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Stateless System Performance Prediction and Health Assessment in Cloud Environments: Introducing cSysGuard, an Ensemble Modeling Approach
(2024-01-01)
Chairatana, Nutt
;
Chawuthai, Rathachai
Stateless cloud computing presents remarkable scalability and cost-effectiveness by offering dynamically adjustable resources tailored to fluctuating demands, eliminating the constraints of stateful architectures. However, the challenges presented by dynamic workload are substantial in the context of system health monitoring, frequently leading to service interruptions owing to insufficient resources. It underscores the need for the development of more efficient monitoring systems. Our study introduces cSysGuard, a novel framework designed to enhance monitoring capabilities within cloud environments. The methodology employs an ensemble regression model with a stacking strategy to forecast dynamic performance metrics. The algorithm also leverages a classification model to assess the system's health based on forecasted metrics, effectively identifying potential failures in the future. Under the configuration utilized, our evaluations demonstrated increased predictive performance with cSysGuard in forecasting various metrics compared to traditional models. The results showed an improvement of up to a remarkable 2.28-fold increase, varying significantly based on the specific metric under consideration. In addition, the effectiveness of health assessment was achieved through Decision Trees with hyperparameter tuning, resulting in a macro-averaged F1 score of 89.79%. This research contributes to both the theoretical and practical aspects of server monitoring, presenting a solution that assesses system performance metrics and health to tackle dynamic challenges in cloud infrastructure.
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Route Prediction from GPS Trajectory and Road Data
(2023-01-01)
Chawuthai, Rathachai
;
Kawachakul, Kampanart
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Boonrod, Kittikom
;
Threepak, Thanunchai
This paper presents an approach to create a route prediction model for multiple vehicles from GPS trajectory and road data. Since the baseline model is designed for a single car and it provides low performance for our experiment, our approach using the HDBSCAN clustering for route data preprocessing and the prediction model based on Viterbi algorithm, which is an extension of the Hidden Markov Model, provides the better performance in terms of Hit@K where K being 3. The result of our work demonstrates the feasibility to improve the smart city technology under the scope of smart mobility as well. (Abstract)
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Cloud Stateless Server Failover Prediction Using Machine Learning on Proactive System Metrics
(2023-01-01)
Chairatana, Nutt
;
Chawuthai, Rathachai
Cloud computing, revered for its extraordinary scalability and elasticity, has revolutionized business operations by providing flexible resource options based on demand. However, this on-demand resource allocation poses distinct challenges. Due to the fluid nature of resource allocation and load distribution in the cloud, monitoring the health of servers using system metrics becomes problematic. This complexity can lead to unexpected server request failures and service interruptions due to resource insufficiencies, highlighting the need for more effective monitoring systems. Our research utilizes machine learning techniques to predict cloud server health based on resource usage and operational metrics, focusing specifically on stateless applications. Our study reveals that a Logistic Regression model trained on these system metrics delivers the most precise predictions. After hyperparameter tuning, the model exhibited robust performance, achieving a macro-Averaged F1 Score of 97.7%. The paper outlines our methodology, findings, and the potential of this approach for cloud server health prediction.
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Travel Time Prediction on Long-Distance Road Segments in Thailand
(2022-06-01)
Chawuthai, Rathachai
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Ainthong, Nachaphat
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Intarawart, Surasee
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Boonyanaet, Niracha
;
Sumalee, Agachai
This study proposes a method by which to predict the travel time of vehicles on long-distance road segments in Thailand. We adopted the Self-Attention Long Short-Term Memory (SA-LSTM) model with a Butterworth low-pass filter to predict the travel time on each road segment using historical data from the Global Positioning System (GPS) tracking of trucks in Thailand. As a result, our prediction method gave a Mean Absolute Error (MAE) of 12.15 min per 100 km, whereas the MAE of the baseline was 27.12 min. As we can estimate the travel time of vehicles with a lower error, our method is an effective way to shape a data-driven smart city in terms of predictive mobility.

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