Mono2Micro is an AI-based toolchain that provides recommendations for decomposing legacy web applications into microservice partitions. Mono2Micro consists of a set of tools that collect static and runtime information from a monolithic application and process the information using an AI-based technique to generate recommendations for partitioning the application classes. Each partition represents a candidate microservice or a grouping of classes with similar business functionalities. Mono2Micro takes a temporo-spatial clustering approach to compute meaningful and explainable partitions. It generates two types of partition recommendations. First, it computes business-logic-seams-based partitions that represent a desired encapsulation of business functionalities. However, such a recommendation may cut across data dependencies between classes, accommodating which could require significant application updates. To address this, Mono2Micro computes natural-seams-based partitions, which respect data dependencies. We describe the set of tools that comprise Mono2Micro and illustrate them using a well-known open-source JEE application.

The growing popularity of cloud computing has increased demands of application migration to the cloud, but among others, the main deterrent has been the complexity of the migration planning for the large-scale projects with 1000s of servers. The complexity is mainly derived from the plethora of different migration options and required platform/application changes for cloud fitness of applications. In addition, the automated or artificial intelligence planning has been used to generate the migration plans, but despite its effectiveness, it has not been adopted broadly in migration because the AI planning language is complex and hard to scale. In this paper, we propose Cloud Readiness Planning Tool (CRPT), a system that constitutes a Migration Type Classifier trained under a novel active learning strategy dealing with "concept drift", and an AI Planner which generates plan from automatically created domain and problem files with declarative specifications such as goal states and data in user friendly input formats. A series of experiments were conducted on a real-world migration task. The results demonstrate the Migration Type Classifier is able to effectively adapt to the changing business needs and achieve high accuracy with low labeling cost.

Presented BlueShift: Self-service for orchestrating automated tasks (via APIs) and human tasks for end-to-end transformation process including, application discovery, analysis, artifact transformation and enablement of cloud value-add services. Demonstrated transformation process for PlantsByWebSphere application, with Liberty Profile runtime as a target in BlueMix (cloud-foundry based platform). Discussed challenges arising in transformation from application complexity and non-functional requirements.

Bluefix is a solution we created for managing the vulnerabilities in a cloud native world where clients operate their own DevOp pipelines and design the same management function and assurance of that of traditional managed services. We show that the challenges imposed by container cloud and DevOp culture requires significant departure of service management design. As a case study, we examined how vulnerabilities are managed in the pre-cloud native vs. post-cloud native world, identified control points in both DevOp pipeline as well as cloud service fabric as to enable a scalable and automated vulnerability management solution for IBM container cloud. Our journey and exploration in this area has only just begun, as the lessons learned and many remaining challenges hold much promise and opportunities.

In this paper, we investigate the benefits of adding autonomic capabilities inside the operating system. We have developed and implemented a solution that focuses on three use cases (continuous file permission compliance, dynamic disk cleanup, and accidental removal protection) for the file system, and encapsulates all the respective file system monitoring, troubleshooting and error remedial operations in a Linux kernel module. The main benefits of this approach are the capability to detect issues instantly when they occur, and fix these issues transparently, with the invoking applications being unaware of their occurrence. These capabilities are not present in external agent architectures, including contemporary configuration management systems, like Puppet, Chef, or CFEngine. We have built a prototype and evaluated the performance of the most resource intensive use case, dynamic disk cleanup, using the FileBench file system benchmarking tool.

Increasingly organizations are considering moving their workloads to clouds to take advantage of the anticipated benefits of a more cost effective and agile IT infrastructure. A key component of a cloud service, as it is exposed to the consumer, is the published selection of instance resource configurations (CPU, memory, and disk). The number of instance configurations, as well as the specific values that characterize them, form important decisions for the cloud service provider. This paper explores these resource configurations; examines how well a traditional data center fits into the cloud model from a resource allocation perspective; and proposes a framework, named CloudAffinity, aimed at selecting an optimal number of configurations based on customer requirements.