a review of qos-driven task scheduling algorithms and their impact on data quality in process management

The term “cloud computing (cc)” has been extensively studied and utilized by major corporations since its inception. within the realm of cloud computing, various research topics and perspectives have been explored, including resource management, cloud security, and energy efficiency. this paper explores the intersection of data quality and business process management within the context of cloud computing. specifically, it examines how quality of service (qos)-driven task scheduling algorithms in cloud environments can enhance data quality and optimize business processes. cloud computing still faces the significant challenge of determining the most effective way to schedule tasks and manage available resources. we need effective scheduling strategies to manage these resources because of the scale and dynamic resource provisioning in modern data centers. the purpose of this work is to provide an overview of the various task scheduling methods that have been utilized in the cloud computing environment to date. an attempt has been made to categorize current methods, investigate issues, and identify important challenges present in this area. our data reveals that 34% of researchers are focusing on makespan for qos (quality of service) metrics, 17% on cost, 15% on load balancing, 10% on deadline, and 9% on energy usage. other criteria for the quality of service (qos) parameter contribute far less than the ones mentioned above. according to this study, scheduling algorithms commonly used by researchers include the genetic algorithm in bio-inspired systems and particle swarm optimization in swarm intelligence 80% of the time. according to the available literature, 70% of the studies have utilized cloudsim as their simulation tool of choice. our findings suggest that current methodologies mainly employ genetic algorithms and particle swarm optimization, with cloudsim being a popular simulation tool. ongoing work emphasizes refining scheduling strategies to enhance resource management in dynamic data center environments, providing crucial insights into future quality-of-service (qos)-driven scheduling algorithms for cloud computing.

a review of qos-driven task scheduling algorithms and their impact on data quality in process management

the term “cloud computing (cc)” has been extensively studied and utilized by major corporations since its inception. within the realm of cloud computing, various research topics and perspectives have been explored, including resource management, cloud security, and energy efficiency. this paper explores the intersection of data quality and business process management within the context of cloud computing. specifically, it examines how quality of service (qos)-driven task scheduling algorithms in cloud environments can enhance data quality and optimize business processes. cloud computing still faces the significant challenge of determining the most effective way to schedule tasks and manage available resources. we need effective scheduling strategies to manage these resources because of the scale and dynamic resource provisioning in modern data centers. the purpose of this work is to provide an overview of the various task scheduling methods that have been utilized in the cloud computing environment to date. an attempt has been made to categorize current methods, investigate issues, and identify important challenges present in this area. our data reveals that 34% of researchers are focusing on makespan for qos (quality of service) metrics, 17% on cost, 15% on load balancing, 10% on deadline, and 9% on energy usage. other criteria for the quality of service (qos) parameter contribute far less than the ones mentioned above. according to this study, scheduling algorithms commonly used by researchers include the genetic algorithm in bio-inspired systems and particle swarm optimization in swarm intelligence 80% of the time. according to the available literature, 70% of the studies have utilized cloudsim as their simulation tool of choice. our findings suggest that current methodologies mainly employ genetic algorithms and particle swarm optimization, with cloudsim being a popular simulation tool. ongoing work emphasizes refining scheduling strategies to enhance resource management in dynamic data center environments, providing crucial insights into future quality-of-service (qos)-driven scheduling algorithms for cloud computing.