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
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.
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.
IBM Research has initiated focused efforts called Code Risk Analyzer to bring security and compliance analytics to DevSecOps. Code Risk Analyzer is a new feature of IBM Cloud Continuous Delivery, a cloud service that helps provision toolchains, automate builds and tests, and control quality with analytics.
IBM Research is developing new ways to use AI to assure clients are moving their mission-critical workloads to a secure cloud environment and can manage those workloads across multiple clouds.
This year’s IBM "5 in 5" predictions focus on accelerating the discovery of new materials to enable a more sustainable future. In line with the United Nation’s global call-to-action through its Sustainable Development Goals, IBM researchers are working to speed up the discovery of new materials that will address significant worldwide problems.