Littérature scientifique sur le sujet « Auto-Scaling policies »
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Articles de revues sur le sujet "Auto-Scaling policies"
Vemasani, Preetham, Sai Mahesh Vuppalapati, Suraj Modi et Sivakumar Ponnusamy. « Achieving Agility through Auto-Scaling : Strategies for Dynamic Resource Allocation in Cloud Computing ». International Journal for Research in Applied Science and Engineering Technology 12, no 4 (30 avril 2024) : 3169–77. http://dx.doi.org/10.22214/ijraset.2024.60566.
Texte intégralGuo, Yuan Yuan, Jing Li, Xin Chun Liu et Wei Wei Wang. « Batch Job Based Auto-Scaling System on Cloud Computing Platform ». Advanced Materials Research 756-759 (septembre 2013) : 2386–90. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2386.
Texte intégralEvangelidis, Alexandros, David Parker et Rami Bahsoon. « Performance modelling and verification of cloud-based auto-scaling policies ». Future Generation Computer Systems 87 (octobre 2018) : 629–38. http://dx.doi.org/10.1016/j.future.2017.12.047.
Texte intégralRajput, R. S., Dinesh Goyal, Rashid Hussain et Pratham Singh. « Provisioning of Virtual Machines in the Context of an Auto-Scaling Cloud Computing Environment ». Journal of Computational and Theoretical Nanoscience 17, no 6 (1 juin 2020) : 2430–34. http://dx.doi.org/10.1166/jctn.2020.8912.
Texte intégralWei, Yi, Daniel Kudenko, Shijun Liu, Li Pan, Lei Wu et Xiangxu Meng. « A Reinforcement Learning Based Auto-Scaling Approach for SaaS Providers in Dynamic Cloud Environment ». Mathematical Problems in Engineering 2019 (3 février 2019) : 1–11. http://dx.doi.org/10.1155/2019/5080647.
Texte intégralBhattacharjee, Brijit, Bikash Debnath, Jadav Chandra Das, Subhashis Kar, Nandan Banerjee, Saurav Mallik, Hong Qin et Debashis De. « Predicting the Future Appearances of Lost Children for Information Forensics with Adaptive Discriminator-Based FLM GAN ». Mathematics 11, no 6 (10 mars 2023) : 1345. http://dx.doi.org/10.3390/math11061345.
Texte intégralBhargavi, K., et B. Sathish Babu. « Uncertainty Aware Resource Provisioning Framework for Cloud Using Expected 3-SARSA Learning Agent : NSS and FNSS Based Approach ». Cybernetics and Information Technologies 19, no 3 (1 septembre 2019) : 94–117. http://dx.doi.org/10.2478/cait-2019-0028.
Texte intégralRusso Russo, Gabriele, Valeria Cardellini et Francesco Lo Presti. « Hierarchical Auto-Scaling Policies for Data Stream Processing on Heterogeneous Resources ». ACM Transactions on Autonomous and Adaptive Systems, 16 mai 2023. http://dx.doi.org/10.1145/3597435.
Texte intégralTournaire, Thomas, Hind Castel-Taleb et Emmanuel Hyon. « Efficient Computation of Optimal Thresholds in Cloud Auto-Scaling Systems ». ACM Transactions on Modeling and Performance Evaluation of Computing Systems, 6 juin 2023. http://dx.doi.org/10.1145/3603532.
Texte intégralThèses sur le sujet "Auto-Scaling policies"
Adolfsson, Henrik. « Comparison of Auto-Scaling Policies Using Docker Swarm ». Thesis, Linköpings universitet, Databas och informationsteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-154160.
Texte intégralTournaire, Thomas. « Model-based reinforcement learning for dynamic resource allocation in cloud environments ». Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS004.
Texte intégralThe emergence of new technologies (Internet of Things, smart cities, autonomous vehicles, health, industrial automation, ...) requires efficient resource allocation to satisfy the demand. These new offers are compatible with new 5G network infrastructure since it can provide low latency and reliability. However, these new needs require high computational power to fulfill the demand, implying more energy consumption in particular in cloud infrastructures and more particularly in data centers. Therefore, it is critical to find new solutions that can satisfy these needs still reducing the power usage of resources in cloud environments. In this thesis we propose and compare new AI solutions (Reinforcement Learning) to orchestrate virtual resources in virtual network environments such that performances are guaranteed and operational costs are minimised. We consider queuing systems as a model for clouds IaaS infrastructures and bring learning methodologies to efficiently allocate the right number of resources for the users.Our objective is to minimise a cost function considering performance costs and operational costs. We go through different types of reinforcement learning algorithms (from model-free to relational model-based) to learn the best policy. Reinforcement learning is concerned with how a software agent ought to take actions in an environment to maximise some cumulative reward. We first develop queuing model of a cloud system with one physical node hosting several virtual resources. On this first part we assume the agent perfectly knows the model (dynamics of the environment and the cost function), giving him the opportunity to perform dynamic programming methods for optimal policy computation. Since the model is known in this part, we also concentrate on the properties of the optimal policies, which are threshold-based and hysteresis-based rules. This allows us to integrate the structural property of the policies into MDP algorithms. After providing a concrete cloud model with exponential arrivals with real intensities and energy data for cloud provider, we compare in this first approach efficiency and time computation of MDP algorithms against heuristics built on top of the queuing Markov Chain stationary distributions.In a second part we consider that the agent does not have access to the model of the environment and concentrate our work with reinforcement learning techniques, especially model-based reinforcement learning. We first develop model-based reinforcement learning methods where the agent can re-use its experience replay to update its value function. We also consider MDP online techniques where the autonomous agent approximates environment model to perform dynamic programming. This part is evaluated in a larger network environment with two physical nodes in tandem and we assess convergence time and accuracy of different reinforcement learning methods, mainly model-based techniques versus the state-of-the-art model-free methods (e.g. Q-Learning).The last part focuses on model-based reinforcement learning techniques with relational structure between environment variables. As these tandem networks have structural properties due to their infrastructure shape, we investigate factored and causal approaches built-in reinforcement learning methods to integrate this information. We provide the autonomous agent with a relational knowledge of the environment where it can understand how variables are related to each other. The main goal is to accelerate convergence by: first having a more compact representation with factorisation where we devise a factored MDP online algorithm that we evaluate and compare with model-free and model-based reinforcement learning algorithms; second integrating causal and counterfactual reasoning that can tackle environments with partial observations and unobserved confounders
Chapitres de livres sur le sujet "Auto-Scaling policies"
Kumari, Anisha, et Bibhudatta Sahoo. « Serverless Architecture for Healthcare Management Systems ». Dans Advances in Healthcare Information Systems and Administration, 203–27. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-4580-8.ch011.
Texte intégralActes de conférences sur le sujet "Auto-Scaling policies"
Evangelidis, Alexandros, David Parker et Rami Bahsoon. « Performance Modelling and Verification of Cloud-Based Auto-Scaling Policies ». Dans 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). IEEE, 2017. http://dx.doi.org/10.1109/ccgrid.2017.39.
Texte intégralGandhi, Anshul, Mor Harchol-Balter, Ram Raghunathan et Michael A. Kozuch. « Distributed, Robust Auto-Scaling Policies for Power Management in Compute Intensive Server Farms ». Dans 2011 6th Open Cirrus Summit (OCS). IEEE, 2011. http://dx.doi.org/10.1109/ocs.2011.6.
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