{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T05:51:42Z","timestamp":1725947502146},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s11432-023-3895-3","type":"journal-article","created":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T14:02:25Z","timestamp":1706796145000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Reducing idleness in financial cloud services via multi-objective evolutionary reinforcement learning based load balancer"],"prefix":"10.1007","volume":"67","author":[{"given":"Peng","family":"Yang","sequence":"first","affiliation":[]},{"given":"Laoming","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Haifeng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Guiying","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,25]]},"reference":[{"key":"3895_CR1","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1109\/TSC.2017.2711009","volume":"11","author":"Y Al-Dhuraibi","year":"2018","unstructured":"Al-Dhuraibi Y, Paraiso F, Djarallah N, et al. Elasticity in cloud computing: state of the art and research challenges. IEEE Trans Serv Comput, 2018, 11: 430\u2013447","journal-title":"IEEE Trans Serv Comput"},{"key":"3895_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3148149","volume":"51","author":"C Qu","year":"2019","unstructured":"Qu C, Calheiros R N, Buyya R. Auto-scaling web applications in clouds: a taxonomy and survey. ACM Comput Surv, 2019, 51: 1\u201333","journal-title":"ACM Comput Surv"},{"key":"3895_CR3","doi-asserted-by":"crossref","unstructured":"Qureshi A, Weber R, Balakrishnan H, et al. Cutting the electric bill for internet-scale systems. In: Proceedings of the ACM SIGCOMM 2009 Conference on Data Communication, 2009. 123\u2013134","DOI":"10.1145\/1592568.1592584"},{"key":"3895_CR4","doi-asserted-by":"crossref","unstructured":"Shastri S, Rizk A, Irwin D. Transient guarantees: maximizing the value of idle cloud capacity. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2016. 992\u20131002","DOI":"10.1109\/SC.2016.84"},{"key":"3895_CR5","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1257\/089533006776526067","volume":"20","author":"H R Stoll","year":"2006","unstructured":"Stoll H R. Electronic trading in stock markets. J Economic Perspect, 2006, 20: 153\u2013174","journal-title":"J Economic Perspect"},{"key":"3895_CR6","doi-asserted-by":"publisher","first-page":"2263","DOI":"10.14778\/3352063.3352141","volume":"12","author":"F Li","year":"2019","unstructured":"Li F. Cloud-native database systems at Alibaba. Proc VLDB Endow, 2019, 12: 2263\u20132272","journal-title":"Proc VLDB Endow"},{"key":"3895_CR7","doi-asserted-by":"publisher","first-page":"118","DOI":"10.3905\/jpm.2011.37.2.118","volume":"37","author":"D Easley","year":"2011","unstructured":"Easley D, de Prado M M L, O\u2019Hara M. The microstructure of the \u201cflash crash\u201d: flow toxicity, liquidity crashes, and the probability of informed trading. J Portfolio Manage, 2011, 37: 118\u2013128","journal-title":"J Portfolio Manage"},{"key":"3895_CR8","doi-asserted-by":"crossref","unstructured":"Mirobi G J, Arockiam L. Dynamic load balancing approach for minimizing the response time using an enhanced throttled load balancer in cloud computing. In: Proceedings of International Conference on Smart Systems and Inventive Technology (ICSSIT), 2019. 570\u2013575","DOI":"10.1109\/ICSSIT46314.2019.8987845"},{"key":"3895_CR9","first-page":"238","volume":"9","author":"N J Kansal","year":"2012","unstructured":"Kansal N J, Chana I. Cloud load balancing techniques: a step towards green computing. IJCSI Int J Comput Sci Issues, 2012, 9: 238\u2013246","journal-title":"IJCSI Int J Comput Sci Issues"},{"key":"3895_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3281010","volume":"51","author":"P Kumar","year":"2019","unstructured":"Kumar P, Kumar R. Issues and challenges of load balancing techniques in cloud computing. ACM Comput Surv, 2019, 51: 1\u201335","journal-title":"ACM Comput Surv"},{"key":"3895_CR11","doi-asserted-by":"crossref","unstructured":"Lu C, Ye K, Xu G, et al. Imbalance in the cloud: an analysis on Alibaba cluster trace. In: Proceedings of IEEE International Conference on Big Data, 2017. 2884\u20132892","DOI":"10.1109\/BigData.2017.8258257"},{"key":"3895_CR12","first-page":"3910","volume":"34","author":"D A Shafiq","year":"2022","unstructured":"Shafiq D A, Jhanjhi N Z, Abdullah A. Load balancing techniques in cloud computing environment: a review. J King Saud Univ-Comput Inf Sci, 2022, 34: 3910\u20133933","journal-title":"J King Saud Univ-Comput Inf Sci"},{"key":"3895_CR13","first-page":"3217","volume":"12","author":"F T Johora","year":"2022","unstructured":"Johora F T, Ahmed I, Shajal M A I, et al. A load balancing strategy for reducing data loss risk on cloud using remodified throttled algorithm. Int J Electr Comput Eng, 2022, 12: 3217","journal-title":"Int J Electr Comput Eng"},{"key":"3895_CR14","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1007\/s10586-016-0700-8","volume":"20","author":"D C Marinescu","year":"2017","unstructured":"Marinescu D C, Paya A, Morrison J P, et al. An approach for scaling cloud resource management. Cluster Comput, 2017, 20: 909\u2013924","journal-title":"Cluster Comput"},{"key":"3895_CR15","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1109\/TSMCA.2003.817391","volume":"33","author":"K M Sim","year":"2003","unstructured":"Sim K M, Sun W H. Ant colony optimization for routing and load-balancing: survey and new directions. IEEE Trans Syst Man Cybern A, 2003, 33: 560\u2013572","journal-title":"IEEE Trans Syst Man Cybern A"},{"key":"3895_CR16","doi-asserted-by":"publisher","first-page":"37689","DOI":"10.1109\/ACCESS.2022.3161511","volume":"10","author":"E Gures","year":"2022","unstructured":"Gures E, Shayea I, Ergen M, et al. Machine learning-based load balancing algorithms in future heterogeneous networks: a survey. IEEE Access, 2022, 10: 37689\u201337717","journal-title":"IEEE Access"},{"key":"3895_CR17","doi-asserted-by":"crossref","unstructured":"Brar G K, Chhabra A. Meta-heuristics based load balancing algorithms in grid and clouds \u2014 a review. In: Proceedings of International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 2016. 2938\u20132943","DOI":"10.1109\/ICEEOT.2016.7755237"},{"key":"3895_CR18","doi-asserted-by":"crossref","unstructured":"Farag H, Stefanovi\u010d \u010c. Congestion-aware routing in dynamic IoT networks: a reinforcement learning approach. In: Proceedings of IEEE Global Communications Conference (GLOBECOM), 2021. 1\u20136","DOI":"10.1109\/GLOBECOM46510.2021.9685191"},{"key":"3895_CR19","unstructured":"Schulman J, Wolski F, Dhariwal P, et al. Proximal policy optimization algorithms. 2017. ArXiv:1707.06347"},{"key":"3895_CR20","first-page":"95","volume":"5","author":"S Mehta","year":"2017","unstructured":"Mehta S. Speckle noise reduction using hybrid wavelet packets-wiener filter. Int J Comput Sci Eng, 2017, 5: 95\u201399","journal-title":"Int J Comput Sci Eng"},{"key":"3895_CR21","first-page":"153","volume":"10","author":"A M Alakeel","year":"2010","unstructured":"Alakeel A M. A guide to dynamic load balancing in distributed computer systems. Int J Comput Sci Inform Secur, 2010, 10: 153\u2013160","journal-title":"Int J Comput Sci Inform Secur"},{"key":"3895_CR22","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.compeleceng.2016.01.029","volume":"58","author":"S L Chen","year":"2017","unstructured":"Chen S L, Chen Y Y, Kuo S H. CLB: a novel load balancing architecture and algorithm for cloud services. Comput Electr Eng, 2017, 58: 154\u2013160","journal-title":"Comput Electr Eng"},{"key":"3895_CR23","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1186\/s13677-019-0146-7","volume":"8","author":"S Afzal","year":"2019","unstructured":"Afzal S, Kavitha G. Load balancing in cloud computing \u2014 a hierarchical taxonomical classification. J Cloud Comp, 2019, 8: 22","journal-title":"J Cloud Comp"},{"key":"3895_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3539606","volume":"55","author":"C Carri\u00f3n","year":"2022","unstructured":"Carri\u00f3n C. Kubernetes scheduling: taxonomy, ongoing issues and challenges. ACM Comput Surv, 2022, 55: 1\u201337","journal-title":"ACM Comput Surv"},{"key":"3895_CR25","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1109\/TSC.2022.3174475","volume":"16","author":"M H Kashani","year":"2023","unstructured":"Kashani M H, Mahdipour E. Load balancing algorithms in fog computing. IEEE Trans Serv Comput, 2023, 16: 1505\u20131521","journal-title":"IEEE Trans Serv Comput"},{"key":"3895_CR26","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.cor.2016.04.020","volume":"74","author":"A Trivella","year":"2016","unstructured":"Trivella A, Pisinger D. The load-balanced multi-dimensional bin-packing problem. Comput Oper Res, 2016, 74: 152\u2013164","journal-title":"Comput Oper Res"},{"key":"3895_CR27","doi-asserted-by":"publisher","first-page":"1786","DOI":"10.1109\/TPDS.2019.2893648","volume":"30","author":"D Basu","year":"2019","unstructured":"Basu D, Wang X, Hong Y, et al. Learn-as-you-go with Megh: efficient live migration of virtual machines. IEEE Trans Parallel Distrib Syst, 2019, 30: 1786\u20131801","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"3895_CR28","doi-asserted-by":"publisher","first-page":"4818","DOI":"10.1109\/TPDS.2022.3202493","volume":"33","author":"J Zhu","year":"2022","unstructured":"Zhu J, Yang R, Sun X, et al. QoS-aware co-scheduling for distributed long-running applications on shared clusters. IEEE Trans Parallel Distrib Syst, 2022, 33: 4818\u20134834","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"3895_CR29","doi-asserted-by":"publisher","first-page":"139105","DOI":"10.1007\/s11432-021-3408-y","volume":"66","author":"S Zhang","year":"2023","unstructured":"Zhang S, Guo Y, Guo Z, et al. SMAF: a secure and makespan-aware framework for executing serverless workflows. Sci China Inf Sci, 2023, 66: 139105","journal-title":"Sci China Inf Sci"},{"key":"3895_CR30","doi-asserted-by":"publisher","first-page":"8519","DOI":"10.1109\/TII.2022.3165636","volume":"18","author":"G G Wang","year":"2022","unstructured":"Wang G G, Gao D, Pedrycz W. Solving multiobjective fuzzy job-shop scheduling problem by a hybrid adaptive differential evolution algorithm. IEEE Trans Ind Inf, 2022, 18: 8519\u20138528","journal-title":"IEEE Trans Ind Inf"},{"key":"3895_CR31","doi-asserted-by":"publisher","first-page":"2917","DOI":"10.1109\/TVCG.2012.219","volume":"18","author":"S Kandel","year":"2012","unstructured":"Kandel S, Paepcke A, Hellerstein J M, et al. Enterprise data analysis and visualization: an interview study. IEEE Trans Visual Comput Graph, 2012, 18: 2917\u20132926","journal-title":"IEEE Trans Visual Comput Graph"},{"key":"3895_CR32","unstructured":"Salimans T, Ho J, Chen X, et al. Evolution strategies as a scalable alternative to reinforcement learning. 2017. ArXiv:1703.03864"},{"key":"3895_CR33","doi-asserted-by":"publisher","first-page":"156336","DOI":"10.1007\/s11704-020-0241-4","volume":"15","author":"H Qian","year":"2021","unstructured":"Qian H, Yu Y. Derivative-free reinforcement learning: a review. Front Comput Sci, 2021, 15: 156336","journal-title":"Front Comput Sci"},{"key":"3895_CR34","doi-asserted-by":"publisher","first-page":"0025","DOI":"10.34133\/icomputing.0025","volume":"2","author":"H Bai","year":"2023","unstructured":"Bai H, Cheng R, Jin Y. Evolutionary reinforcement learning: a survey. Intell Comput, 2023, 2: 0025","journal-title":"Intell Comput"},{"key":"3895_CR35","unstructured":"Jianye H, Li P, Tang H, et al. ERL-Re2: efficient evolutionary reinforcement learning with shared state representation and individual policy representation. In: Proceedings of the 11th International Conference on Learning Representations, 2022"},{"key":"3895_CR36","doi-asserted-by":"publisher","first-page":"155333","DOI":"10.1007\/s11704-020-0431-0","volume":"15","author":"P Yang","year":"2021","unstructured":"Yang P, Yang Q, Tang K, et al. Parallel exploration via negatively correlated search. Front Comput Sci, 2021, 15: 155333","journal-title":"Front Comput Sci"},{"key":"3895_CR37","unstructured":"Wang Y, Xue K, Qian C. Evolutionary diversity optimization with clustering-based selection for reinforcement learning. In: Proceedings of International Conference on Learning Representations, 2022"},{"key":"3895_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2792984","volume":"48","author":"B Li","year":"2015","unstructured":"Li B, Li J, Tang K, et al. Many-objective evolutionary algorithms: a survey. ACM Comput Surv, 2015, 48: 1\u201335","journal-title":"ACM Comput Surv"},{"key":"3895_CR39","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.tcs.2022.12.011","volume":"943","author":"C Qian","year":"2023","unstructured":"Qian C, Liu D X, Feng C, et al. Multi-objective evolutionary algorithms are generally good: maximizing monotone submodular functions over sequences. Theor Comput Sci, 2023, 943: 241\u2013266","journal-title":"Theor Comput Sci"},{"key":"3895_CR40","doi-asserted-by":"publisher","first-page":"101261","DOI":"10.1016\/j.swevo.2023.101261","volume":"78","author":"S Wang","year":"2023","unstructured":"Wang S, Li B, Zhou A. A regularity augmented evolutionary algorithm with dual-space search for multiobjective optimization. Swarm Evolary Comput, 2023, 78: 101261","journal-title":"Swarm Evolary Comput"},{"key":"3895_CR41","doi-asserted-by":"publisher","first-page":"6091","DOI":"10.1109\/TCYB.2020.2966593","volume":"51","author":"W Hong","year":"2020","unstructured":"Hong W, Qian C, Tang K. Efficient minimum cost seed selection with theoretical guarantees for competitive influence maximization. IEEE Trans Cybern, 2020, 51: 6091\u20136104","journal-title":"IEEE Trans Cybern"},{"key":"3895_CR42","unstructured":"Liu S, Lu N, Hong W, et al. Effective and imperceptible adversarial textual attack via multi-objectivization. 2021. ArXiv:2111.01528"},{"key":"3895_CR43","doi-asserted-by":"crossref","unstructured":"Liu S, Peng F, Tang K. Reliable robustness evaluation via automatically constructed attack ensembles. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence, Washington, 2023. 8852\u20138860","DOI":"10.1609\/aaai.v37i7.26064"},{"key":"3895_CR44","doi-asserted-by":"publisher","unstructured":"Li B, Zhang Y, Yang P, et al. A two-population algorithm for large-scale multi-objective optimization based on fitness-aware operator and adaptive environmental selection. IEEE Trans Evol Comput, 2023, doi: https:\/\/doi.org\/10.1109\/TEVC.2023.3296488","DOI":"10.1109\/TEVC.2023.3296488"},{"key":"3895_CR45","doi-asserted-by":"publisher","first-page":"202201","DOI":"10.1007\/s11432-020-3092-y","volume":"64","author":"L Chen","year":"2021","unstructured":"Chen L, Xin B, Chen J. Interactive multiobjective evolutionary algorithm based on decomposition and compression. Sci China Inf Sci, 2021, 64: 202201","journal-title":"Sci China Inf Sci"},{"key":"3895_CR46","first-page":"35","volume":"53","author":"J G Falc\u00f3n-Cardona","year":"2020","unstructured":"Falc\u00f3n-Cardona J G, Coello C A C. Indicator-based multi-objective evolutionary algorithms: a comprehensive survey. ACM Comput Surv, 2020, 53: 35","journal-title":"ACM Comput Surv"},{"key":"3895_CR47","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput, 2002, 6: 182\u2013197","journal-title":"IEEE Trans Evol Comput"},{"key":"3895_CR48","doi-asserted-by":"crossref","unstructured":"Shen R, Zheng Y, Hao J, et al. Generating behavior-diverse game ais with evolutionary multi-objective deep reinforcement learning. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence, 2020. 3371\u20133377","DOI":"10.24963\/ijcai.2020\/466"},{"key":"3895_CR49","doi-asserted-by":"crossref","first-page":"7387","DOI":"10.1109\/TMC.2021.3139666","volume":"22","author":"F Song","year":"2023","unstructured":"Song F, Xing H, Wang X, et al. Evolutionary multi-objective reinforcement learning based trajectory control and task offloading in UAV-assisted mobile edge computing. IEEE Trans Mobile Comput, 2023, 22: 7387\u20137405","journal-title":"IEEE Trans Mobile Comput"},{"key":"3895_CR50","unstructured":"Xu J, Tian Y, Ma P, et al. Prediction-guided multi-objective reinforcement learning for continuous robot control. In: Proceedings of International Conference on Machine Learning, 2020. 10607\u201310616"},{"key":"3895_CR51","unstructured":"Abels A, Roijers D, Lenaerts T, et al. Dynamic weights in multi-objective deep reinforcement learning. In: Proceedings of International Conference on Machine Learning, 2019. 11\u201320"},{"key":"3895_CR52","first-page":"3483","volume":"15","author":"K van Moffaert","year":"2014","unstructured":"van Moffaert K, Now\u00e9 A. Multi-objective reinforcement learning using sets of Pareto dominating policies. J Mach Learn Res, 2014, 15: 3483\u20133512","journal-title":"J Mach Learn Res"},{"key":"3895_CR53","unstructured":"Kaushik R, Chatzilygeroudis K, Mouret J B. Multi-objective model-based policy search for data-efficient learning with sparse rewards. In: Proceedings of the 2nd Conference on Robot Learning, 2018. 839\u2013855"},{"key":"3895_CR54","unstructured":"Mao H, Venkatakrishnan S B, Schwarzkopf M, et al. Variance reduction for reinforcement learning in input-driven environments. In: Proceedings of International Conference on Learning Representations, 2019"},{"key":"3895_CR55","doi-asserted-by":"publisher","first-page":"150103","DOI":"10.1007\/s11432-020-3114-y","volume":"64","author":"C Bian","year":"2021","unstructured":"Bian C, Qian C, Yu Y, et al. On the robustness of median sampling in noisy evolutionary optimization. Sci China Inf Sci, 2021, 64: 150103","journal-title":"Sci China Inf Sci"},{"key":"3895_CR56","doi-asserted-by":"crossref","unstructured":"Hao H, Zhang X, Zhou A. Enhancing SAEAs with unevaluated solutions: a case study of relation model for expensive optimization. 2023. ArXiv:2309.11994","DOI":"10.1007\/s11432-023-3909-x"},{"key":"3895_CR57","first-page":"14","volume":"18","author":"S Liu","year":"2023","unstructured":"Liu S, Zhang Y, Tang K, et al. How good is neural combinatorial optimization? A systematic evaluation on the traveling salesman problem. IEEE Comput Intell Mag, 2023, 18: 14\u201328","journal-title":"IEEE Comput Intell Mag"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-023-3895-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-023-3895-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-023-3895-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,4]],"date-time":"2024-02-04T18:07:57Z","timestamp":1707070077000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-023-3895-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,25]]},"references-count":57,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["3895"],"URL":"https:\/\/doi.org\/10.1007\/s11432-023-3895-3","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,25]]},"assertion":[{"value":"27 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"120102"}}