An Auto-Scaling Framework for Containerized Elastic Applications

Control theory has proven to be a practical approach for the design and implementation of controllers, which does not inherit the problems of non-control theoretic controllers due to its strong mathematical background. State-of-the-art auto-scaling controllers suffer from one or more of the following limitations: (1) lack of a reliable performance model, (2) using a performance model with low scalability, tractability, or fidelity, (3) being application- or architecture-specific leading to low extendability, and (4) no guarantee on their efficiency. Consequently, in this article, we strive to mitigate these problems by leveraging an adaptive controller, which is composed of a neural network as the performance model and a Proportional-Integral-Derivative (PID) controller as the scaling engine. More specifically, we design, implement, and analyze different flavours of these adaptive and non-adaptive controllers, and we compare and contrast them against each other to find the most suitable one for managing containerized cloud software systems at runtime. The controller’s objective is to maintain the response time of the controlled software system in a pre-defined range, and meeting the Service-level Agreements, while leading to efficient resource provisioning.

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Efficient Cloud Auto-Scaling with SLA Objective Using Q-Learning

2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud) ◽

10.1109/ficloud.2018.00020 ◽

2018 ◽

Cited By ~ 8

Author(s):

Shay Horovitz ◽

Yair Arian

Keyword(s):

Q Learning ◽

Auto Scaling

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Adaptive Resource Provisioning and Auto-scaling for Cloud Native Software

Communications in Computer and Information Science - Information and Software Technologies ◽

10.1007/978-3-319-99972-2_9 ◽

2018 ◽

pp. 113-129

Author(s):

Olesia Pozdniakova ◽

Dalius Mažeika ◽

Aurimas Cholomskis

Keyword(s):

Resource Provisioning ◽

Adaptive Resource Provisioning ◽

Auto Scaling ◽

Adaptive Resource

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Efficiency of Linear and Non-Linear Classifiers for Gas Identification from Electrocatalytic Gas Sensor

Metrology and Measurement Systems ◽

10.2478/mms-2013-0043 ◽

2013 ◽

Vol 20 (3) ◽

pp. 501-512 ◽

Cited By ~ 14

Author(s):

Paweł Kalinowski ◽

Łukasz Woźniak ◽

Anna Strzelczyk ◽

Piotr Jasinski ◽

Grzegorz Jasinski

Keyword(s):

Gas Sensor ◽

Gas Sensors ◽

Support Vector ◽

Voltammetric Sensor ◽

Linear Classifiers ◽

Lack Of Information ◽

The Family ◽

Gas Identification ◽

Auto Scaling ◽

Nonlinear Support

Abstract Electrocatalytic gas sensors belong to the family of electrochemical solid state sensors. Their responses are acquired in the form of I-V plots as a result of application of cyclic voltammetry technique. In order to obtain information about the type of measured gas the multivariate data analysis and pattern classification techniques can be employed. However, there is a lack of information in literature about application of such techniques in case of standalone chemical sensors which are able to recognize more than one volatile compound. In this article we present the results of application of these techniques to the determination from a single electrocatalytic gas sensor of single concentrations of nitrogen dioxide, ammonia, sulfur dioxide and hydrogen sulfide. Two types of classifiers were evaluated, i.e. linear Partial Least Squares Discriminant Analysis (PLS-DA) and nonlinear Support Vector Machine (SVM). The efficiency of using PLS-DA and SVM methods are shown on both the raw voltammetric sensor responses and pre-processed responses using normalization and auto-scaling

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