Resource Optimization and Task Scheduling Using Logistic Regression for Cloud Computing
Keywords:
Resource Optimization, Task Scheduling, Logistic Regression, Cloud Computing, Resource Management, Virtualization, Distributed Systems, Scalability.Abstract
Resource-optimized task scheduling is an essential issue in green computing. This paper uses a logistic regression-based deep recurrent network in cloud computing to optimize task scheduling. We first train the network on a large dataset of real-world task scheduling. We then use the network to find the best scheduling for a given resource configuration. Our results show that the network can reduce resource usage by up to 50% for a given task. Resource-optimized task scheduling aims to minimize the resources used while still meeting deadlines. This is often accomplished in cloud computing using a logistic regression-based deep recurrent network. This type of network can learn patterns in data and make predictions about future data. Using this type of network makes it possible to schedule tasks to minimize the resources used while still meeting deadlines. This method has the potential to save significant amounts of resources in cloud computing, which can translate into cost savings for companies that use cloud services. Task scheduling is allocating tasks to a set of resources to complete the tasks within a given timeframe. In cloud computing, task scheduling allocates tasks to virtual machines (VMs) to complete the tasks within a given timeframe.
Various optimization techniques have been proposed to optimize resource use and minimize task scheduling costs. This blog post will focus on a resource-optimized task scheduling technique that uses a logistic regression-based deep recurrent network.
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