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A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling

Hamid Arabnejad, Claus Pahl, Pooyan Jamshidi, Giovani Estrada
2017 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)  
A self-adaptive fuzzy logic controller is combined with two reinforcement learning (RL) approaches: (i) Fuzzy SARSA learning (FSL) and (ii) Fuzzy Q-learning (FQL).  ...  A goal of cloud service management is to design self-adaptable auto-scaler to react to workload fluctuations and changing the resources assigned.  ...  [6] investigate horizontal auto-scaling using threshold-based and reinforcement learning techniques.  ... 
doi:10.1109/ccgrid.2017.15 dblp:conf/ccgrid/ArabnejadPJE17 fatcat:nh2rs5xknncxllwbtxh2xkge5q

Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Architectures

Pooyan Jamshidi, Amir Sharifloo, Claus Pahl, Hamid Arabnejad, Andreas Metzger, Giovani Estrada
2016 2016 12th International ACM SIGSOFT Conference on Quality of Software Architectures (QoSA)  
The experimental results demonstrate that FQL4KE outperforms both a fuzzy controller without learning and the native Azure auto-scaling.  ...  We introduce FQL4KE, a self-learning fuzzy controller that learns and modifies fuzzy rules at runtime.  ...  Fuzzy Q-Learning Until this stage, we have shown how to design a fuzzy controller for auto-scaling a cloud-based application where the elasticity policies are provided by users at design-time, like RobusT2Scale  ... 
doi:10.1109/qosa.2016.13 dblp:conf/qosa/JamshidiSPAME16 fatcat:uj5w3r7pfrcmvovk57kbccuczm

Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution [article]

Pooyan Jamshidi, Amir Sharifloo, Claus Pahl, Andreas Metzger, Giovani Estrada
2015 arXiv   pre-print
The experimental results indicate that FQL4KE outperforms our previously developed fuzzy controller without learning mechanisms and the native Azure auto-scaling.  ...  In this paper, we propose learning adaptation rules during runtime. To this end, we introduce FQL4KE, a self-learning fuzzy cloud controller.  ...  ACKNOWLEDGMENT The authors would like to thank Soodeh Farokhi and Saeid Masoumzadeh for their constructive comments on the final draft of the paper.  ... 
arXiv:1507.00567v1 fatcat:uyci4bfkwbbuzgypzzi63pqajy

Uncertainty Aware Resource Provisioning Framework for Cloud Using Expected 3-SARSA Learning Agent: NSS and FNSS Based Approach

K. Bhargavi, B. Sathish Babu
2019 Cybernetics and Information Technologies  
The performance of the proposed work compared to the existing fuzzy auto scaling work achieves the throughput of 80% with the learning rate of 75% on homogeneous and heterogeneous workloads by considering  ...  Efficiently provisioning the resources in a large computing domain like cloud is challenging due to uncertainty in resource demands and computation ability of the cloud resources.  ...  A comparison of fuzzy SARSA and fuzzy Q-Learning towards auto-scaling of resources in the cloud environment is given in [24] .  ... 
doi:10.2478/cait-2019-0028 fatcat:xnkaaf32iffofcyfjo7bw24b3u

Auto-scaling Web Applications in Clouds: A Taxonomy and Survey [article]

Chenhao Qu, Rodrigo N. Calheiros, Rajkumar Buyya
2017 arXiv   pre-print
In this paper, we comprehensively analyze the challenges that remain in auto-scaling web applications in clouds and review the developments in this field.  ...  We present a taxonomy of auto-scalers according to the identified challenges and key properties. We analyze the surveyed works and map them to the taxonomy to identify the weaknesses in this field.  ...  Yaser Mansouri, Xunyun Liu, Minxian Xu, and Bowen Zhou for their valuable comments and suggestions in improving the quality of the paper.  ... 
arXiv:1609.09224v6 fatcat:dkk2ftpvpbcnvhcmc6lz2omwa4

A Self-Adaptive Resource Provisioning Approach using Fuzzy Logic for Cloud-Based Applications

Muhammad Azeem Akbar, Tooba Tehreem, Shaukat Hayat, Nasrullah, Muhammad Mateen
2020 International Journal of Computing and Digital Systems  
Based on fuzzy logic, cloud applications having self-learning provisioning resources outperformed the hybrid resource provisioning approach.  ...  Additionally, the proposed fuzzy logic approach enhanced the performance of planning phase for better decision making.  ...  ACKNOWLEDGMENT The authors declare no conflict of interest.  ... 
doi:10.12785/ijcds/090301 fatcat:ly2sd6rbkbewvljg5eeypek5bm

Adaptive Service Performance Control using Cooperative Fuzzy Reinforcement Learning in Virtualized Environments

Olumuyiwa Ibidunmoye, Mahshid Helali Moghadam, Ewnetu Bayuh Lakew, Erik Elmroth
2017 Proceedings of the10th International Conference on Utility and Cloud Computing - UCC '17  
To this end, we extend the standard reinforcement learning approach in two ways: a) we formulate the system state as a fuzzy space and b) exploit a set of cooperative agents to explore multiple fuzzy states  ...  A popular approach is fine-grained resource provisioning via auto-scaling mechanisms that rely on either threshold-based adaptation rules or sophisticated queuing/control-theoretic models.  ...  The closest technique to the proposed mechanism is Auto-scale [24] , an RL-based technique for adaptive QoS control via vertical scaling using Neuro-Fuzzy function approximations.  ... 
doi:10.1145/3147213.3147225 dblp:conf/ucc/IbidunmoyeMLE17 fatcat:jgrqm645cvgwncl3bnx6glztpi

Autonomic resource provisioning for cloud-based software

Pooyan Jamshidi, Aakash Ahmad, Claus Pahl
2014 Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems - SEAMS 2014  
The state-of-thepractice with respect to auto-scaling involves specifying thresholdbased rules to implement elasticity policies for cloud-based applications.  ...  This paper exploits fuzzy logic to enable qualitative specification of elasticity rules for cloud-based software.  ...  Reinforcement learning (e.g., [42] ) enable learning elasticity policies from observations. However, it requires long learning, which is only applicable for stable workloads.  ... 
doi:10.1145/2593929.2593940 dblp:conf/icse/JamshidiAP14 fatcat:p4vpr3ahrvbnrdklfkirjhucmq

Auto-scaling techniques for elastic data stream processing

Thomas Heinze, Valerio Pappalardo, Zbigniew Jerzak, Christof Fetzer
2014 Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems - DEBS '14  
In this paper we investigate the application of different auto-scaling techniques for solving this problem.  ...  , and (3) we perform evaluation of the selected auto scaling techniques using the real world data.  ...  REQUIREMENTS FOR AN AUTO-SCALING TECHNIQUE For selecting possible approaches from the set of available auto-scaling techniques [11] we propose a set of requirements for an auto-scaling technique in an  ... 
doi:10.1145/2611286.2611314 dblp:conf/debs/HeinzePJF14 fatcat:plgqlm2wore6hkh34a45ehffiq

Auto-scaling techniques for elastic data stream processing

Thomas Heinze, Valerio Pappalardo, Zbigniew Jerzak, Christof Fetzer
2014 2014 IEEE 30th International Conference on Data Engineering Workshops  
In this paper we investigate the application of different auto-scaling techniques for solving this problem.  ...  , and (3) we perform evaluation of the selected auto scaling techniques using the real world data.  ...  REQUIREMENTS FOR AN AUTO-SCALING TECHNIQUE For selecting possible approaches from the set of available auto-scaling techniques [11] we propose a set of requirements for an auto-scaling technique in an  ... 
doi:10.1109/icdew.2014.6818344 dblp:conf/icde/HeinzePJF14 fatcat:zy7pzq4rbjg5fjqvsbhyuokqda

Intelligent Autoscaling of Microservices in the Cloud for Real-time Applications

Abeer Abdel Khaleq, Ilkyeun Ra
2021 IEEE Access  
Advancements in machine learning and reinforcement learning (RL) provides a means for autoscaling in cloud applications with no domain knowledge.  ...  What adds to the challenge is that cloud applications impose quality of service (QoS) requirements and have various resource demands requiring a customized scaling approach.  ...  Our research builds on this where we use microservices log data along with machine learning and reinforcement techniques to auto scale the cloud microservices while abiding with QoS constraints.  ... 
doi:10.1109/access.2021.3061890 fatcat:awgamwbrffdhbchq5xuca7shja

Exterminating Computational Limits of Machine Learning with Merits of Serverless

Harpreet Kaur, Prabhpreet Kaur
2018 International Journal of Engineering Research and  
A serverless compute may give an optimal solution to the computational resources provided by the cloud.  ...  The trend of determining patterns using the various machine-learning, data mining, deep-learning or neural networks are limited due to the computational power of machines.  ...  Infra: SLO Aware Elastic Auto Scaling in the Cloud for Cost Reduction Sidhanta and Mukhopadhyay[2016] presented SLO (Service Level Objective) Aware Elastic Auto Scaling in the cloud for cost reduction.  ... 
doi:10.17577/ijertv7is010147 fatcat:xsbi5flvkjgtjnxctkspzggxhu

Research on Auto-Scaling of Web Applications in Cloud: Survey, Trends and Future Directions

Parminder Singh, Pooja Gupta, Kiran Jyoti, Anand Nayyar
2019 Scalable Computing : Practice and Experience  
In this article, we presented the literature survey for auto-scaling techniques of web applications in cloud computing.  ...  One of the key challenges for web application in cloud computing is auto-scaling.  ...  One of the rule-based technique for an auto-scaling approach is fuzzy rules. In this technique, rules are defined using if-else conditions.  ... 
doi:10.12694/scpe.v20i2.1537 fatcat:5zdylggvtjdslichn6mpoleese

Improved Q Network Auto-Scaling in Microservice Architecture

Yeonggwang Kim, Jaehyung Park, Junchurl Yoon, Jinsul Kim
2022 Applied Sciences  
A cloud provider requires flexible resource management to meet the continually changing demands, such as auto-scaling and provisioning.  ...  Microservice architecture has emerged as a powerful paradigm for cloud computing due to its high efficiency in infrastructure management as well as its capability of largescale user service.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app12031206 fatcat:73rul7ujb5br3dmt7irftx3t54

Applying reinforcement learning towards automating resource allocation and application scalability in the cloud

Enda Barrett, Enda Howley, Jim Duggan
2012 Concurrency and Computation  
By applying a temporal difference reinforcement learning algorithm known as Q-learning, optimal scaling policies can be determined.  ...  Additionally reinforcement learning techniques typically suffer from curse of dimensionality problems, where the state space grows exponentially with each additional state variable.  ...  ACKNOWLEDGMENT The authors would like to gratefully acknowledge the continued support of Science Foundation Ireland. (2011)  ... 
doi:10.1002/cpe.2864 fatcat:tilsmxw6h5eyhedqe2y3si6pyy
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