As the coronavirus pandemic has brought the workforce online, organizations are struggling to manage dynamic remote workloads. On Thursday, a team of data scientists led by a Purdue University professor, Somali Chaterji, introduced a solution called OPTIMUSCLOUD. This new software technology, which runs with a database server, harnesses machine learning to create algorithms to improve the efficiency of virtual machine selection and options for database management systems. The system is designed to help organizations reap the greatest benefit from cloud-based databases. Chaterji directs the Innovatory for Cells and Neural Machines and teaches agricultural and biological engineering.

Research intended to help scientists maximize data throughput to process microbiome or metagenomics data ended up improving the cloud efficiency of long-running dynamic workloads, saving both cloud providers and users money. The software, called OPTIMUSCLOUD, boosts efficiency for cloud-hosted databases by rightsizing resources. It works by using machine learning to develop algorithms that help optimize the cost and performance of both the virtual machine selection and the database management system options. "Our system takes a look at the hundreds of options available and determines the best one normalized by the dollar cost," said Somali Chaterji, a Purdue University assistant professor of agricultural and biological engineering and OPTIMUSCLOUD team leader. "When it comes to cloud databases and computations, you don't want to buy the whole car when you only need a tire."

Broader adoption of machine learning and artificial intelligence (AI) has some system and storage managers really excited. Machine learning algorithms, for example, can be incorporated into the control layer to enable administrators to diagnose the various causes of traffic congestions far more easily. This allows them to predict potentially vulnerable network sectors. "User requests and data traffic can be channeled to and from alternative storage locations based on network usage patterns," said Shiladitya Chaterji, an AI analyst at MarketsAndMarkets. But it goes far beyond being a mere traffic cop.

Broader adoption of machine learning and artificial intelligence (AI) has some system and storage managers really excited. Machine learning algorithms, for example, can be incorporated into the control layer to enable administrators to diagnose the various causes of traffic congestions far more easily. This allows them to predict potentially vulnerable network sectors. "User requests and data traffic can be channeled to and from alternative storage locations based on network usage patterns," said Shiladitya Chaterji, an AI analyst at MarketsAndMarkets. But it goes far beyond being a mere traffic cop. AI and machine learning are influencing data storage in many different ways.