Artificial intelligence-based Kubernetes container for scheduling nodes of energy composition
Kubernetes is a portable, extensible, open-source platform for managing containerized workloads and services that facilitates both declarative configuration and automation. This study presents Kubernetes Container Scheduling Strategy (KCSS) based on Artificial Intelligence (AI) that can assist in de...
- Autores:
-
Diaz Martinez, Jorge Luis
Aziz Butt, Shariq
Michael Onyema, Edeh
Chakraborty, Dr. Chinmay
Shaheen, Qaisar
De-La-Hoz-Franco, Emiro
Ariza Colpas, Paola Patricia
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8586
- Acceso en línea:
- https://hdl.handle.net/11323/8586
https://doi.org/10.1007/s13198-021-01195-8
https://repositorio.cuc.edu.co/
- Palabra clave:
- Artificial intelligence
Automated scheduling
Cloud infrastructure
Kubernetes
Multi-criteria scheduler
Scheduling strategy
- Rights
- embargoedAccess
- License
- CC0 1.0 Universal
| id |
RCUC2_c429f6260b1e019c5fd45977ff0f255a |
|---|---|
| oai_identifier_str |
oai:repositorio.cuc.edu.co:11323/8586 |
| network_acronym_str |
RCUC2 |
| network_name_str |
REDICUC - Repositorio CUC |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Artificial intelligence-based Kubernetes container for scheduling nodes of energy composition |
| title |
Artificial intelligence-based Kubernetes container for scheduling nodes of energy composition |
| spellingShingle |
Artificial intelligence-based Kubernetes container for scheduling nodes of energy composition Artificial intelligence Automated scheduling Cloud infrastructure Kubernetes Multi-criteria scheduler Scheduling strategy |
| title_short |
Artificial intelligence-based Kubernetes container for scheduling nodes of energy composition |
| title_full |
Artificial intelligence-based Kubernetes container for scheduling nodes of energy composition |
| title_fullStr |
Artificial intelligence-based Kubernetes container for scheduling nodes of energy composition |
| title_full_unstemmed |
Artificial intelligence-based Kubernetes container for scheduling nodes of energy composition |
| title_sort |
Artificial intelligence-based Kubernetes container for scheduling nodes of energy composition |
| dc.creator.fl_str_mv |
Diaz Martinez, Jorge Luis Aziz Butt, Shariq Michael Onyema, Edeh Chakraborty, Dr. Chinmay Shaheen, Qaisar De-La-Hoz-Franco, Emiro Ariza Colpas, Paola Patricia |
| dc.contributor.author.spa.fl_str_mv |
Diaz Martinez, Jorge Luis Aziz Butt, Shariq Michael Onyema, Edeh Chakraborty, Dr. Chinmay Shaheen, Qaisar De-La-Hoz-Franco, Emiro Ariza Colpas, Paola Patricia |
| dc.subject.spa.fl_str_mv |
Artificial intelligence Automated scheduling Cloud infrastructure Kubernetes Multi-criteria scheduler Scheduling strategy |
| topic |
Artificial intelligence Automated scheduling Cloud infrastructure Kubernetes Multi-criteria scheduler Scheduling strategy |
| description |
Kubernetes is a portable, extensible, open-source platform for managing containerized workloads and services that facilitates both declarative configuration and automation. This study presents Kubernetes Container Scheduling Strategy (KCSS) based on Artificial Intelligence (AI) that can assist in decision making to control the scheduling and shifting of load to nodes. The aim is to improve the container’s schedule requested digitally from users to enhance the efficiency in scheduling and reduce cost. The constraints associated with the existing container scheduling techniques which often assign a node to every new container based on a personal criterion by relying on individual terms has been greatly improved by the new system presented in this study. The KCSS presented in this study provides multicriteria node selection based on artificial intelligence in terms of decision making systems thereby giving the scheduler a broad picture of the cloud's condition and the user's requirements. AI Scheduler allows users to easily make use of fractional Graphics Processing Units (GPUs), integer GPUs, and multiple-nodes of GPUs, for distributed training on Kubernetes. © 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden. |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2021-08-24T16:57:13Z |
| dc.date.available.none.fl_str_mv |
2021-08-24T16:57:13Z |
| dc.date.issued.none.fl_str_mv |
2021-07-17 |
| dc.date.embargoEnd.none.fl_str_mv |
2022-07-17 |
| dc.type.spa.fl_str_mv |
Artículo de revista |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| format |
http://purl.org/coar/resource_type/c_6501 |
| status_str |
acceptedVersion |
| dc.identifier.issn.spa.fl_str_mv |
09756809 |
| dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/8586 |
| dc.identifier.doi.spa.fl_str_mv |
https://doi.org/10.1007/s13198-021-01195-8 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
09756809 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/8586 https://doi.org/10.1007/s13198-021-01195-8 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
Ahmed E, Gani A, Khan MK, Buyya R, Khan SU (2015) Seamless application execution in mobile cloud computing: motivation, taxonomy, and open challenges. J Netw Comput Appl 52:154–172 Ahmed E, Gani A, Khan MK, Buyya R, Khan SU (2015) Seamless application execution in mobile cloud computing: motivation, taxonomy, and open challenges. J Netw Comput Appl 52:154–172 Alicherry M, Lakshman T (2013) Optimizing data access latencies in cloud systems by intelligent virtual machine placement. IEEE INFOCOM. pp. 647–655 Amit K, Chinmay C, Wilson J, Kishor A, Chakraborty C, Jeberson W (2020) A novel fog computing approach for minimization of latency in healthcare using machine learning. Int J Interact Multimedia Artif Intell. https://doi.org/10.9781/ijimai.2020.12.004 Amit S, Lalit G, Chinmay C (2021) Improvement of system performance in an IT production support environment. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-021-01092-0 Ardagna D, Panicucci B, Trubian M, Zhang L (2012) Energy-aware autonomic resource allocation in multitier virtualized environments. IEEE Trans Serv Comput 5(1):2–19 Ariza-Colpas PP, Ayala-Mantilla CE, Shaheen Q, Piñeres-Melo MA, Villate-Daza DA, Morales-Ortega RC, De-la-Hoz-Franco E, Sanchez-Moreno H, Aziz BS, Afzal M (2021) SISME, estuarine monitoring system based on IOT and machine learning for the detection of salt wedge in aquifers: case study of the Magdalena River estuary. Sensors 21(7):2374 Aroca JA, Anta AF, Mosteiro MA, Thraves C, Wang L (2016) Power-efficient assignment of virtual machines to physical machines. Future Generat Comput Syst 54:82–94 Baccarelli E, Amendola D, Cordeschi N (2015) Minimum-energy bandwidth management for qos live migration of virtual machines. Comput Network 93:1–22 Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generat Comput Syst 28(5):755–768 Chen X, Li C, Jiang Y (2015) Optimization model and algorithm for energy efficient virtual node embedding. IEEE Commun Lett 19(8):1327–1330 Chinmay C, Roy R, Pathak S, Chakrabarti S (2011) An optimal probabilistic traffic engineering scheme for heterogeneous networks. CIIT Int J Fuzzy Syst 3(2):35–39 Chinmay C, Roy R (2012) Markov decision process based optimal gateway selection algorithm. Int J Syst Algorithms Appl 48–52 Chowdhury N, Rahman M, Boutaba R (2009) Virtual network embedding with coordinated node and link mapping. In: IEEE INFOCOM, pp. 783–791 Cordeschi N, Patriarca T, Baccarelli E (2012) Stochastic traffic engineering for realtime applications over wireless networks. J Netw Comput Appl 35(2):681–694 Dean J, Ghemawat S (2008) Mapreduce: simplified data processing on large clusters. Commun ACM 51(1):107–113 Dłaz M, Martłn C, Rubio B (2016) State-of-the-art, challenges, and open issues in the integration of internet of things and cloud computing. J Netw Comput Appl 67:99–117 Elhady GF, Tawfeek MA (2015) A comparative study into swarm intelligence algorithms for dynamic tasks scheduling in cloud computing. In: 2015 IEEE Seventh international conference on intelligent computing and information systems (ICICIS) (pp. 362–369). IEEE Fazio M, Celesti A, Ranjan R, Liu C, Chen L, Villari M (2016) Open issues in scheduling microservices in the cloud. IEEE Cloud Comput 3(5):81–88 Felter W, Ferreira A, Rajamony R, Rubio J, (2015) An updated performance comparison of virtual machines and linux containers. In: 2015 IEEE International symposium on performance analysis of systems and software (ISPASS), pp. 171–172 Gill SS, Tuli S, Xu M, Singh I, Singh KV, Lindsay D, Tuli S, Smirnova D, Singh M, Jain U, Pervaiz H (2019) Transformative effects of IoT, blockchain and artificial intelligence on cloud computing: evolution, vision, trends and open challenges. Internet Things 8:100118 Guan X, Choi BY, Song S (2015) Energy efficient virtual network embedding for green dcs using dc topology and future migration. Comput Commun 69:50–59 Guan X, Choi BY, Song S (2014) Topology and migration-aware energy efficient virtual network embedding for green dcs. In: 23rd International conference on computer communication and networks (ICCCN). IEEE, pp. 1–8 Guerrero C, Lera I, Juiz C (2018) Genetic algorithm for multi-objective optimization of container allocation in cloud architecture. J Grid Comput 16(1):113–135 Jiang J, Lu J, Zhang G, Long G (2013) Optimal cloud resource auto-scaling for web applications. In: International symposium on cluster, cloud and grid computing (CCGrid), pp. 58–65 Kaewkasi C, Chuenmuneewong K (2017) Improvement of container scheduling for docker using ant colony optimization. In: 9th International conference on knowledge and smart technology (KST), pp. 254–259 López-Torres S, López-Torres H, Rocha-Rocha J, Butt SA, Tariq MI, Collazos-Morales C, Piñeres-Espitia G (2019) IoT monitoring of water consumption for irrigation systems using SEMMA methodology. In: International conference on intelligent human computer interaction (pp. 222–234). Springer, Cham Onyema EM (2019) Integration of emerging technologies in teaching and learning process in Nigeria: the challenges. Central Asian J Math Theory Comput Sci 1(1):35–39 Rimal Y, Pandit P, Gocchait S, Butt SA, Obaid AJ (1804) (2021) Hyperparameter determines the best learning curve on single, multi-layer and deep neural network of student grade prediction of Pokhara University Nepal. J Phys Conf Ser 1:012054 Sachin D, Chinmay C, Jaroslav F, Rashmi G, Arun KR, Subhendu KP (2021) SSII: Secured and high-quality Steganography using Intelligent hybrid optimization algorithms for IoT. IEEE Access 9:1–16. https://doi.org/10.1109/ACCESS.2021.3089357 Shaheen Q, Shiraz M, Hashmi MU, Mahmood D, Akhtar R (2020) A lightweight location-aware fog framework (LAFF) for QoS in internet of things paradigm. Mobile Inf Syst. https://doi.org/10.1155/2020/8871976 Zheng Y, Cai L, Huang S, WangZ (2014) VM scheduling strategies based on artificial intelligence in Cloud Testing. In: 15th IEEE/ACIS international conference on software engineering, artificial intelligence, networking and parallel/distributed computing (SNPD) (pp. 1–7). IEEE |
| dc.rights.spa.fl_str_mv |
CC0 1.0 Universal |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/publicdomain/zero/1.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
| dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_f1cf |
| rights_invalid_str_mv |
CC0 1.0 Universal http://creativecommons.org/publicdomain/zero/1.0/ http://purl.org/coar/access_right/c_f1cf |
| eu_rights_str_mv |
embargoedAccess |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
International Journal of Systems Assurance Engineering and Management |
| dc.source.spa.fl_str_mv |
International Journal of Systems Assurance Engineering and Management |
| institution |
Corporación Universidad de la Costa |
| dc.source.url.spa.fl_str_mv |
https://link.springer.com/article/10.1007%2Fs13198-021-01195-8 |
| bitstream.url.fl_str_mv |
https://repositorio.cuc.edu.co/bitstreams/a74c106f-bbbc-43d6-b079-7701325ad562/download https://repositorio.cuc.edu.co/bitstreams/a303f0fa-ee6f-483b-be7c-cf2ba583d35e/download https://repositorio.cuc.edu.co/bitstreams/e1e601ef-c521-4b93-b66d-1f31176b83f7/download https://repositorio.cuc.edu.co/bitstreams/454671be-636f-44cc-886b-92cdf8e26ac4/download https://repositorio.cuc.edu.co/bitstreams/d3de6023-b9d0-4b46-87be-c78bfafb1a47/download |
| bitstream.checksum.fl_str_mv |
f12cc6ae60518835fbab7fd7cf80913f 42fd4ad1e89814f5e4a476b409eb708c e30e9215131d99561d40d6b0abbe9bad d13f38f9d050864d6ca3220d2e91bd92 a9e03f9aec5cfd8cf8dd257d9fc55ce1 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio de la Universidad de la Costa CUC |
| repository.mail.fl_str_mv |
repdigital@cuc.edu.co |
| _version_ |
1811760756855668736 |
| spelling |
Diaz Martinez, Jorge LuisAziz Butt, ShariqMichael Onyema, EdehChakraborty, Dr. ChinmayShaheen, QaisarDe-La-Hoz-Franco, EmiroAriza Colpas, Paola Patricia2021-08-24T16:57:13Z2021-08-24T16:57:13Z2021-07-172022-07-1709756809https://hdl.handle.net/11323/8586https://doi.org/10.1007/s13198-021-01195-8Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Kubernetes is a portable, extensible, open-source platform for managing containerized workloads and services that facilitates both declarative configuration and automation. This study presents Kubernetes Container Scheduling Strategy (KCSS) based on Artificial Intelligence (AI) that can assist in decision making to control the scheduling and shifting of load to nodes. The aim is to improve the container’s schedule requested digitally from users to enhance the efficiency in scheduling and reduce cost. The constraints associated with the existing container scheduling techniques which often assign a node to every new container based on a personal criterion by relying on individual terms has been greatly improved by the new system presented in this study. The KCSS presented in this study provides multicriteria node selection based on artificial intelligence in terms of decision making systems thereby giving the scheduler a broad picture of the cloud's condition and the user's requirements. AI Scheduler allows users to easily make use of fractional Graphics Processing Units (GPUs), integer GPUs, and multiple-nodes of GPUs, for distributed training on Kubernetes. © 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.Diaz Martinez, Jorge Luis-will be generated-orcid-0000-0001-9555-0424-600Aziz Butt, ShariqMichael Onyema, Edeh-will be generated-orcid-0000-0002-4067-3256-600Chakraborty, Dr. Chinmay-will be generated-orcid-0000-0002-4385-0975-600Shaheen, Qaisar-will be generated-orcid-0000-0002-1839-1412-600De-La-Hoz-Franco, Emiro-will be generated-orcid-0000-0002-4926-7414-600Ariza Colpas, Paola Patricia-will be generated-orcid-0000-0003-4503-5461-600application/pdfengInternational Journal of Systems Assurance Engineering and ManagementCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfInternational Journal of Systems Assurance Engineering and Managementhttps://link.springer.com/article/10.1007%2Fs13198-021-01195-8Artificial intelligenceAutomated schedulingCloud infrastructureKubernetesMulti-criteria schedulerScheduling strategyArtificial intelligence-based Kubernetes container for scheduling nodes of energy compositionArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionAhmed E, Gani A, Khan MK, Buyya R, Khan SU (2015) Seamless application execution in mobile cloud computing: motivation, taxonomy, and open challenges. J Netw Comput Appl 52:154–172Ahmed E, Gani A, Khan MK, Buyya R, Khan SU (2015) Seamless application execution in mobile cloud computing: motivation, taxonomy, and open challenges. J Netw Comput Appl 52:154–172Alicherry M, Lakshman T (2013) Optimizing data access latencies in cloud systems by intelligent virtual machine placement. IEEE INFOCOM. pp. 647–655Amit K, Chinmay C, Wilson J, Kishor A, Chakraborty C, Jeberson W (2020) A novel fog computing approach for minimization of latency in healthcare using machine learning. Int J Interact Multimedia Artif Intell. https://doi.org/10.9781/ijimai.2020.12.004Amit S, Lalit G, Chinmay C (2021) Improvement of system performance in an IT production support environment. Int J Syst Assur Eng Manag. https://doi.org/10.1007/s13198-021-01092-0Ardagna D, Panicucci B, Trubian M, Zhang L (2012) Energy-aware autonomic resource allocation in multitier virtualized environments. IEEE Trans Serv Comput 5(1):2–19Ariza-Colpas PP, Ayala-Mantilla CE, Shaheen Q, Piñeres-Melo MA, Villate-Daza DA, Morales-Ortega RC, De-la-Hoz-Franco E, Sanchez-Moreno H, Aziz BS, Afzal M (2021) SISME, estuarine monitoring system based on IOT and machine learning for the detection of salt wedge in aquifers: case study of the Magdalena River estuary. Sensors 21(7):2374Aroca JA, Anta AF, Mosteiro MA, Thraves C, Wang L (2016) Power-efficient assignment of virtual machines to physical machines. Future Generat Comput Syst 54:82–94Baccarelli E, Amendola D, Cordeschi N (2015) Minimum-energy bandwidth management for qos live migration of virtual machines. Comput Network 93:1–22Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generat Comput Syst 28(5):755–768Chen X, Li C, Jiang Y (2015) Optimization model and algorithm for energy efficient virtual node embedding. IEEE Commun Lett 19(8):1327–1330Chinmay C, Roy R, Pathak S, Chakrabarti S (2011) An optimal probabilistic traffic engineering scheme for heterogeneous networks. CIIT Int J Fuzzy Syst 3(2):35–39Chinmay C, Roy R (2012) Markov decision process based optimal gateway selection algorithm. Int J Syst Algorithms Appl 48–52Chowdhury N, Rahman M, Boutaba R (2009) Virtual network embedding with coordinated node and link mapping. In: IEEE INFOCOM, pp. 783–791Cordeschi N, Patriarca T, Baccarelli E (2012) Stochastic traffic engineering for realtime applications over wireless networks. J Netw Comput Appl 35(2):681–694Dean J, Ghemawat S (2008) Mapreduce: simplified data processing on large clusters. Commun ACM 51(1):107–113Dłaz M, Martłn C, Rubio B (2016) State-of-the-art, challenges, and open issues in the integration of internet of things and cloud computing. J Netw Comput Appl 67:99–117Elhady GF, Tawfeek MA (2015) A comparative study into swarm intelligence algorithms for dynamic tasks scheduling in cloud computing. In: 2015 IEEE Seventh international conference on intelligent computing and information systems (ICICIS) (pp. 362–369). IEEEFazio M, Celesti A, Ranjan R, Liu C, Chen L, Villari M (2016) Open issues in scheduling microservices in the cloud. IEEE Cloud Comput 3(5):81–88Felter W, Ferreira A, Rajamony R, Rubio J, (2015) An updated performance comparison of virtual machines and linux containers. In: 2015 IEEE International symposium on performance analysis of systems and software (ISPASS), pp. 171–172Gill SS, Tuli S, Xu M, Singh I, Singh KV, Lindsay D, Tuli S, Smirnova D, Singh M, Jain U, Pervaiz H (2019) Transformative effects of IoT, blockchain and artificial intelligence on cloud computing: evolution, vision, trends and open challenges. Internet Things 8:100118Guan X, Choi BY, Song S (2015) Energy efficient virtual network embedding for green dcs using dc topology and future migration. Comput Commun 69:50–59Guan X, Choi BY, Song S (2014) Topology and migration-aware energy efficient virtual network embedding for green dcs. In: 23rd International conference on computer communication and networks (ICCCN). IEEE, pp. 1–8Guerrero C, Lera I, Juiz C (2018) Genetic algorithm for multi-objective optimization of container allocation in cloud architecture. J Grid Comput 16(1):113–135Jiang J, Lu J, Zhang G, Long G (2013) Optimal cloud resource auto-scaling for web applications. In: International symposium on cluster, cloud and grid computing (CCGrid), pp. 58–65Kaewkasi C, Chuenmuneewong K (2017) Improvement of container scheduling for docker using ant colony optimization. In: 9th International conference on knowledge and smart technology (KST), pp. 254–259López-Torres S, López-Torres H, Rocha-Rocha J, Butt SA, Tariq MI, Collazos-Morales C, Piñeres-Espitia G (2019) IoT monitoring of water consumption for irrigation systems using SEMMA methodology. In: International conference on intelligent human computer interaction (pp. 222–234). Springer, ChamOnyema EM (2019) Integration of emerging technologies in teaching and learning process in Nigeria: the challenges. Central Asian J Math Theory Comput Sci 1(1):35–39Rimal Y, Pandit P, Gocchait S, Butt SA, Obaid AJ (1804) (2021) Hyperparameter determines the best learning curve on single, multi-layer and deep neural network of student grade prediction of Pokhara University Nepal. J Phys Conf Ser 1:012054Sachin D, Chinmay C, Jaroslav F, Rashmi G, Arun KR, Subhendu KP (2021) SSII: Secured and high-quality Steganography using Intelligent hybrid optimization algorithms for IoT. IEEE Access 9:1–16. https://doi.org/10.1109/ACCESS.2021.3089357Shaheen Q, Shiraz M, Hashmi MU, Mahmood D, Akhtar R (2020) A lightweight location-aware fog framework (LAFF) for QoS in internet of things paradigm. Mobile Inf Syst. https://doi.org/10.1155/2020/8871976Zheng Y, Cai L, Huang S, WangZ (2014) VM scheduling strategies based on artificial intelligence in Cloud Testing. In: 15th IEEE/ACIS international conference on software engineering, artificial intelligence, networking and parallel/distributed computing (SNPD) (pp. 1–7). IEEEPublicationORIGINALARTIFICIAL INTELLIGENCE-BASED KUBERNETES CONTAINER FOR SCHEDULING NODES OF ENERGY COMPOSITION.pdfARTIFICIAL INTELLIGENCE-BASED KUBERNETES CONTAINER FOR SCHEDULING NODES OF ENERGY COMPOSITION.pdfapplication/pdf55815https://repositorio.cuc.edu.co/bitstreams/a74c106f-bbbc-43d6-b079-7701325ad562/downloadf12cc6ae60518835fbab7fd7cf80913fMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/a303f0fa-ee6f-483b-be7c-cf2ba583d35e/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/e1e601ef-c521-4b93-b66d-1f31176b83f7/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILARTIFICIAL INTELLIGENCE-BASED KUBERNETES CONTAINER FOR SCHEDULING NODES OF ENERGY COMPOSITION.pdf.jpgARTIFICIAL INTELLIGENCE-BASED KUBERNETES CONTAINER FOR SCHEDULING NODES OF ENERGY COMPOSITION.pdf.jpgimage/jpeg58959https://repositorio.cuc.edu.co/bitstreams/454671be-636f-44cc-886b-92cdf8e26ac4/downloadd13f38f9d050864d6ca3220d2e91bd92MD54TEXTARTIFICIAL INTELLIGENCE-BASED KUBERNETES CONTAINER FOR SCHEDULING NODES OF ENERGY COMPOSITION.pdf.txtARTIFICIAL INTELLIGENCE-BASED KUBERNETES CONTAINER FOR SCHEDULING NODES OF ENERGY COMPOSITION.pdf.txttext/plain1806https://repositorio.cuc.edu.co/bitstreams/d3de6023-b9d0-4b46-87be-c78bfafb1a47/downloada9e03f9aec5cfd8cf8dd257d9fc55ce1MD5511323/8586oai:repositorio.cuc.edu.co:11323/85862024-09-17 11:00:05.347http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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 |