Emerging generative and fine-tuning LLMs services have been widely benchmarked and used for various software development tasks. These LLMs services are powerful but have different output qualities for software development tasks and may not be able to deal with complex development tasks in edge-cloud software modernization and new developments due to their generative capabilities and lack of up-ro-date (domain) knowledge. Many queries and solutions related to target platforms, deploy-ment configurations, policies, data regulation, observability, to name just a few, are not well integrated with these LLMs, but are accessed by the developer through other sources. In this work, we discuss situations where the gaps between the needs and the offerings from LLMs can be compensated by Platform Knowledge, which captures knowledge about, e.g., software, service and infrastructure catalogs, architectural decision records and code patterns. We propose COLLMS - a framework for coordinating LLMs services and Platform Knowledge. At the starting point of the framework, we will discuss challenges for achieving the coordination centered around Platform Knowledge, LLMs management and integration, quality-aware coordination of LLMs, and observability and knowledge updating.

With the adoption of cloud services and the reliability and resiliency it offers, enterprises are eager to understand how many of their legacy applications can be containerized. We propose Application Containerization advisor (ACA), a framework that provides a containerization advisory for legacy applications. Given an application description in terms of its technical components, ACA proposes a multi-step process that standardizes the raw inputs and curates technology stack into various components, detects missing components and finally recommends the best possible containerization approach.

To support IT change automation in cloud-native deployments, we propose to discover change actions from IT change requests. Traditional approaches to discover actions rely on a pre-established set of actions. However in practice, the catalog dictionaries rarely cover a sufficient portion of the IT change requests, resulting in missed automation opportunities. To this end, supervised and semi-supervised approaches have been proposed to detect change actions, but fall short in covering new action discovery as the IT environment evolves. We propose an ensemble technique of parametric and non-parametric grammar-based methods to discover change actions from IT change requests. We evaluate our approach on an IT dataset and find our approach provides significant coverage of actions compared to baseline approaches.

When optimizing software for the cloud, monolithic applications need to be partitioned into many smaller microservices. While many tools have been proposed for this task, we warn that the evaluation of those approaches has been incomplete; e.g. minimal prior exploration of hyperparameter optimization. Using a set of open source Java EE applications, we show here that (a) such optimization can significantly improve microservice partitioning; and that (b) an open issue for future work is how to find which optimizer works best for different problems. To facilitate that future work, see https://github.com/yrahul3910/ase-tuned-mono2micro for a reproduction package for this research.

In migrating production workloads to cloud, enterprises often face the daunting task of evolving monolithic applications toward a microservice architecture. At IBM, we developed a tool called Mono2Micro to assist with this challenging task. Mono2Micro performs spatio-temporal decomposition, leveraging well-defined business use cases and runtime call relations to create functionally cohesive partitioning of application classes. Our preliminary evaluation of Mono2Micro showed promising results.

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.

IT compliance is an area of increasing attention and capital spend in enterprise IT environments. Enforcing compliance is a complex process, which involves following regulatory requirements coming from many, often overlapping sources, and mapping those requirements against a controls framework that implements them on the ground. In this paper, we propose a solution for streamlining the process of analyzing, monitoring and enforcing compliance in the cloud. We rely on text classification methodologies to match both regulatory requirements and controls against a common hierarchy. Finally, we explain how to use the text classification techniques to analyze the regulatory requirements, and match them to executable code that enforces these requirements in the cloud infrastructure components, such as in virtual machines and containers.

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.

Existing approaches to recommend services using natural language queries are supervised or unsupervised. Supervised approaches rely on a dataset with natural language queries annotated with categorizing labels. As the annotation process is manual and requires deep domain knowledge, these approaches are not readily applicable on new datasets. On the other hand, unsupervised approaches overcome the limitation. To date, unsupervised approaches are primarily based on matching keywords, entity relationships, topics and clusters. Keywords and entity relationships ignore the semantic similarity between a query and services. Topics and clusters capture the semantic similarity, but rely on mashups that explicitly capture relationships between services. Again, for new services, the information are not readily available. We propose NL2API, a framework that relies solely on service descriptions for recommending services. NL2API has the benefit of being immediately applicable as a bootstrap recommender for new datasets. To capture relationships among services, NL2API provides different approaches to construct communities where a community represents an abstraction over a group of services. Based on the communities and users' queries, NL2API applies a query matching approach to recommend top-k services. We evaluate NL2API on datasets collected from Programmable Web and API Harmony. Our evaluation shows that for sizable datasets such as Programmable Web NL2API outperforms baseline approaches.

Process behavior analysis (PBA) has been applied to service quality control for decades. In IT service delivery management, PBA has been widely and successfully applied to monitor key performance indicators (KPIs) over time to identify process anomalies. However, we notice that PBA is often limited to a relatively small number of high-level indicators instead of slicing and dicing data for each KPI to find the key dimensions that highlight specific problems. This is due to the fact that without applying domain knowledge to identify relevant alerts, monitoring all possible dimensions leads to too many irrelevant alerts. In this paper, we demonstrate that, using domain-specific rules for each dimension as well as for dimension combinations, we generate targeted alerts for specific consumers. While the presented framework is generic, we demonstrate specifically how targeted alerting can be applied to the incident management process, in particular to monitoring incident volumes over time. We showcase the value of our targeted alerting mechanism using data from 18 outsourcing clients.

IT compliance is an area of increasing attention and capital spend in enterprise IT environments. We present "Continuous Compliance", a framework that allows a managed IT services provider to automate the overall process of keeping IT assets conformant with enterprise policies, regulatory frameworks, and other best practices. Our framework applies to all cloud layers and service models: Infrastructure-, Platform-, and Software-as-a-Service. We describe our framework design, its operation, and the post-process analytics and reporting. We also examine remediation reports gathered from over 2,000 servers for a seven month period, graph the incidence of repeated remediations, and explore some reasons for gradually subsiding remediations.

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.

Cloud migration transforms company's data, applications and services to (or between) one or more other Cloud environments. Enterprises are increasingly migrating their IT infrastructures to Cloud, given the appeal of (pay-per-use) elastic resources. Yet, existing IT infrastructures are complex, heterogeneous and dynamic ecosystems. As a result, there is no single standardized process to seamlessly manage migration at enterprise scale, and often significant level of manual intervention is required, both in reasoning about migration and during its execution. This paper presents a system that automates the process of migration to Cloud. It embeds a Metric-FF Artificial Intelligence (AI) planning algorithm to dynamically assemble migration plans based on the properties of source and target environments, as well as available migration tooling. The paper describes the challenges in migration planning, AI domain design for migration. This work demonstrates that the system provides an effective and scalable solution to generating plans based on the source environment of 700 servers, and varying size of the migration service requests.

With the promise of low-cost access to flexible and elastic resources, enterprises are increasingly migrating their existing workloads into the Cloud. Yet, the heterogeneity of the workloads and existing configuration of legacy IT infrastructure make it challenging to enable a one-click, seamless migration process. There are multiple tools available for migrating servers based on their existing configurations and multiple ways of dealing with data synchronization (post migration). In this paper, we present a Cloud Migration Orchestrator (CMO), based on business process management (BPM) approach to provide a systematic framework to automate and coordinate migration activities. CMO coordinates the process of migration, starting from discovery, provisioning, network configuration, execution of migration, cutover and validation. CMO integrates multiple migration technologies, to support different migration scenarios. We present and discuss our results from a preliminary deployment of CMO to migrate 25 VMware instances and discuss how this approach improves the effectiveness of migration, and seamlessly coordinates activities required to be executed.

When planning a data center migration it is critical to discover the client's business applications and on which devices (server, storage and appliances) those applications are deployed in the infrastructure. It is also important to understand the dependencies the applications have on the infrastructure, on other applications, and in some cases on systems external to the client. Clients can only rarely provide that information in a complete and accurate manner. The usual approach then has been to obtain the information by asking the client's application and platform owners a series of questions but in most cases clients do not have the tools or skills to acquire the requested information. The lack of accurate information leads to project delays, increased cost and higher levels of risk. In this paper we present an algorithm and tools for programmatically identifying and locating business application instances in an infrastructure, based on weighted similarity metric. We discuss results from our preliminary evaluation and the correctness of the algorithm. Such automated approach to application discovery significantly helps clients to achieve their project objectives and timeline without imposing additional work on the application and platform owners.

APIs are increasingly important for companies to enable partners and consumers to access their services and resources. API ecosystems deal with related challenges like publication, promotion and provision of APIs by providers and identification, selection and consumption of APIs by consumers. To address these challenges, to match consumers with relevant APIs, and to support API providers and thus ultimately the ecosystem to evolve, API ecosystems rely on information about APIs, their usage and characteristics, and the social environment around them. We present an extensible, graph-based data model to capture the entities in an API ecosystem and their relations. The data model includes temporal information to capture the evolution of API ecosystems. Analysis operations on top of the data model provide insights for consumers, providers and the ecosystem provider to address the introduced challenges. We present a system implementing the conceptualized data model. We integrate this system with an API ecosystem used in the context of a hackathon event to continuously collect data. We furthermore show the data model's capabilities to represent a well-known dataset about ProgrammableWeb and to drive analysis operations on both datasets.

Using enterprise crowdsourcing service we have engaged multiple teams that interact with 300 clients to obtain insights into workload configurations on client infrastructure, identifying over 400 new project/revenue opportunities.

No abstract available.

IT outsourcing companies have adopted global delivery model, where IT services, such as backup management, are supplied out of multiple locations worldwide, based on the skill and cost of IT delivery staff, such as system administrators (SAs) and call center agents. Managing IT services quality requires insights obtained by extracting large volumes of tacit knowledge about processes, products and people, which is in collective possession of experts. Current practices to discovering this distributed and unstructured knowledge are semi-automated. Often they involve manual data collection using spreadsheets and tracking of exerts through e-mail. As such they fail to scale and provide accurate insights on demand, such as IT infrastructure snapshots. We present an enterprise crowdsourcing service that enables harnessing of human knowledge to derive quality insights in IT services. Our approach automates knowledge and knowledge owner discovery, and is based on the concept of distributed questionnaires. Experts can breakdown the knowledge requests into multiple parts and engage their networks to co-create the content. We discuss the effectiveness of our approach for knowledge discovery in the context of large-scale, on-going business activities in IT outsourcing organization, which collectively engaged over 2500 experts globally.

IAM needs will only grow as devices, servers, and end points continue to increase . Current schemes are not sustainable as the number of IDs will explode. Environment is heterogeneous, and constantly adding new systems including Cloud. Our solution offers a platform where a user gets an individual user ID on a system - but only if they need it, when they need it, for only as long as they need it . Reusable ID scheme reduces the number of IDs in the system yielding cost savings on lifecycle management activities, improved security compliance . A compliance readiness platform can be enabled to prevent, flag, or monitor questionable access in or near real-time . Provide easily accessible logs to prove compliance policies.

With the adoption of mobile, digital and social media networked crowds are reporting and acting upon events in smart environments. Existing platforms for crowdsourcing, support specific activity types, such as micro-tasks on the Amazon's Mechanical Turk; and fall short of facilitating general mechanisms for setting up and maintaining crowd networks easily, flexibly and in a variety of domains. Building upon First International Workshop on Ubiquitous Crowdsourcing, in this edition we challenge researchers and practitioners to identify requirements for a platform for crowd computing, arising from experiences in deployment crowdsourcing applications, which engage crowd members as sensors, controllers and actuators in smart cities and environments. This workshop brings together researchers to produce a vision for the universal crowdsourcing platform, documenting it in a theme publication.

Locating IT Inventory Management information is a challenging task, as the knowledge gets transferred among employees that move within or leave the context of a large organization. Information that relates to IT inventory is hidden in the knowledge of individual team members. This fact is not reflected in organizational expertise repositories and therefore locating those employees becomes a cumbersome manual process, if not intractable. In this paper, we present an expert discovery service that leverages the professional social network of an employee, who was previously known to hold the desired inventory information but is no longer available. Evaluation results suggest that this method reconstructs the desired information more than 80% of the time, as per our experiment involving 50 cases. We demonstrate how a carefully designed crowdsourcing approach can effectively extract the targeted information from the employee's professional social network and discuss its limitations.

Current tools for contact centers provide simple mechanisms for manually logging a technical problem, and recording the route of the problem, from agent to agent, until a solution is identified and then recorded in a "problem ticket." These tools fall far short of accurately capturing the intricacies of technical problems, or providing the details of solution procedures to the right people at the right time. We present a multimedia chronicling system called blogIT that enhances the richness of capture of problem resolution activities, provides the ability to share and socially annotate experiences and solutions, and enables automatic retrieval of relevant solutions in response to a user's desktop activities.