Show simple item record

dc.creatorDiaz Martinez, Jorge Luis
dc.creatorAziz Butt, Shariq
dc.creatorMichael Onyema, Edeh
dc.creatorChakraborty, Dr. Chinmay
dc.creatorShaheen, Qaisar
dc.creatorDe-La-Hoz-Franco, Emiro
dc.creatorAriza Colpas, Paola Patricia
dc.description.abstractKubernetes 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,
dc.publisherInternational Journal of Systems Assurance Engineering and Managementspa
dc.rightsCC0 1.0 Universal*
dc.sourceInternational Journal of Systems Assurance Engineering and Managementspa
dc.subjectArtificial intelligencespa
dc.subjectAutomated schedulingspa
dc.subjectCloud infrastructurespa
dc.subjectMulti-criteria schedulerspa
dc.subjectScheduling strategyspa
dc.titleArtificial intelligence-based Kubernetes container for scheduling nodes of energy compositionspa
dcterms.referencesAhmed 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 52:154–172spa
dcterms.referencesAhmed 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 52:154–172spa
dcterms.referencesAlicherry M, Lakshman T (2013) Optimizing data access latencies in cloud systems by intelligent virtual machine placement. IEEE INFOCOM. pp. 647–655spa
dcterms.referencesAmit 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.
dcterms.referencesAmit S, Lalit G, Chinmay C (2021) Improvement of system performance in an IT production support environment. Int J Syst Assur Eng Manag.
dcterms.referencesArdagna D, Panicucci B, Trubian M, Zhang L (2012) Energy-aware autonomic resource allocation in multitier virtualized environments. IEEE Trans Serv Comput 5(1):2–19spa
dcterms.referencesAriza-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):2374spa
dcterms.referencesAroca JA, Anta AF, Mosteiro MA, Thraves C, Wang L (2016) Power-efficient assignment of virtual machines to physical machines. Future Generat Comput Syst 54:82–94spa
dcterms.referencesBaccarelli E, Amendola D, Cordeschi N (2015) Minimum-energy bandwidth management for qos live migration of virtual machines. Comput Network 93:1–22spa
dcterms.referencesBeloglazov 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):755–768spa
dcterms.referencesChen X, Li C, Jiang Y (2015) Optimization model and algorithm for energy efficient virtual node embedding. IEEE Commun Lett 19(8):1327–1330spa
dcterms.referencesChinmay C, Roy R, Pathak S, Chakrabarti S (2011) An optimal probabilistic traffic engineering scheme for heterogeneous networks. CIIT Int J Fuzzy Syst 3(2):35–39spa
dcterms.referencesChinmay C, Roy R (2012) Markov decision process based optimal gateway selection algorithm. Int J Syst Algorithms Appl 48–52spa
dcterms.referencesChowdhury N, Rahman M, Boutaba R (2009) Virtual network embedding with coordinated node and link mapping. In: IEEE INFOCOM, pp. 783–791spa
dcterms.referencesCordeschi N, Patriarca T, Baccarelli E (2012) Stochastic traffic engineering for realtime applications over wireless networks. J Netw Comput Appl 35(2):681–694spa
dcterms.referencesDean J, Ghemawat S (2008) Mapreduce: simplified data processing on large clusters. Commun ACM 51(1):107–113spa
dcterms.referencesDłaz 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 67:99–117spa
dcterms.referencesElhady 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). IEEEspa
dcterms.referencesFazio 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):81–88spa
dcterms.referencesFelter 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. 171–172spa
dcterms.referencesGill 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 8:100118spa
dcterms.referencesGuan X, Choi BY, Song S (2015) Energy efficient virtual network embedding for green dcs using dc topology and future migration. Comput Commun 69:50–59spa
dcterms.referencesGuan 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. 1–8spa
dcterms.referencesGuerrero C, Lera I, Juiz C (2018) Genetic algorithm for multi-objective optimization of container allocation in cloud architecture. J Grid Comput 16(1):113–135spa
dcterms.referencesJiang 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. 58–65spa
dcterms.referencesKaewkasi 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. 254–259spa
dcterms.referencesLópez-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, Chamspa
dcterms.referencesOnyema EM (2019) Integration of emerging technologies in teaching and learning process in Nigeria: the challenges. Central Asian J Math Theory Comput Sci 1(1):35–39spa
dcterms.referencesRimal 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 1:012054spa
dcterms.referencesSachin 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.
dcterms.referencesShaheen 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.
dcterms.referencesZheng 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). IEEEspa

Files in this item


This item appears in the following Collection(s)

Show simple item record

CC0 1.0 Universal
Except where otherwise noted, this item's license is described as CC0 1.0 Universal