Optimizing Virtual Resources Management Using Docker on Cloud Applications

This study aims to optimize servers with low utility levels on hardware using container virtualization techniques from Docker. This study's primary focus is to maximize the work of the CPU, RAM, and Hard Drive. The application of virtualization techniques is to create many containers as each of the containers is for the application to run a cloud storage system with the CaaS service infrastructure concept (Container as a Service). Containers on infrastructure will interact with other containers using configuration commands at Docker to form an infrastructure service such as CaaS in general. Testing of hardware carried out by running five Nextcloud cloud storage applications and five MariaDB database applications running in Docker containers and tested by random testing using a multimedia dataset. Random testing with datasets includes uploading and downloading datasets simultaneously and CPU monitoring under load, RAM, and Disk hardware resources. The testing will be done using Docker stats, HTOP, and Cockpit monitoring tools to determine the hardware capabilities when processing multimedia datasets

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system