Abstract
In recent years, cloud workflow task scheduling has always been an important research topic in the business world. Cloud workflow task scheduling means that the workflow tasks submitted by users are allocated to appropriate computing resources for execution, and the corresponding fees are paid in real time according to the usage of resources. For most ordinary users, they are mainly concerned with the two service quality indicators of workflow task completion time and execution cost. Therefore, how cloud service providers design a scheduling algorithm to optimize task completion time and cost is a very important issue. This paper proposes research on workflow scheduling based on mobile cloud computing machine learning, and this paper conducts research by using literature research methods, experimental analysis methods, and other methods. This article has deeply studied mobile cloud computing, machine learning, task scheduling, and other related theories, and a workflow task scheduling system model was established based on mobile cloud computing machine learning from different algorithms used in processing task completion time, task service costs, task scheduling, and resource usage The situation and the influence of different tasks on the experimental results are analyzed in many aspects. The algorithm in this paper speeds up the scheduling time by about 7% under a different number of tasks and reduces the scheduling cost by about 2% compared with other algorithms. The algorithm in this paper has been obviously optimized in time scheduling and task scheduling.
Highlights
With the widespread popularization and application of Internet technology, as well as the rapid growth of information, the data that scientific research and business need to face and process has become increasingly large and complex, far exceeding the computing power of the existing IT infrastructures
(2) We combined theoretical research with empirical research based on mobile cloud computing and machine learning theory, and we investigated based on the specific situation of workflow task scheduling
This article is mainly about the research of workflow scheduling based on mobile cloud computing machine learning
Summary
With the widespread popularization and application of Internet technology, as well as the rapid growth of information, the data that scientific research and business need to face and process has become increasingly large and complex, far exceeding the computing power of the existing IT infrastructures. With the application of cloud computing in the field of healthcare, the development and momentum of networked medical applications and systems have emerged They reviewed the techniques, tools, and applications of big data analysis. The research results of using big data and mobile cloud computing technology to design networked medical systems are summarized. Aiming at the simple and scientific workflow scheduling problem in cloud computing, many latest workflow scheduling schemes have been proposed in the literature, and they have conducted a comprehensive review and analysis of these schemes They clarified the goals of scheduling schemes in cloud computing, and they classified the proposed schemes according to the type of scheduling algorithm applied in each scheme. Research Method of Workflow Scheduling Based on Mobile Cloud Computing Machine Learning
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
