average task loss possibility of all applications by jointly determining the split of tasks among multiple Internet service providers and the task service rates of each green data…”
MethodA Hybrid Meta-Heuristic for a Bi-Objective Stochastic Optimization of Urban Water Supply System
Dogani, A., Dourandish, A., Ghorbani, M., & Shahbazbegian, M. (2020) · IEEE Access · doi:10.1109/access.2020.3009885 · Cited by 18
“To take the benefits of stochastic and fuzzy programming, Shibu and Reddy [30] proposed an MWSS design problem by applying a fuzzy stochastic programming method where a triangular fuzzy number was assigned to the water demand [31].”
MethodLi, Q., Li, M., Gan, J., & Guo, C. (2020) · International Transactions in Operational Research · doi:10.1111/itor.12900 · Cited by 18
Hu, B., Xu, S., Cao, Z., & Zhou, M. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3030607 · Cited by 17
Self-citationKumar, N. J., Dhinakaran, D., Kumar, A. N., & Kalpana, A. V. (2024) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.8127 · Cited by 17
Architectural Design of Cloud Applications: A Performance-Aware Cost Minimization Approach
Ciavotta, M., Gibilisco, G. P., Ardagna, D., Nitto, E. D., Lattuada, M., & da Silva, M. A. A. (2020) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2020.3015703 · Cited by 17
Alhazmi, M., Zhao, P., Li, W., & Yang, C. (2025) · IEEE Access · doi:10.1109/access.2025.3583436 · Cited by 17
“Yuan et al. [14] propose a resource allocation strategy that optimizes energy consumption and service quality under dynamic pricing.”
Multiobjective Demand Response for Internet Data Centers
Chiu, W., Hsieh, W.-K., Chen, C., & Chuang, Y.-C. (2021) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2021.3055232 · Cited by 16
Shaik, M., Reddy, K. S., Chokkanathan, K., Biabani, S. A. A., Shanmugaraja, P., & Brabin, D. R. D. (2024) · IEEE Access · doi:10.1109/access.2024.3489960 · Cited by 18
“A bi-objective differential evolution algorithm based on simulated annealing (SADE) is developed in [25].”
Optimizing Locations and Qualities of Multiple Facilities With Competition via Intelligent Search
Wu, P., Chu, F., Saidani, N., Chen, H., & Zhou, M. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3046885 · Cited by 16
Self-citationTask Scheduling in Geo-Distributed Computing: A Survey
Wu, Y., Tang, S., Yu, C., Yang, B., Sun, C., Xiao, J. K., Wu, H., & Feng, J. (2025) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2025.3591010 · Cited by 15
“For green cloud DCs, Yuan et al. [26] develop SA-based biobjective differential evolution (SBDE) that optimizes both revenue and energy consumption by considering spatial variations in renewable power generation and electricity pricing.”
Design and Control of a Wave-Driven Solar Tracker
Xu, R., Ji, X., Liu, C., Hou, J., Cao, Z., & Qian, H. (2022) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2022.3177353 · Cited by 15
“Solar energy is one of the most promising sources of renewable energy, and can be widely used in data centers [1], greenhouse [2], and other scenarios in the green industry.”
BackgroundAn integrated optimization method to task scheduling and VM placement for green datacenters
Liu, H., Zhou, X., Gao, K., & Ju, Y. (2024) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2024.102962 · Cited by 14
Hou, D., Wang, L., Ma, Y., Lyu, L., Liu, W., & Li, S. (2025) · Sustainability · doi:10.3390/su17083374 · Cited by 14
Barredo, P., & Puente, J. (2023) · Natural Computing · doi:10.1007/s11047-023-09950-5 · Cited by 13
“Different bio-inspired algorithms have been proposed, some of them based on physical processes, this is the case of Yuan et al. (2021), where Simulated Annealing is applied in a multi-objective approach, Biswas et al. (2019) propose the use of a Gravitational Search Algorithm (GSA), or Adhikari and…”
MethodAdaptive Scheduling Algorithm Based Task Loading in Cloud Data Centers
Mukherjee, D., Ghosh, S., Pal, S., Aly, A. A., & Le, D. (2022) · IEEE Access · doi:10.1109/access.2022.3168288 · Cited by 12
“[18] presented a system for task scheduling using bio objective.”
MethodChakraborty, T., Kopp, C., & Toosi, A. N. (2024) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2024.3487954 · Cited by 12
“Additionally, Yuan et al. [26] introduced a multi-objective optimization technique for DGDCs that aims to maximize the profit of DGDC providers and minimize the average task loss for all applications.”
Li, D., Zhang, Y., Song, J., Liu, H., & Jiang, J. (2022) · Sustainability · doi:10.3390/su14159005 · Cited by 11
Multi-Objective Discrete Migratory Bird Optimizer for Stochastic Disassembly Line Balancing Problem
Qin, G., Guo, X., Zhou, M., Liu, S., & Qi, L. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9283371 · Cited by 11
“Our future work intends to introduce other strategies into the proposed one to further improve its performance and apply it to other practical problems [1], [6], [22]-[32].”
BackgroundSelf-citationWu, K., Guo, X., Zhou, M., Liu, S., & Qi, L. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9282908 · Cited by 11
“Our future work intends to incorporate other optimization strategies [17-32] into it to make it more powerful to solve practical problems.”
BackgroundSelf-citationFigini, E., & Paolone, M. (2025) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2025.101920 · Cited by 10
“In [8], the authors present an optimization problem to schedule tasks in Distributed Green Data Centers (DGDCs), considering local factors (s.a. electricity prices, wind speed, solar irradiance, etc).”
Green Data Centers and Renewable Energy
Channi, H. K., & Kumar, P. (2024) · Practice, progress, and proficiency in sustainability · doi:10.4018/979-8-3693-6567-0.ch009 · Cited by 10
Smart Healthcare System Based on AIoT Emerging Technologies: A Brief Review
Prabha, C., Mittal, P., Gahlot, K., & Phul, V. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-0601-7_23 · Cited by 11
Energy-Aware Scheduling Based on Marginal Cost and Task Classification in Heterogeneous Data Centers
Ji, K., Chi, C., Zhang, F., Anta, A. F., Song, P., Marahatta, A., Wang, Y., & Liu, Z. (2021) · Energies · doi:10.3390/en14092382 · Cited by 9
A comparative study of GA and PSO approach for cost optimisation in product recovery systems
Dwivedi, A., Madaan, J., Chan, F. T. S., & Dalal, M. (2022) · International Journal of Production Research · doi:10.1080/00207543.2022.2035008 · Cited by 9
“Similarly, Yuan et al. (2020) designed a multi-objective optimisation method for Distributed Green Data Centres (DGDCs) to maximise the profit of DGDC providers and to minimise the average task loss possibility.”
MethodHan, O., Ding, T., Mu, C., Jia, W., Ma, Z., & Li, F. F. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3329004 · Cited by 8
“Due to the spatially and temporally transferable workloads, DCs show great potential in DR programs [7], [8], [9].”
BackgroundZhang, S., Guo, L., Guo, X., Liu, S., Qi, L., Qin, S., Tang, Y. G., & Zhao, Z. (2021) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc52481.2021.9702256 · Cited by 7
A New Bi-Objective Batch Scheduling Problem: NSGA-II-and-Local-Search-Based Memetic Algorithms
Zhao, Z. Y., Liu, S. X., & Zhou, M. C. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9283072 · Cited by 7
“Different from a single-objective optimization problem whose one or more equivalent exact solutions exist in its feasible domain, a multi-objective optimization problem has a Pareto set (or Pareto front), which contains all of its non-dominated solutions [18].”
BackgroundSelf-citationA non‐cooperative game model for reliability‐based task scheduling in cloud computing
Li, K., Wang, Y., & Liu, M. (2020) · International Journal of Communication Systems · doi:10.1002/dac.4512 · Cited by 7
Zade, B. M. H., Javidi, M. M., & Mansouri, N. (2023) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-023-01541-6 · Cited by 6
A Survey on Task Scheduling Algorithms in Cloud Computing for Fast Big Data Processing
Jalalian, Z., & Sharifi, M. (2021) · International Journal of Information & Communication Technology Research · doi:10.52547/itrc.13.4.28 · Cited by 6
“This lower latency as well as improved task scheduling scheme is proven to be considerably effective for medical, smart home, intelligent traffic and transportation, environmental-friendly green applications that have distributed or local nature [48].”
BackgroundHierRL: Hierarchical Reinforcement Learning for Task Scheduling in Distributed Systems
Guan, Y., Liu, Y., Li, Y., & Xu, X. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892507 · Cited by 6
Three decades of differential evolution: a bibliometric analysis (1995-2025)
Verma, P., Chaudhary, R., Gupta, H., & Salgotra, R. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131451 · Cited by 5
Mao, Y., & Jiao, X. (2026) · Electrical Engineering · doi:10.1007/s00202-026-03600-6 · Cited by 4
FOLD: Fog-dew infrastructure-aided optimal workload distribution for cloud robotic operations
Sarker, S. C., Arafat, M. T., Lameesa, A., Afrin, M., Mahmud, R., Razzaque, M. A., & Iqbal, T. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101185 · Cited by 4
Evolutionary Optimization of Energy Consumption and Makespan of Workflow Execution in Clouds
Xing, L., Li, J., Cai, Z., & Hou, F. (2023) · Mathematics · doi:10.3390/math11092126 · Cited by 4
Adaptive Pricing and Online Scheduling for Distributed Machine Learning Jobs
Wang, Y., Su, L., Chen, J., Wang, N., & Li, Z.-Q. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3336757 · Cited by 3
“Yuan et al. [23] focused on the task scheduling problem in distributed green data centers (GDCs) and proposed a multiobjective optimization method to jointly assign tasks to different service providers and determine service rates of all GDCs. Yuan et al. [24] proposed a geography-aware task…”
MethodEnergy Management for Renewable-Colocated Artificial Intelligence Data Centers
Li, S., Tong, L., & Mount, T. D. (2026) · IEEE PES International Meeting (PES IM) · doi:10.1109/pesim67009.2026.11439004 · Cited by 3
“In response to the growing volume of tasks generated by the digital economy, Yuan et al. [12] proposed a multi-objective optimization framework to determine task distribution across multiple internet service providers and the service rates of individual data centers.”
D3QN-based secure scheduling of microservice workflows in cloud environments
Long, S., Yang, J., Rao, C., Deng, Q., & Cao, K. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111227 · Cited by 3
Ammari, A. C., Labidi, W., & Al‐Hmouz, R. (2025) · Energies · doi:10.3390/en18112940 · Cited by 3
Self-citationContent Caching for IoT Devices by Using Self-Feedback Adversarial Semi-Bandits Learning
Li, J., Chen, P., Xia, Y., Zhou, M., Ma, Y., Dong, Y., Liu, H., Peng, Q., & Xu, X. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3598689 · Cited by 3
“Our novel contributions are as follows: 1) By employing an M / G / 1 queuing model [16], [17] for analyzing total request delay of ESs when serving multiple content requests from user-end IoT devices, we interpret the content caching problem as an adversarial semi-bandits problem.”
Self-citationLi, S., Liu, H., Gong, B., & Wang, J. (2020) · IEEE Access · doi:10.1109/access.2020.3003304 · Cited by 3
“This is not only for the low-carbon economy or sustainable human development, but also for the reliability and stability of the system [3]–[6].”
BackgroundChamkoori, A., & Katebi, S. (2023) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7580 · Cited by 3
Metadata
- Authors
- Yuan, H., Bi, J., Zhou, M., Liu, Q., & Ammari, A. C.
- Affiliations
- New Jersey Institute of Technology (US)
- Beijing University of Technology (CN)
- Sultan Qaboos University (OM)
- Venue
- IEEE Transactions on Automation Science and Engineering
- Published
- 2021-04
- Volume
- 18
- Issue
- 2
- Pages
- 731-742
- ISSN
- 1545-5955 (ISSN-L)
- 1558-3783
- OpenAlex
- W3000090293
- Cited by
- 138
- References
- 49
- Citations by year
- 2026: 10 · 2025: 16 · 2024: 17 · 2023: 23 · 2022: 21 · 2021: 25
- FWCI
- 22.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Automation Science and Engineering (publishedVersion)
- Funders
- National Natural Science Foundation of China (61802015, 61703011)
- Major Science and Technology Program for Water Pollution Control and Treatment of China (2018ZX07111005)
- National Defense Pre-Research Foundation of China (41401020401, 41401050102)
- Sultan Qaboos University through the Omantel Research Program (EG/SQU-OT/19/04)
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed and Parallel Computing Systems
- Keywords
- task scheduling, bi-objective optimization, simulated annealing, differential evolution, Pareto front, quality of service, renewable energy, smart grid, internet service providers, task decomposition, multi-objective optimization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
