Running microservices at the edge – the periphery of the network – significantly decreases microservice latency. Edge computing makes microservice architectures more efficient by removing data processing from a centralized core and placing it as close as possible to users.
Over the last decade, what began with software as a service (SaaS) has proliferated into more advanced offerings such as platform as a service (PaaS) and infrastructure as a service (IaaS). Now, even something as complex and data-intensive as machine learning can be offered as a service—if you have enough bandwidth.
As we leave the industrial age behind us and move into the information age, education is undergoing a profound transformation, becoming significantly more individualized and technology-based. There is, however, one key component to unlocking all of these new advancements and strategies: High-speed internet connectivity. And in rural areas, that component is significantly lacking. According to the nonprofit Education Superhighway’s 2018 State of the States report, there are 1,356 schools in America without broadband. And about 80% of these schools are in rural areas.
As the IoT revolution transforms modern agriculture, farms will generate a tremendous amount of data that the cloud simply won’t be able to accommodate. It is estimated that the average farm will generate over four million data points a day by the year 2034. In order to feed the planet, farms in the near future will need to connect at the edge – the periphery of the network that’s as close to the end-user as possible – in order to store and process this daily deluge of data.
Conventional data center cooling methods simply won’t hold up under the processing demands of AI, 5G wireless, Internet of Things (IoT), and the rise of Smart Cities. Promising new cooling methods will be needed not only to ensure that data centers can keep up with data processing demands but also to make data centers ever more sustainable.
Data centers today process ever larger, more complex volumes of data. Hybrid cloud, 5G wireless, the internet of things (IoT), and artificial intelligence (AI)-based applications are pushing traditional data center capabilities to their limits. Data centers require agility and intelligence to effectively manage these increasing demands and workloads.
The demand for content flowing from large-scale gaming and streaming services is driving hyperscale expansion. Because delivering this content without a loss of quality–or a subpar customer experience–requires processing and distribution at the edge, hyperscalers need to supplement their footprint with interconnected points of presence that can optimize content delivery.
St. Louis, Missouri has one of the most robust and distinguished biotech industries in the world. Home to an extraordinary concentration of world-class scientists, major multinational biotech companies, emerging enterprises, first-rate universities and research institutions, St. Louis is a world leader in corporate and academic bioscience research and development.
5G’s initial effects will be most noticeable in technologies like medical robots and autonomous vehicles, it’s clear that 5G will positively – and significantly – disrupt enterprises and organizations leveraging latency-dependent technologies to analyze data in real time and deliver content instantaneously.
Enterprises have so far been forced to work with AI in siloes, due to limitations caused by cloud provider lock-in of data, causing some enterprise IT teams to shy away from the innovation. Now, major tech players are jumping in to help make the implementation of AI seamless for enterprises, opening up the opportunity for hybrid cloud in ways never seen before.