Enhancing Cloud Availability via Intelligent Monitoring using Time Series Database and Machine Learning

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Production Systems ◽  
Technical Support ◽  
Machine Learning Techniques ◽  
Cloud Provider ◽  
Monitoring Tool ◽  
Intelligent Monitoring ◽  
Learning Techniques ◽  
Proactive Measures

Every cloud provider, wishes to provide 99.9999% availabil- ity for the systems provisioned and operated by them for the customer i.e. may it be SaaS or PaaS or IaaS model, the availability of the system must be greater than 99.9999%.It becomes vital for the provider to mon- itor the systems and take proactive measures to reduce the downtime.In an ideal scenario, the support colleagues (24*7 technical support) must be aware of the on-going issues in the production systems before it is raised as an incident by the customer. But currently, there is no effective alert monitoring solutions for the same. The proposed solution presented in this paper is to have a central alert monitoring tool for all cloud so- lutions offered by the cloud provider. The central alert monitoring tool constantly observes the time series database which contains metric val- ues populated by HA and compares the incoming metric values with the defined thresholds. When a metric value exceeds the defined threshold, using machine learning techniques the monitoring tool decides & takes actions.

Metaheuristic based MLP-SARIMAX HybridizationOne Hour Ahead Solar Radiation Forecasting

2021 ◽  
Author(s):  
Hugo Abreu Mendes ◽  
João Fausto Lorenzato Oliveira ◽  
Paulo Salgado Gomes Mattos Neto ◽  
Alex Coutinho Pereira ◽  
Eduardo Boudoux Jatoba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Time Series ◽  
Solar Radiation ◽  
Statistical Models ◽  
Clean Energy ◽  
Machine Learning Techniques ◽  
Exogenous Variables ◽  
Learning Techniques ◽  
Photovoltaic Plants

Within the context of clean energy generation, solar radiation forecast is applied for photovoltaic plants to increase maintainability and reliability. Statistical models of time series like ARIMA and machine learning techniques help to improve the results. Hybrid Statistical + ML are found in all sorts of time series forecasting applications. This work presents a new way to automate the SARIMAX modeling, nesting PSO and ACO optimization algorithms, differently from R's AutoARIMA, its searches optimal seasonality parameter and combination of the exogenous variables available. This work presents 2 distinct hybrid models that have MLPs as their main elements, optimizing the architecture with Genetic Algorithm. A methodology was used to obtain the results, which were compared to LSTM, CLSTM, MMFF and NARNN-ARMAX topologies found in recent works. The obtained results for the presented models is promising for use in automatic radiation forecasting systems since it outperformed the compared models on at least two metrics.

scholarly journals Forecasting of Air Passengers using ARIMA Modeling

2019 ◽  
Vol 8 (11S) ◽  
pp. 1050-1054
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Autocorrelation Function ◽  
Arima Model ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Forecasting Method ◽  
Partial Autocorrelation Function ◽  
Time Series Method ◽  
Arima Modeling

Air passengers prediction is said to be the centre of gravity of the growth. With people on the move constantly, there is bound to be some dissatisfaction amongst the customers which could be due to various reason, varying from overbooking of flights to ground operations. This dissatisfaction can be controlled till a limit, in ballpark figuring. In the past, this has been done using various machine learning techniques. For this prediction, in this project, ARIMA Modeling is used which is a time series forecasting method, based on machine learning. To test the stationarity of the data, which is done using Dickey Fuller test. If the data is stationary, it is fit into the ARIMA Model. If the data isn’t stationary, it is made stationary by differencing or by logarithmic transformation. The logarithmic method to make the data stationary. Once the data is stationary, using the Partial autocorrelation function and the autocorrelation function, values of p and q are found, which are required in the time series method. These values are then fit into the ARIMA Modeling and hence, the results are predicted. Upon the use and fitting of various models, the ARIMA(2,1,2) has been the best fit, having the least RMS and RMSE values.

Applications of Artificial Intelligence in the Realm of Business Intelligence

2021 ◽  
pp. 358-386
Author(s):  
Prakhar Mehrotra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Time Series ◽  
Natural Language Processing ◽  
Language Processing ◽  
Business Intelligence ◽  
Machine Learning Techniques ◽  
Current State ◽  
Learning Techniques ◽  
High Level

The objective of this chapter is to discuss the integration of advancements made in the field of artificial intelligence into the existing business intelligence tools. Specifically, it discusses how the business intelligence tool can integrate time series analysis, supervised and unsupervised machine learning techniques and natural language processing in it and unlock deeper insights, make predictions, and execute strategic business action from within the tool itself. This chapter also provides a high-level overview of current state of the art AI techniques and provides examples in the realm of business intelligence. The eventual goal of this chapter is to leave readers thinking about what the future of business intelligence would look like and how enterprise can benefit by integrating AI in it.

scholarly journals Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques

2019 ◽  
Author(s):  
Reed D. Gurchiek ◽  
Nicholas Cheney ◽  
Ryan S. McGinnis
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Practical Implementation ◽  
Learning Techniques

Wearable sensors have the potential to enable comprehensive patient characterization and optimized clinical intervention. Critical to realizing this vision is accurate estimation of biomechanical time-series in daily-life, including joint, segment, and muscle kinetics and kinematics, from wearable sensor data. The use of physical models for estimation of these quantities often requires many wearable devices making practical implementation more difficult. However, regression techniques may provide a viable alternative by allowing the use of a reduced number of sensors for estimating biomechanical time-series. Herein, we review 46 articles that used regression algorithms to estimate joint, segment, and muscle kinematics and kinetics. We present a high-level comparison of the many different techniques identified and discuss the implications of our findings concerning practical implementation and further improving estimation accuracy. In particular, we found that several studies report the incorporation of domain knowledge often yielded superior performance. Further, most models were trained on small datasets in which case nonparametric regression often performed best. No models were open-sourced, and most were subject-specific and not validated on impaired populations. Future research should focus on developing open-source algorithms using complementary physics-based and machine learning techniques that are validated in clinically impaired populations. This approach may further improve estimation performance and reduce barriers to clinical adoption.

scholarly journals A Holistic Quality Assurance Approach for Machine Learning Applications in Cyber-Physical Production Systems

2021 ◽  
Vol 11 (20) ◽  
pp. 9590
Author(s):  
Hajo Wiemer ◽  
Alexander Dementyev ◽  
Steffen Ihlenfeldt
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Process Model ◽  
Production Systems ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Monitoring Process ◽  
Machine Learning Applications ◽  
Cyber Physical Production Systems ◽  
Automation Technology

With the trend of increasing sensors implementation in production systems and comprehensive networking, essential preconditions are becoming required to be established for the successful application of data-driven methods of equipment monitoring, process optimization, and other relevant automation tasks. As a protocol, these tasks should be performed by engineers. Engineers usually do not have enough experience with data mining or machine learning techniques and are often skeptical about the world of artificial intelligence (AI). Quality assurance of AI results and transparency throughout the IT chain are essential for the acceptance and low-risk dissemination of AI applications in production and automation technology. This article presents a conceptual method of the stepwise and level-wise control and improvement of data quality as one of the most important sources of AI failures. The appropriate process model (V-model for quality assurance) forms the basis for this.

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