Auto-scaling containerized cloud applications: A workload-driven approach

[1]  Aiman Erbad,et al.  B5G: Predictive Container Auto-Scaling for Cellular Evolved Packet Core , 2021, 2021 IEEE International Conference on Communications Workshops (ICC Workshops).

[2]  Cristina Boeres,et al.  Managing Vertical Memory Elasticity in Containers , 2020, 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC).

[3]  T. Ahmed,et al.  Proactive Autoscaling for Cloud-Native Applications using Machine Learning , 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference.

[4]  Stelios Sotiriadis,et al.  Real-Time Anomaly Detection of NoSQL Systems Based on Resource Usage Monitoring , 2020, IEEE Transactions on Industrial Informatics.

[5]  Niklas Kühl,et al.  AI-based Resource Allocation: Reinforcement Learning for Adaptive Auto-scaling in Serverless Environments , 2020, 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid).

[6]  I. Ahmad,et al.  Machine learning-based auto-scaling for containerized applications , 2019, Neural Computing and Applications.

[7]  Rajkumar Buyya,et al.  Elastic Load Balancing for Dynamic Virtual Machine Reconfiguration Based on Vertical and Horizontal Scaling , 2019, IEEE Transactions on Services Computing.

[8]  Behrouz H. Far,et al.  Dynamic Cloud Resource Allocation Considering Demand Uncertainty , 2019, IEEE Transactions on Cloud Computing.

[9]  Helen D. Karatza,et al.  Performance evaluation of a SaaS cloud under different levels of workload computational demand variability and tardiness bounds , 2019, Simul. Model. Pract. Theory.

[10]  Waheed Iqbal,et al.  Dynamic workload patterns prediction for proactive auto-scaling of web applications , 2018, J. Netw. Comput. Appl..

[11]  Shay Horovitz,et al.  Efficient Cloud Auto-Scaling with SLA Objective Using Q-Learning , 2018, 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud).

[12]  Steffen Becker,et al.  CAUS: An Elasticity Controller for a Containerized Microservice , 2018, ICPE Companion.

[13]  Samee Ullah Khan,et al.  Quantifying Cloud Elasticity with Container-Based Autoscaling , 2017, 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech).

[14]  Philippe Merle,et al.  Autonomic Vertical Elasticity of Docker Containers with ELASTICDOCKER , 2017, 2017 IEEE 10th International Conference on Cloud Computing (CLOUD).

[15]  Lucas Chaufournier,et al.  Containers and Virtual Machines at Scale: A Comparative Study , 2016, Middleware.

[16]  Songyun Wang,et al.  Auto scaling virtual machines for web applications with queueing theory , 2016, 2016 3rd International Conference on Systems and Informatics (ICSAI).

[17]  Philippe Merle,et al.  Model-Driven Management of Docker Containers , 2016, 2016 IEEE 9th International Conference on Cloud Computing (CLOUD).

[18]  Parijat Dube,et al.  Autoscaling for Hadoop Clusters , 2016, 2016 IEEE International Conference on Cloud Engineering (IC2E).

[19]  Evgenia Smirni,et al.  PRACTISE: Robust prediction of data center time series , 2015, 2015 11th International Conference on Network and Service Management (CNSM).

[20]  José Antonio Lozano,et al.  A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments , 2014, Journal of Grid Computing.

[21]  Dirk Merkel,et al.  Docker: lightweight Linux containers for consistent development and deployment , 2014 .

[22]  Jörg Domaschka,et al.  Beyond IaaS and PaaS: An Extended Cloud Taxonomy for Computation, Storage and Networking , 2013, 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing.

[23]  Rajkumar Buyya,et al.  Revenue Maximization Using Adaptive Resource Provisioning in Cloud Computing Environments , 2012, 2012 ACM/IEEE 13th International Conference on Grid Computing.

[24]  Johan Tordsson,et al.  Efficient provisioning of bursty scientific workloads on the cloud using adaptive elasticity control , 2012, ScienceCloud '12.

[25]  Moustafa Ghanem,et al.  Lightweight Resource Scaling for Cloud Applications , 2012, 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012).

[26]  Alexander Clemm,et al.  Integrated and autonomic cloud resource scaling , 2012, 2012 IEEE Network Operations and Management Symposium.

[27]  Aniruddha S. Gokhale,et al.  Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[28]  Pierre Gançarski,et al.  A global averaging method for dynamic time warping, with applications to clustering , 2011, Pattern Recognit..

[29]  Zhenhuan Gong,et al.  PRESS: PRedictive Elastic ReSource Scaling for cloud systems , 2010, 2010 International Conference on Network and Service Management.

[30]  Jeffrey S. Chase,et al.  Automated control for elastic storage , 2010, ICAC '10.

[31]  Le Yi Wang,et al.  VCONF: a reinforcement learning approach to virtual machines auto-configuration , 2009, ICAC '09.

[32]  Kang G. Shin,et al.  Automated control of multiple virtualized resources , 2009, EuroSys '09.

[33]  Rajarshi Das,et al.  A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation , 2006, 2006 IEEE International Conference on Autonomic Computing.

[34]  Donald J. Berndt,et al.  Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.

[35]  Stelios Sotiriadis,et al.  Detecting Performance Degradation in Cloud Systems Using LSTM Autoencoders , 2021, AINA.

[36]  Mehrdad Ashtiani,et al.  Proactive auto-scaling for cloud environments using temporal convolutional neural networks , 2021, J. Parallel Distributed Comput..

[37]  Chuanqi Kan,et al.  DoCloud: An elastic cloud platform for Web applications based on Docker , 2016, 2016 18th International Conference on Advanced Communication Technology (ICACT).