Ganesh misal (52 risultati)

Paperback. Condizione: 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.…

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 72 pp. Englisch.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condizione: 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.…

Paperback. Condizione: 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.…

Paperback. Condizione: new. Paperback. La prevision du temps d'execution des taches d'un workflow dans un environnement cloud est essentielle pour optimiser l'utilisation des ressources et respecter les accords de niveau de service (SLA). Dans cette etude, nous proposons une approche d'apprentissage automatique en deux etapes pour la prevision du temps d'execution des taches d'un workflow. Au cours de la premiere etape, nous utilisons un modele de classification des taches pour regrouper celles-ci en fonction de leurs durees d'execution prevues (par exemple: courte, moyenne, longue). L'approche en deux etapes proposee vise a ameliorer la precision et l'interpretabilite des predictions en exploitant a la fois des techniques de classification et de regression. Nous evaluons les performances du modele a l'aide de donnees historiques, en tenant compte de mesures telles que la precision, l'erreur absolue moyenne et l'erreur quadratique moyenne. Enfin, nous abordons le deploiement et la surveillance du modele dans un environnement cloud, en soulignant son adaptabilite a une infrastructure et a des modeles de charge de travail en constante evolution. De nombreuses methodes, notamment l'allocation des ressources et l'ordonnancement, dependent de la prediction des performances des activites du flux de travail pour des donnees d'entree variables. En revanche, produire de telles estimations dans le cloud represente un veritable defi. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Condizione: 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.…

Taschenbuch. Condizione: 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.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 72 pp. Französisch.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 68 pp. Spanisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - La prévision du temps d'exécution des tâches d'un workflow dans un environnement cloud est essentielle pour optimiser l'utilisation des ressources et respecter les accords de niveau de service (SLA). Dans cette étude, nous proposons une approche d'apprentissage automatique en deux étapes pour la prévision du temps d'exécution des tâches d'un workflow. Au cours de la première étape, nous utilisons un modèle de classification des tâches pour regrouper celles-ci en fonction de leurs durées d'exécution prévues (par exemple : courte, moyenne, longue). L'approche en deux étapes proposée vise à améliorer la précision et l'interprétabilité des prédictions en exploitant à la fois des techniques de classification et de régression. Nous évaluons les performances du modèle à l'aide de données historiques, en tenant compte de mesures telles que la précision, l'erreur absolue moyenne et l'erreur quadratique moyenne. Enfin, nous abordons le déploiement et la surveillance du modèle dans un environnement cloud, en soulignant son adaptabilité à une infrastructure et à des modèles de charge de travail en constante évolution. De nombreuses méthodes, notamment l'allocation des ressources et l'ordonnancement, dépendent de la prédiction des performances des activités du flux de travail pour des données d'entrée variables. En revanche, produire de telles estimations dans le cloud représente un véritable défi.…

Paperback. Condizione: new. Paperback. Prever o tempo de execucao das tarefas de um fluxo de trabalho num ambiente na nuvem e fundamental para otimizar a utilizacao dos recursos e cumprir os acordos de nivel de servico (SLAs). Neste estudo, propomos uma abordagem de aprendizagem automatica em duas fases para a previsao do tempo de execucao das tarefas de um fluxo de trabalho. Na primeira fase, utilizamos um modelo de classificacao de tarefas para categorizar as tarefas em grupos com base nos seus tempos de execucao previstos (por exemplo, curto, medio, longo). A abordagem em duas fases proposta visa melhorar a precisao e a interpretabilidade da previsao, tirando partido tanto de tecnicas de classificacao como de regressao. Avaliamos o desempenho do modelo utilizando dados historicos, tendo em conta metricas como a precisao, o erro absoluto medio e o erro quadratico medio. Por fim, discutimos a implementacao e a monitorizacao do modelo num ambiente de nuvem, enfatizando a sua adaptabilidade a infraestruturas e padroes de carga de trabalho em constante evolucao. Muitos metodos, incluindo o aprovisionamento de recursos e a programacao, dependem da previsao do desempenho das atividades do fluxo de trabalho para dados de entrada variaveis. Por outro lado, produzir tais estimativas na nuvem e um desafio. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. Prevedere il tempo di esecuzione delle attivita di un flusso di lavoro in un ambiente cloud e fondamentale per ottimizzare l'utilizzo delle risorse e rispettare gli accordi sul livello di servizio (SLA). In questo studio proponiamo un approccio di apprendimento automatico in due fasi per la previsione del tempo di esecuzione delle attivita di un flusso di lavoro. Nella prima fase, utilizziamo un modello di classificazione delle attivita per raggrupparle in base ai tempi di esecuzione previsti (ad esempio, breve, medio, lungo). L'approccio in due fasi proposto mira a migliorare l'accuratezza delle previsioni e l'interpretabilita sfruttando sia tecniche di classificazione che di regressione. Valutiamo le prestazioni del modello utilizzando dati storici, considerando metriche quali l'accuratezza, l'errore assoluto medio e l'errore quadratico medio. Infine, discutiamo l'implementazione e il monitoraggio del modello in un ambiente cloud, sottolineandone l'adattabilita all'evoluzione dell'infrastruttura e dei modelli di carico di lavoro. Molti metodi, tra cui l'allocazione delle risorse e la pianificazione, dipendono dalla previsione delle prestazioni delle attivita del flusso di lavoro per dati di input mutevoli. D'altra parte, produrre tali stime nel cloud rappresenta una sfida. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. Prognozowanie czasu wykonania zadan w ramach przeplywu pracy w srodowisku chmury ma kluczowe znaczenie dla optymalizacji wykorzystania zasobow i dotrzymywania umow dotyczacych poziomu uslug (SLA). W niniejszym badaniu proponujemy dwuetapowe podejscie oparte na uczeniu maszynowym do prognozowania czasu wykonania zadan w ramach przeplywu pracy. W pierwszym etapie wykorzystujemy model klasyfikacji zadan do podzialu zadan na grupy w oparciu o ich przewidywany czas wykonania (np. krotki, sredni, dlugi). Proponowane podejscie dwuetapowe ma na celu zwiekszenie dokladnosci prognozowania i interpretowalnosci poprzez wykorzystanie zarowno technik klasyfikacji, jak i regresji. Oceniamy wydajnosc modelu na podstawie danych historycznych, biorac pod uwage wskazniki takie jak dokladnosc, sredni blad bezwzgledny oraz sredni blad kwadratowy. Na koniec omawiamy wdrozenie i monitorowanie modelu w srodowisku chmury, podkreslajac jego zdolnosc do dostosowywania sie do zmieniajacej sie infrastruktury i wzorcow obciazenia. Wiele metod, w tym alokacja zasobow i planowanie, opiera sie na prognozowaniu wydajnosci dzialan w ramach przeplywu pracy dla zmieniajacych sie danych wejsciowych. Z drugiej strony, generowanie takich prognoz w chmurze stanowi wyzwanie. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. Die Vorhersage der Ausfuehrungszeit von Workflow-Aufgaben in einer Cloud-Umgebung ist entscheidend fuer die Optimierung der Ressourcenauslastung und die Einhaltung von Service Level Agreements (SLAs). In dieser Studie schlagen wir einen zweistufigen Machine-Learning-Ansatz zur Vorhersage der Ausfuehrungszeit von Workflow-Aufgaben vor. In der ersten Stufe verwenden wir ein Aufgabenklassifizierungsmodell, um Aufgaben anhand ihrer erwarteten Ausfuehrungszeiten (z. B. kurz, mittel, lang) in Gruppen einzuteilen. Der vorgeschlagene zweistufige Ansatz zielt darauf ab, die Vorhersagegenauigkeit und Interpretierbarkeit zu verbessern, indem sowohl Klassifizierungs- als auch Regressionstechniken genutzt werden. Wir bewerten die Leistung des Modells anhand historischer Daten unter Beruecksichtigung von Metriken wie Genauigkeit, mittlerer absoluter Fehler und mittlerer quadratischer Fehler. Abschliessend eroertern wir den Einsatz und die UEberwachung des Modells in einer Cloud-Umgebung und heben dabei dessen Anpassungsfaehigkeit an sich veraendernde Infrastruktur- und Arbeitslastmuster hervor. Viele Methoden, darunter die Ressourcenbereitstellung und -planung, sind auf die Vorhersage der Leistung von Workflow-Aktivitaeten bei sich aendernden Eingabedaten angewiesen. Andererseits ist die Erstellung solcher Schaetzungen in der Cloud eine Herausforderung. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. La prevision du temps d'execution des taches d'un workflow dans un environnement cloud est essentielle pour optimiser l'utilisation des ressources et respecter les accords de niveau de service (SLA). Dans cette etude, nous proposons une approche d'apprentissage automatique en deux etapes pour la prevision du temps d'execution des taches d'un workflow. Au cours de la premiere etape, nous utilisons un modele de classification des taches pour regrouper celles-ci en fonction de leurs durees d'execution prevues (par exemple: courte, moyenne, longue). L'approche en deux etapes proposee vise a ameliorer la precision et l'interpretabilite des predictions en exploitant a la fois des techniques de classification et de regression. Nous evaluons les performances du modele a l'aide de donnees historiques, en tenant compte de mesures telles que la precision, l'erreur absolue moyenne et l'erreur quadratique moyenne. Enfin, nous abordons le deploiement et la surveillance du modele dans un environnement cloud, en soulignant son adaptabilite a une infrastructure et a des modeles de charge de travail en constante evolution. De nombreuses methodes, notamment l'allocation des ressources et l'ordonnancement, dependent de la prediction des performances des activites du flux de travail pour des donnees d'entree variables. En revanche, produire de telles estimations dans le cloud represente un veritable defi. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware 72 pp. Französisch.

Taschenbuch. Condizione: Neu. Un système d'exploitation mobile à optimisation en temps réel: un contexte interactif | Ganesh Misal (u. a.) | Taschenbuch | Französisch | 2026 | Editions Notre Savoir | EAN 9786630344790 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. …

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 72 pp. Deutsch.