Auswahl der wissenschaftlichen Literatur zum Thema „Auto-Scaling policies“
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Zeitschriftenartikel zum Thema "Auto-Scaling policies"
Vemasani, Preetham, Sai Mahesh Vuppalapati, Suraj Modi und 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, Nr. 4 (30.04.2024): 3169–77. http://dx.doi.org/10.22214/ijraset.2024.60566.
Der volle Inhalt der QuelleGuo, Yuan Yuan, Jing Li, Xin Chun Liu und Wei Wei Wang. „Batch Job Based Auto-Scaling System on Cloud Computing Platform“. Advanced Materials Research 756-759 (September 2013): 2386–90. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2386.
Der volle Inhalt der QuelleEvangelidis, Alexandros, David Parker und Rami Bahsoon. „Performance modelling and verification of cloud-based auto-scaling policies“. Future Generation Computer Systems 87 (Oktober 2018): 629–38. http://dx.doi.org/10.1016/j.future.2017.12.047.
Der volle Inhalt der QuelleRajput, R. S., Dinesh Goyal, Rashid Hussain und Pratham Singh. „Provisioning of Virtual Machines in the Context of an Auto-Scaling Cloud Computing Environment“. Journal of Computational and Theoretical Nanoscience 17, Nr. 6 (01.06.2020): 2430–34. http://dx.doi.org/10.1166/jctn.2020.8912.
Der volle Inhalt der QuelleWei, Yi, Daniel Kudenko, Shijun Liu, Li Pan, Lei Wu und Xiangxu Meng. „A Reinforcement Learning Based Auto-Scaling Approach for SaaS Providers in Dynamic Cloud Environment“. Mathematical Problems in Engineering 2019 (03.02.2019): 1–11. http://dx.doi.org/10.1155/2019/5080647.
Der volle Inhalt der QuelleBhattacharjee, Brijit, Bikash Debnath, Jadav Chandra Das, Subhashis Kar, Nandan Banerjee, Saurav Mallik, Hong Qin und Debashis De. „Predicting the Future Appearances of Lost Children for Information Forensics with Adaptive Discriminator-Based FLM GAN“. Mathematics 11, Nr. 6 (10.03.2023): 1345. http://dx.doi.org/10.3390/math11061345.
Der volle Inhalt der QuelleBhargavi, K., und 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, Nr. 3 (01.09.2019): 94–117. http://dx.doi.org/10.2478/cait-2019-0028.
Der volle Inhalt der QuelleRusso Russo, Gabriele, Valeria Cardellini und Francesco Lo Presti. „Hierarchical Auto-Scaling Policies for Data Stream Processing on Heterogeneous Resources“. ACM Transactions on Autonomous and Adaptive Systems, 16.05.2023. http://dx.doi.org/10.1145/3597435.
Der volle Inhalt der QuelleTournaire, Thomas, Hind Castel-Taleb und Emmanuel Hyon. „Efficient Computation of Optimal Thresholds in Cloud Auto-Scaling Systems“. ACM Transactions on Modeling and Performance Evaluation of Computing Systems, 06.06.2023. http://dx.doi.org/10.1145/3603532.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleTournaire, 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.
Der volle Inhalt der QuelleThe 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
Buchteile zum Thema "Auto-Scaling policies"
Kumari, Anisha, und Bibhudatta Sahoo. „Serverless Architecture for Healthcare Management Systems“. In Advances in Healthcare Information Systems and Administration, 203–27. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-4580-8.ch011.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Auto-Scaling policies"
Evangelidis, Alexandros, David Parker und Rami Bahsoon. „Performance Modelling and Verification of Cloud-Based Auto-Scaling Policies“. In 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). IEEE, 2017. http://dx.doi.org/10.1109/ccgrid.2017.39.
Der volle Inhalt der QuelleGandhi, Anshul, Mor Harchol-Balter, Ram Raghunathan und Michael A. Kozuch. „Distributed, Robust Auto-Scaling Policies for Power Management in Compute Intensive Server Farms“. In 2011 6th Open Cirrus Summit (OCS). IEEE, 2011. http://dx.doi.org/10.1109/ocs.2011.6.
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