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A Real Time Optimizing Mobile Operating System: An Interactive Context: Context-Aware Intelligence for Adaptive and Real-Time Mobile Systems - Softcover
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Predicting the execution time of workflow tasks in a cloud environment is vital for optimizing resource utilization and meeting service level agreements (SLAs). In this study, we propose a Two-Stage Machine Learning Approach for Workflow Task Execution Time Prediction. In the first stage, we employ a task classifica-tion model to categorize tasks into groups based on their expected execution times (e.g., short, medium, long). The proposed two-stage approach aims to enhance prediction accuracy and interpretability by leveraging both classification and re-gression techniques. We evaluate the performance of the model using historical data, considering metrics such as accuracy, Mean Absolute Error, and Root Mean Squared Error. Finally, we discuss the deployment and monitoring of the model in a cloud environment, emphasizing its adaptability to evolving infrastructure and workload patterns. Many methods, including resource provisioning and scheduling, depend on predict-ing the workflow activities' performance for changing input data. On the other hand, producing such estimates in the cloud is challenging.
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Über die Autorin bzw. den Autor
Prof. Ganesh Misal has 6+ years of academic experience & serves as Assistant Professor, AI&DS, FTC COER, with additional responsibilities as IQAC & ERP Coordinator.Dr. Somnath Thigale has 13+ years academic & 6 years IT experience & serves as Professor & HoD, AI&DS and Dean R&D.Dr. Sahadev Shinde has 18 years academic experience & serves as COE.
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A Real Time Optimizing Mobile Operating System (Paperback)
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LAP Lambert Academic Publishing, 2026
ISBN 10: 6630413288
ISBN 13: 9786630413281
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Paperback. Zustand: new. Paperback. Predicting the execution time of workflow tasks in a cloud environment is vital for optimizing resource utilization and meeting service level agreements (SLAs). In this study, we propose a Two-Stage Machine Learning Approach for Workflow Task Execution Time Prediction. In the first stage, we employ a task classifica-tion model to categorize tasks into groups based on their expected execution times (e.g., short, medium, long). The proposed two-stage approach aims to enhance prediction accuracy and interpretability by leveraging both classification and re-gression techniques. We evaluate the performance of the model using historical data, considering metrics such as accuracy, Mean Absolute Error, and Root Mean Squared Error. Finally, we discuss the deployment and monitoring of the model in a cloud environment, emphasizing its adaptability to evolving infrastructure and workload patterns. Many methods, including resource provisioning and scheduling, depend on predict-ing the workflow activities' performance for changing input data. On the other hand, producing such estimates in the cloud is challenging. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9786630413281
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A Real Time Optimizing Mobile Operating System: An Interactive Context
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A Real Time Optimizing Mobile Operating System: An Interactive Context
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A Real Time Optimizing Mobile Operating System: An Interactive Context
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ISBN 10: 6630413288
ISBN 13: 9786630413281
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A Real Time Optimizing Mobile Operating System: An Interactive Context : Context-Aware Intelligence for Adaptive and Real-Time Mobile Systems
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ISBN 10: 6630413288
ISBN 13: 9786630413281
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Predicting the execution time of workflow tasks in a cloud environment is vital for optimizing resource utilization and meeting service level agreements (SLAs). In this study, we propose a Two-Stage Machine Learning Approach for Workflow Task Execution Time Prediction. In the first stage, we employ a task classifica-tion model to categorize tasks into groups based on their expected execution times (e.g., short, medium, long). The proposed two-stage approach aims to enhance prediction accuracy and interpretability by leveraging both classification and re-gression techniques. We evaluate the performance of the model using historical data, considering metrics such as accuracy, Mean Absolute Error, and Root Mean Squared Error. Finally, we discuss the deployment and monitoring of the model in a cloud environment, emphasizing its adaptability to evolving infrastructure and workload patterns. Many methods, including resource provisioning and scheduling, depend on predict-ing the workflow activities' performance for changing input data. On the other hand, producing such estimates in the cloud is challenging. Bestandsnummer des Verkäufers 9786630413281
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A Real Time Optimizing Mobile Operating System (Paperback)
Verlag:
LAP Lambert Academic Publishing, 2026
ISBN 10: 6630413288
ISBN 13: 9786630413281
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Paperback. Zustand: new. Paperback. Predicting the execution time of workflow tasks in a cloud environment is vital for optimizing resource utilization and meeting service level agreements (SLAs). In this study, we propose a Two-Stage Machine Learning Approach for Workflow Task Execution Time Prediction. In the first stage, we employ a task classifica-tion model to categorize tasks into groups based on their expected execution times (e.g., short, medium, long). The proposed two-stage approach aims to enhance prediction accuracy and interpretability by leveraging both classification and re-gression techniques. We evaluate the performance of the model using historical data, considering metrics such as accuracy, Mean Absolute Error, and Root Mean Squared Error. Finally, we discuss the deployment and monitoring of the model in a cloud environment, emphasizing its adaptability to evolving infrastructure and workload patterns. Many methods, including resource provisioning and scheduling, depend on predict-ing the workflow activities' performance for changing input data. On the other hand, producing such estimates in the cloud is challenging. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9786630413281
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A Real Time Optimizing Mobile Operating System: An Interactive Context
Verlag:
LAP LAMBERT Academic Publishing Aug 2026, 2026
ISBN 10: 6630413288
ISBN 13: 9786630413281
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Predicting the execution time of workflow tasks in a cloud environment is vital for optimizing resource utilization and meeting service level agreements (SLAs). In this study, we propose a Two-Stage Machine Learning Approach for Workflow Task Execution Time Prediction. In the first stage, we employ a task classifica-tion model to categorize tasks into groups based on their expected execution times (e.g., short, medium, long). The proposed two-stage approach aims to enhance prediction accuracy and interpretability by leveraging both classification and re-gression techniques. We evaluate the performance of the model using historical data, considering metrics such as accuracy, Mean Absolute Error, and Root Mean Squared Error. Finally, we discuss the deployment and monitoring of the model in a cloud environment, emphasizing its adaptability to evolving infrastructure and workload patterns. Many methods, including resource provisioning and scheduling, depend on predict-ing the workflow activities' performance for changing input data. On the other hand, producing such estimates in the cloud is challenging. 72 pp. Englisch. Bestandsnummer des Verkäufers 9786630413281
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A Real Time Optimizing Mobile Operating System: An Interactive Context | Context-Aware Intelligence for Adaptive and Real-Time Mobile Systems
Verlag:
LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6630413288
ISBN 13: 9786630413281
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Taschenbuch. Zustand: Neu. A Real Time Optimizing Mobile Operating System: An Interactive Context | Context-Aware Intelligence for Adaptive and Real-Time Mobile Systems | Ganesh Misal (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786630413281 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Bestandsnummer des Verkäufers 136826250
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