Next Generation Systems and Cloud       


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Next Generation Systems and Cloud - overview

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Dataset Lifecycle Framework: the swiss army knife for data source management in Kubernetes

Hybrid Cloud is rapidly becoming the go-to IT strategy for organizations seeking the perfect mix of scalability, performance and security. As a result, it is now common for an organization to rely on a mix of on-premise and cloud solutions, or “data-sources”, from different providers to store and manage their data. It doesn’t really sound problematic, not until applications have to access the data []


Advancing cloud with memory disaggregation

Here at IBM Research – Ireland, we are rethinking the very foundations on which the cloud is built. We are developing a concept and prototype for low-power and high-utilization disaggregated cloud data centres that break known boundaries, enabling the dynamic creation of fit-for-purpose computing environments from a pool of disaggregated resources. Today’s cloud data []


Designing new materials with data-centric systems

For decades, researchers have used high performance computing (HPC) to simulate systems at ever-growing speeds and scales. Recently, the design of HPC systems has started to evolve to handle and exploit the vast amounts of data now produced by both models and real-world data-sources, a paradigm IBM calls Data-Centric Computing (DCS). DCS provides a flexible []


A Robust AI-Centric Indoor Positioning System

In modern airport terminals, hospital complexes, office buildings, sports arenas, university campuses, and retail outlets, there is a growing market for convenient and easy-to-use applications for navigating indoors. With an expected growth rate of 30 percent CAGR by 2022, according to a MarketWatch Report, this demand is accelerated by the presence of advanced sensors in modern smartphones like magnetometers, accelerometers and gyroscopes. To meet the demand,  [...]


Semantic Cache for AI-Enabled Image Analysis

The availability of high-resolution, inexpensive sensors has exponentially increased the amount of data being produced, which could overwhelm the existing Internet. This has led to the need for computing capacity to process the data close to where it is generated, at the edges of the network, in lieu of sending it to cloud datacenters. Edge computing, as this is known, not only reduces the strain on bandwidth but also reduces latency of obtaining intelligence from raw data. However, availability of resources at the edge [...]