We use AI to automatically break down the overall application by representing application code as graphs. Our AI relies on Graph Representation Learning – a popular method in deep learning. Graphs are a natural representation for software and applications. We translated the application to a graph where the programs become nodes. Their relationships with other programs become edges and determine the boundary to separate the nodes of common business functionality.
To help the developers that update legacy applications, our team has created Mono2Micro (monolith-to-microservice) – an AI assistant that modernizes legacy applications to help move them to the cloud as microservices. Our tool simplifies and speeds up the often error-prone “application refactoring” process of partitioning and preserving the original semantics of the legacy, monolith applications.
IBM Research has partnered with Red Hat to bring iter8 into Kiali. Iter8 lets developers automate the progressive rollout of new microservice versions. From Kiali, developers can launch these rollouts interactively, watch their progress while iter8 shifts user traffic to the best microservice version, gain real-time insights into how competing versions (two or more) perform, and uncover trends on service metrics across versions.
New IBM, Fujifilm prototype breaks world record, delivers record 27X more areal density than today’s tape drives
IEDM 2020: Advances in memory, analog AI and interconnects point to the future of hybrid cloud and AI
At this year’s IEEE International Electron Devices Meeting, IBM researchers will describe a number of breakthroughs aimed at advancing key hardware infrastructure components, including: Spin-Transfer Torque Magnetic Random-Access Memory (STT-MRAM), analog AI hardware, and advanced interconnect scaling designed to meet those hardware infrastructure demands.
In 2021, our hybrid cloud predictions show that we expect businesses to address challenges in ways that will apply new resources and strategies to drive business outcomes, in a world that will continue to require new advances in cloud and AI research.
Unfortunately, there are no default scheduler plugins in Kubernetes to consider the actual load in clusters for scheduling. To achieve that goal, we developed a way to optimize resource allocation through load-aware scheduling and submitted our "Trimaran: Real Load Aware Scheduling" Kubernetes enhancement proposal, with the hope of soon merging this feature into the Kubernetes scheduler plugin.
IBM and New York State have upgraded their workhorse lithography tool with the latest EUV system, the NXE3400. Now, this system is fully operational in a state-of-the-art semiconductor research fab on the SUNY Poly campus in Albany, enabling logic research for the next decade to come.
Watch the replay of the virtual roundtable, “Talking in Code: The New Frontier for AI and Hybrid Cloud,” with researchers from IBM, Columbia University and North Carolina State University discussing how AI can simplify and streamline hybrid cloud environments as well as make them more secure for mission-critical workloads.