{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T04:41:27Z","timestamp":1780375287475,"version":"3.54.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702320"],"award-info":[{"award-number":["61702320"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072301"],"award-info":[{"award-number":["62072301"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s11227-024-06534-7","type":"journal-article","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T14:55:42Z","timestamp":1730300142000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Mitigating interference of microservices with a scoring mechanism in large-scale clusters"],"prefix":"10.1007","volume":"81","author":[{"given":"Dingyu","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kangpeng","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiyou","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qin","family":"Hua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaixuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangtao","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"key":"6534_CR1","doi-asserted-by":"crossref","unstructured":"Tirmazi M, Barker A, Deng N, Haque ME, Qin ZG, Hand S, Harchol-Balter M, Wilkes J (2020) Borg: The Next Generation. In: EuroSys \u201920, pp. 1\u201314","DOI":"10.1145\/3342195.3387517"},{"key":"6534_CR2","doi-asserted-by":"crossref","unstructured":"Liu Q, Yu Z (2018) The Elasticity and Plasticity in Semi-Containerized Co-Locating Cloud workload: A View from Alibaba trace. In: Proceedings of the ACM Symposium on Cloud Computing (SoCC \u201918), pp. 347\u2013360","DOI":"10.1145\/3267809.3267830"},{"key":"6534_CR3","doi-asserted-by":"crossref","unstructured":"Zhu H, Erez M (2016) Dirigent: Enforcing Qos for Latency-Critical Tasks on Shared Multicore Systems. In: ASPLOS \u201916, pp. 33\u201347","DOI":"10.1145\/2954679.2872394"},{"key":"6534_CR4","doi-asserted-by":"crossref","unstructured":"Chen Q, Yang H, Guo M, Kannan RS, Mars J, Tang L (2017) Prophet: Precise qos Prediction on Non-Preemptive Accelerators to Improve Utilization in Warehouse-Scale Computers. In: ASPLOS \u201917, pp. 17\u201332","DOI":"10.1145\/3093336.3037700"},{"key":"6534_CR5","doi-asserted-by":"crossref","unstructured":"Chen S, Delimitrou C, Mart\u00ednez JF (2019) Parties: Qos-Aware Resource Partitioning for Multiple Interactive Services. In: ASPLOS \u201919, pp. 107\u201312","DOI":"10.1145\/3297858.3304005"},{"key":"6534_CR6","doi-asserted-by":"publisher","unstructured":"Patel T, Tiwari D (2020) Clite: Efficient and qos-Aware co-Location of Multiple Latency-Critical Jobs for Warehouse Scale Computers. In: HPCA \u201920, pp. 193\u2013206 . https:\/\/doi.org\/10.1109\/HPCA47549.2020.00025","DOI":"10.1109\/HPCA47549.2020.00025"},{"key":"6534_CR7","unstructured":"Iorgulescu C, Azimi R, Kwon Y, Elnikety S, Syamala M, Narasayya V, Herodotou H, Tomita P, Chen A, Zhang J, Wang J (2018) PerfIso: Performance Isolation for Commercial Latency-Sensitive Services. In: USENIX ATC 18, pp. 519\u2013532"},{"key":"6534_CR8","doi-asserted-by":"crossref","unstructured":"Lo D, Cheng L, Govindaraju R, Ranganathan P, Kozyrakis C (2015) Heracles: Improving Resource Efficiency at Scale. In: ISCA \u201915, pp. 450\u2013462","DOI":"10.1145\/2872887.2749475"},{"key":"6534_CR9","doi-asserted-by":"crossref","unstructured":"Park J, Park S, Baek W (2019) Copart: Coordinated Partitioning of Last-Level Cache and Memory Bandwidth for Fairness-Aware Workload Consolidation on Commodity Servers. In: EuroSys \u201919, pp. 1\u201316","DOI":"10.1145\/3302424.3303963"},{"key":"6534_CR10","doi-asserted-by":"crossref","unstructured":"Liu L, Wang H, Wang A, Xiao M, Cheng Y, Chen S (2021) Mind the Gap: Broken Promises of Cpu Reservations in Containerized Multi-Tenant Clouds. In: Proceedings of the ACM Symposium on Cloud Computing (SoCC \u201921), pp. 243\u2013257","DOI":"10.1145\/3472883.3486997"},{"key":"6534_CR11","doi-asserted-by":"publisher","unstructured":"Tirmazi M, Barker A, Deng N, Haque ME, Qin ZG, Hand S, Harchol-Balter M, Wilkes J (2020) Borg: The Next Generation. In: Proceedings of the Fifteenth European Conference on Computer Systems. EuroSys \u201920. Association for Computing Machinery, New York, NY, USA . https:\/\/doi.org\/10.1145\/3342195.3387517","DOI":"10.1145\/3342195.3387517"},{"key":"6534_CR12","doi-asserted-by":"crossref","unstructured":"Mars J, Tang L, Hundt R, Skadron K, Soffa ML (2011) Bubble-up: Increasing Utilization in Modern Warehouse Scale Computers Via Sensible Co-Locations. In: MICRO-44, pp. 248\u2013259","DOI":"10.1145\/2155620.2155650"},{"key":"6534_CR13","doi-asserted-by":"crossref","unstructured":"Delimitrou C, Kozyrakis C (2013) Paragon: Qos-Aware Scheduling for Heterogeneous Datacenters. In: ASPLOS \u201913, pp. 77\u201388","DOI":"10.1145\/2499368.2451125"},{"key":"6534_CR14","doi-asserted-by":"crossref","unstructured":"Rzadca K, Findeisen P, Swiderski J, Zych P, Broniek P, Kusmierek J, Nowak P, Strack B, Witusowski P, Hand S, Wilkes J (2020) Autopilot: Workload Autoscaling at Google. In: EuroSys \u201920, pp. 1\u201316","DOI":"10.1145\/3342195.3387524"},{"key":"6534_CR15","doi-asserted-by":"publisher","unstructured":"Kwan A, Wong J, Jacobsen H-A, Muthusamy V (2019) Hyscale: Hybrid and Network Scaling of Dockerized Microservices in Cloud Data Centres. In: 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pp. 80\u201390 . https:\/\/doi.org\/10.1109\/ICDCS.2019.00017","DOI":"10.1109\/ICDCS.2019.00017"},{"key":"6534_CR16","doi-asserted-by":"publisher","unstructured":"Verma A, Pedrosa L, Korupolu M, Oppenheimer D, Tune E, Wilkes J (2015) Large-Scale Cluster Management at Google with Borg. In: R\u00e9veill\u00e8re, L., Harris, T., Herlihy, M. (eds.) Proceedings of the Tenth European Conference on Computer Systems, EuroSys 2015, Bordeaux, France, April 21-24, 2015, pp. 18\u201311817. ACM, ??? . https:\/\/doi.org\/10.1145\/2741948.2741964","DOI":"10.1145\/2741948.2741964"},{"key":"6534_CR17","unstructured":"Hadary O, Marshall L, Menache I, Pan A, Greeff EE, Dion D, Dorminey S, Joshi S, Chen Y, Russinovich M, Moscibroda T (2020) Protean: VM Allocation Service at Scale. In: 14th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2020, Virtual Event, November 4-6, 2020, pp. 845\u2013861. USENIX Association, ???"},{"key":"6534_CR18","doi-asserted-by":"publisher","unstructured":"Liu Q, Yu Z (2018) The Elasticity and Plasticity in Semi-Containerized Co-Locating Cloud Workload: a View from Alibaba Trace. In: Proceedings of the ACM Symposium on Cloud Computing, SoCC 2018, Carlsbad, CA, USA, October 11-13, 2018, pp. 347\u2013360. ACM, ??? . https:\/\/doi.org\/10.1145\/3267809.3267830","DOI":"10.1145\/3267809.3267830"},{"key":"6534_CR19","unstructured":"Amvrosiadis G, Park JW, Ganger GR, Gibson GA, Baseman E, DeBardeleben N (2018) On the Diversity of Cluster Workloads and its Impact on Research Results. In: USENIX ATC 18, Boston, MA, pp. 533\u2013546"},{"key":"6534_CR20","doi-asserted-by":"crossref","unstructured":"Tian H, Zheng Y, Wang W(2019) Characterizing and Synthesizing Task Dependencies of Data-Parallel Jobs in Alibaba cloud. In: Proceedings of the ACM Symposium on Cloud Computing (SoCC \u201919), pp. 139\u2013151","DOI":"10.1145\/3357223.3362710"},{"key":"6534_CR21","doi-asserted-by":"crossref","unstructured":"Guo J, Chang Z, Wang S, Ding H, Feng Y, Mao L, Bao Y (2019) Who Limits the Resource Efficiency of my Datacenter: An Analysis of Alibaba Datacenter Traces. In: Proceedings of the International Symposium on Quality of Service (IWQoS \u201919), pp. 1\u201310","DOI":"10.1145\/3326285.3329074"},{"key":"6534_CR22","doi-asserted-by":"crossref","unstructured":"Sriraman A, Dhanotia A, Wenisch TF (2019) Softsku: Optimizing Server Architectures for Microservice Diversity @scale. In: ISCA \u201919, pp. 513\u2013526","DOI":"10.1145\/3307650.3322227"},{"key":"6534_CR23","doi-asserted-by":"crossref","unstructured":"Luo S, Xu H, Lu C, Ye K, Xu G, Zhang L, Ding Y, He J, Xu C (2021) Characterizing Microservice Dependency and Performance: Alibaba Trace Analysis. In: Proceedings of the ACM Symposium on Cloud Computing (SoCC \u201921), pp. 412\u2013426","DOI":"10.1145\/3472883.3487003"},{"key":"6534_CR24","doi-asserted-by":"crossref","unstructured":"Gan Y, Zhang Y, Cheng D, Shetty A, Rathi P, Katarki N, Bruno A, Hu J, Ritchken B, Jackson B, Hu K, Pancholi M, He Y, Clancy B, Colen C, Wen F, Leung C, Wang S, Zaruvinsky L, Espinosa M, Lin R, Liu Z, Padilla J, Delimitrou C (2019) An Open-Source Benchmark Suite for Microservices and Their Hardware-Software Implications for Cloud and Edge Systems. In: ASPLOS \u201919, pp. 3\u201318","DOI":"10.1109\/MM.2020.2985960"},{"key":"6534_CR25","doi-asserted-by":"crossref","unstructured":"Chen S, GalOn S, Delimitrou C, Manne S, Mart\u00ednez JF (2017) Workload Characterization of Interactive Cloud Services on Big and Small Server Platforms. In: 2017 IEEE International Symposium on Workload Characterization (IISWC), pp. 125\u2013134","DOI":"10.1109\/IISWC.2017.8167770"},{"key":"6534_CR26","doi-asserted-by":"publisher","unstructured":"Hua Q, Yang D, Qian S, Hu H, Cao J, Xue G (2023) Kae-Informer: A Knowledge Auto-Embedding Informer for Forecasting Long-Term Workloads of Microservices. In: Proceedings of the ACM Web Conference 2023. WWW \u201923, pp. 1551\u20131561. Association for Computing Machinery, New York, NY, USA . https:\/\/doi.org\/10.1145\/3543507.3583288","DOI":"10.1145\/3543507.3583288"},{"key":"6534_CR27","doi-asserted-by":"publisher","unstructured":"Wang M, Meng C, Long G, Wu C, Yang J, Lin W, Jia Y (2019) Characterizing Deep Learning Training Workloads on Alibaba-pai. In: 2019 IEEE International Symposium on Workload Characterization (IISWC), pp. 189\u2013202 . https:\/\/doi.org\/10.1109\/IISWC47752.2019.9042047","DOI":"10.1109\/IISWC47752.2019.9042047"},{"key":"6534_CR28","unstructured":"Sapio A, Canini M, Ho C-Y, Nelson J, Kalnis P, Kim C, Krishnamurthy A, Moshref M, Ports D, Richtarik P (2021) Scaling Distributed Machine Learning with In-Network Aggregation. In: NSDI 21, pp. 785\u2013808"},{"key":"6534_CR29","doi-asserted-by":"crossref","unstructured":"Peng Y, Zhu Y, Chen Y, Bao Y, Yi B, Lan C, Wu C, Guo C (2019) A Generic Communication Scheduler for Distributed DNN Training Acceleration. In: SOSP \u201919, pp. 16\u201329","DOI":"10.1145\/3341301.3359642"},{"key":"6534_CR30","doi-asserted-by":"crossref","unstructured":"Weng Q, Xiao W, Yu Y, Wang W, Wang C, He J, Li Y, Zhang L, Lin W, Ding Y (2022) MLaaS in the Wild: Workload Analysis and Scheduling in Large-Scale heterogeneous GPU clusters. In: NSDI 22, pp. 945\u2013960","DOI":"10.21203\/rs.3.rs-2266264\/v1"},{"key":"6534_CR31","doi-asserted-by":"publisher","unstructured":"Kambadur M, Moseley T, Hank R, Kim MA (2012) Measuring Interference Between Live Datacenter Applications. In: SC \u201912, pp. 1\u201312 . https:\/\/doi.org\/10.1109\/SC.2012.78","DOI":"10.1109\/SC.2012.78"},{"key":"6534_CR32","unstructured":"Novakovi\u0107 D, Vasi\u0107 N, Novakovi\u0107 S, Kosti\u0107 D, Bianchini R (2013) Deepdive: Transparently Identifying and Managing Performance Interference in Virtualized Environments. In: USENIX ATC 13, pp. 219\u2013230"},{"key":"6534_CR33","doi-asserted-by":"crossref","unstructured":"Zhang X, Tune E, Hagmann R, Jnagal R, Gokhale V, Wilkes J (2013) Cpi2: CPU Performance Isolation for Shared Compute Clusters. In: EuroSys \u201913, pp. 379\u2013391","DOI":"10.1145\/2465351.2465388"},{"key":"6534_CR34","doi-asserted-by":"crossref","unstructured":"Yang H, Breslow A, Mars J, Tang L (2013) Bubble-Flux: Precise Online QOS Management for Increased Utilization in Warehouse Scale Computers. In: ISCA \u201913, pp. 607\u2013618","DOI":"10.1145\/2508148.2485974"},{"key":"6534_CR35","doi-asserted-by":"publisher","unstructured":"Delimitrou C, Kozyrakis C (2013) ibench: Quantifying Interference for Datacenter Applications. In: 2013 IEEE International Symposium on Workload Characterization (IISWC), pp. 23\u201333 . https:\/\/doi.org\/10.1109\/IISWC.2013.6704667","DOI":"10.1109\/IISWC.2013.6704667"},{"key":"6534_CR36","doi-asserted-by":"crossref","unstructured":"Gan Y, Zhang Y, Hu K, Cheng D, He Y, Pancholi M, Delimitrou C (2019) Seer: Leveraging Big Data to Navigate the Complexity of Performance Debugging in Cloud Microservices. In: ASPLOS \u201919, pp. 19\u201333","DOI":"10.1145\/3297858.3304004"},{"key":"6534_CR37","doi-asserted-by":"crossref","unstructured":"Romero F, Delimitrou C (2018) Mage: Online and Interference-Aware Scheduling for Multi-Scale Heterogeneous Systems. In: Proceedings of the 27th International Conference on Parallel Architectures and Compilation Techniques (PACT \u201918), pp. 1\u201313","DOI":"10.1145\/3243176.3243183"},{"key":"6534_CR38","doi-asserted-by":"publisher","unstructured":"Gan Y, Liang M, Dev S, Lo D, Delimitrou C (2021) Sage: Practical and Scalable ML-Driven Performance Debugging in Microservices. In: Proceedings of the Twenty-Sixth International Conference on Architectural Support for Programming Languages and Operating Systems. ASPLOS \u201921, pp. 135\u2013151. Association for Computing Machinery, New York, NY, USA . https:\/\/doi.org\/10.1145\/3445814.3446700","DOI":"10.1145\/3445814.3446700"},{"key":"6534_CR39","unstructured":"Ikram A, Chakraborty S, Mitra S, Saini S, Bagchi S, Kocaoglu M (2022) Root Cause Analysis of Failures in Microservices Through Causal Discovery. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems, vol. 35, pp. 31158\u201331170. Curran Associates, Inc., ??? . https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2022\/file\/c9fcd02e6445c7dfbad6986abee53d0d-Paper-Conference.pdf"},{"key":"6534_CR40","doi-asserted-by":"crossref","unstructured":"Delimitrou C, Kozyrakis C (2014) Quasar: Resource-Efficient and QOS-Aware Cluster Management. In: ASPLOS \u201914, pp. 127\u2013144","DOI":"10.1145\/2644865.2541941"},{"key":"6534_CR41","doi-asserted-by":"crossref","unstructured":"Isard M, Prabhakaran V, Currey J, Wieder U, Talwar K, Goldberg A (2009) Quincy: Fair Scheduling for Distributed Computing Clusters. In: SOSP \u201909, pp. 261\u2013276","DOI":"10.1145\/1629575.1629601"},{"key":"6534_CR42","doi-asserted-by":"crossref","unstructured":"Yang D, Xiao Z, Zhang D, Zhang S, Cao J, Chen G (2024) Preact: Predictive Resource Allocation for Bursty Workloads in a Co-Located Data Center. In: ICPP\u201924, pp. 0\u201311","DOI":"10.1145\/3673038.3673135"},{"key":"6534_CR43","unstructured":"Delgado P, Dinu F, Kermarrec A-M, Zwaenepoel W (2015) Hawk: Hybrid Datacenter Scheduling. In: USENIX ATC 15, pp. 499\u2013510"},{"key":"6534_CR44","unstructured":"Qiu H, Banerjee SS, Jha S, Kalbarczyk ZT, Iyer RK (2020) FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservices. In: 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20), pp. 805\u2013825. USENIX Association, ??? . https:\/\/www.usenix.org\/conference\/osdi20\/presentation\/qiu"},{"key":"6534_CR45","doi-asserted-by":"crossref","unstructured":"Cortez E, Bonde A, Muzio A, Russinovich M, Fontoura M, Bianchini R (2017) Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms. In: SOSP \u201917, pp. 153\u2013167","DOI":"10.1145\/3132747.3132772"},{"key":"6534_CR46","unstructured":"Chung A, Krishnan S, Karanasos K, Curino C, Ganger GR (2020) Unearthing Inter-Job Dependencies for Better Cluster Scheduling. In: OSDI 20, pp. 1205\u20131223"},{"key":"6534_CR47","doi-asserted-by":"crossref","unstructured":"Jyothi SA, Curino C, Menache I, Narayanamurthy SM, Tumanov A, Yaniv J, Mavlyutov R, Goiri I, Krishnan S, Kulkarni J, Rao S (2016) Morpheus: Towards Automated SLOs for Enterprise Clusters. In: OSDI 16, Savannah, GA, pp. 117\u2013134","DOI":"10.18374\/IJBR-16-2.9"},{"key":"6534_CR48","doi-asserted-by":"publisher","unstructured":"Mars J, Tang L, Hundt R, Skadron K, Soffa ML (2011) Bubble-Up: Increasing Utilization in Modern Warehouse Scale Computers Via Sensible Co-Locations. In: Galuzzi, C., Carro, L., Moshovos, A., Prvulovic, M. (eds.) 44rd Annual IEEE\/ACM International Symposium on Microarchitecture, MICRO 2011, Porto Alegre, Brazil, December 3-7, 2011, pp. 248\u2013259. ACM, ??? . https:\/\/doi.org\/10.1145\/2155620.2155650","DOI":"10.1145\/2155620.2155650"},{"key":"6534_CR49","doi-asserted-by":"publisher","unstructured":"Kabir R, Kim RG, Nikdast M (2023) RISA: Round-Robin Intra-Rack Friendly Scheduling Algorithm for Disaggregated Datacenters. In: Proceedings of the SC \u201923 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, SC-W 2023, Denver, CO, USA, November 12-17, 2023, pp. 1512\u20131520. ACM, ??? . https:\/\/doi.org\/10.1145\/3624062.3624228","DOI":"10.1145\/3624062.3624228"},{"key":"6534_CR50","doi-asserted-by":"publisher","unstructured":"Bao Y, Peng Y, Wu C (2019) Deep Learning-Based Job Placement in Distributed Machine Learning Clusters. In: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, pp. 505\u2013513 . https:\/\/doi.org\/10.1109\/INFOCOM.2019.8737460","DOI":"10.1109\/INFOCOM.2019.8737460"},{"key":"6534_CR51","doi-asserted-by":"crossref","unstructured":"Tang X, Wang H, Ma X, El-Sayed N, Zhai J, Chen W, Aboulnaga A (2019) Spread-n-share: Improving Application Performance and Cluster Throughput with Resource-Aware Job Placement. In: SC \u201919, pp. 1\u201315","DOI":"10.1145\/3295500.3356152"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06534-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06534-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06534-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T15:08:58Z","timestamp":1730300938000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06534-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,30]]},"references-count":51,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["6534"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06534-7","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,30]]},"assertion":[{"value":"17 September 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We confirm that the study has not been published and is not under consideration for publication elsewhere. Further, this submission has been approved by all authors and the institution where the study was conducted (Zhejiang University and Shanghai Jiao Tong University), and we wish to be considered for publication in The Journal of Supercomputing.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}],"article-number":"104"}}