We apply the machinery of interventional causal learning with programmable interventions to the domain of applications management. Modern applications are modularized into interdependent components or services (e.g. microservices) for ease of development and management. The communication graph among such components is a function of application code and is not always known to the platform provider. In our solution we learn this unknown communication graph solely using application logs observed during the execution of the application by using fault injections in a staging environment. Specifically, we have developed an active (or interventional) causal learning algorithm that uses the observations obtained during fault injections to learn a model of error propagation in the communication among the components. The “power of intervention” additionally allows us to address the presence of confounders in unobserved user interactions. We demonstrate the effectiveness of our solution in learning the communication graph of well-known microservice application benchmarks. We also show the efficacy of the solution on a downstream task of fault localization in which the learned graph indeed helps to localize faults at runtime in a production environment (in which the location of the fault is unknown). Additionally, we briefly discuss the implementation and deployment status of a fault injection framework which incorporates the developed technology.

In today’s IT environment with a growing number of costly outages, increasing complexity of the systems, and availability of massive operational data, there is a strengthening demand to effectively leverage Artificial Intelligence and Machine Learning (AI/ML) towards enhanced resiliency. In this paper, we present an automatic fault injection platform to enable and optimize the generation of data needed for building AI/ML models to support modern IT operations. The merits of our platform include the ease of use, the possibility to orchestrate complex fault scenarios and to optimize the data generation for the modeling task at hand. Specifically, we designed a fault injection service that (i) combines fault injection with data collection in a unified framework, (ii) supports hybrid and multi-cloud environments, and (iii) does not require programming skills for its use. Our current implementation covers the most common fault types both at the application and infrastructure levels. The platform also includes some AI capabilities. In particular, we demonstrate the interventional causal learning capability currently available in our platform. We show how our system is able to learn a model of error propagation in a micro-service application in a cloud environment (when the communication graph among micro-services is unknown and only logs are available) for use in subsequent applications such as fault localization.

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.

The steady drumbeat of Cloud as a disruptive influence for Infrastructure Service Providers (ISP's) and the enablement vehicle for Software As A Service (SAAS)providers can be heard loud and clear in the industry today. In fact, Cloud is probably at the peak of the hype curve, and already there are identified challenges associated with effective deployment for business critical applications (so called Production Applications) in mature enterprises. One of these challenges is the smooth migration of workload from the previous environment to the new cloud enabled environment in a cost effective way, with minimal disruption and risk. In this paper we introduce extensions to an integrated automation capability called the Darwin framework that enables workload migration for this scenario and discuss the impact that automated migration has on the cost and risks normally associated with migration to clouds.

Current techniques employed to aurally render HTML tables often result in output that is very difficult for sight-impaired users to understand. This paper proposes TTPML, an XML-compliant markup language, which facilitates the generation of prose descriptions of tabular information. The markup language enables content creators to specify contextual reinforcement of, and linear navigation through, tabular information. The markup language may be applied to pre-existing Web content and is reusable across multiple tables. TTPML may be interpreted by origin servers, proxy servers, or browsers. We believe that our approach benefits sight-impaired users by improving accessibility to tabular information.