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 cloud-native applications, a large fraction of operational failures, known as outages, result in violations of Service Level Objectives (SLOs). SLOs are defined around specific measurable characteristics: availability, throughput, frequency, response time, and quality. Four metrics, latency, traffic, errors, and saturation, ensure coverage for most outages of an application. These are often called golden signals. The dynamicity and complexity of cloud-native applications complicate Site Reliability Engineers’ (SREs) efforts in problem determination, in particular in its fault localization. The fault localization is often a try-and-error process in which SREs rely on their domain knowledge and experience. It is laborious and frequently results in long Mean Time To Resolution (MTTR) for outages. This paper describes a lightweight fault localization system, that establishes causal relationships among the golden signal service errors and error logs, and further leverages PageRank centrality of the derived causal graph for generating a ranked list of faulty microservices.

The loosely-coupled microservices architecture has become increasingly popular due to the advantage of its modularity and elasticity in cloud applications. However, it also seriously complicates cloud management and degrades the performance of IT operations. Today, AI has been the locus of commerce and transactions, and transforming traditional IT operations for speed and growth. Inferring the dependencies among an application's microservices can greatly help SREs diagnose possible root causes of performance issues, which is a hard task due to the complex topology of microservices is often unknown in practice. Prior literature on detecting causal structure for cloud services requires significant application instrumentation, which rarely holds in reality. In this work, we leverage Granger causality models on just monitored log data of a microservice-based application to infer the impact of dependencies between microservices. We first describe the approach of modeling discrete log data as time series, and then formally define the Granger causality problem using both linear and nonlinear autoregressive models. Finally, we conduct an extensive comparative study to show the performance of the state-of-the-art linear and nonlinear (i.e., neural) Granger causality methods on both synthetic data and real-world log data from a publicly available benchmark microservice system. Our preliminary results indicate that neural Granger causality models outperform traditional Granger causality methods on both linear and nonlinear time series data, while for large linear time series, linear Granger causal models are more efficient with high accuracy. Using the real-world log data, we also demonstrate our interesting findings on inferred dependency graph of microservices by linear and neural Granger causality models.

Online interactive recommender systems strive to promptly suggest users appropriate items (e.g., movies and news articles) according to the current context including both user and item content information. Such contextual information is often unavailable in practice, where only the users' interaction data on items can be utilized by recommender systems. The lack of interaction records, especially for new users and items, inflames the performance of recommendation further. To address these issues, both collaborative filtering, one of the most popular recommendation techniques relying on the interaction data only, and bandit mechanisms, capable of achieving the balance between exploitation and exploration, are adopted into an online interactive recommendation setting assuming independent items (i.e., arms). This assumption rarely holds in reality, since the real-world items tend to be correlated with each other. In this paper, we study online interactive collaborative filtering problems by considering the dependencies among items. We explicitly formulate item dependencies as the clusters of arms in the bandit setting, where the arms within a single cluster share the similar latent topics. In light of topic modeling techniques, we come up with a novel generative model to generate the items from their underlying topics. Furthermore, an efficient particle-learning based online algorithm is developed for inferring both latent parameters and states of our model by taking advantage of the fully adaptive inference strategy of particle learning techniques. Additionally, our inferred model can be naturally integrated with existing multi-armed selection strategies in an interactive collaborative filtering setting. Empirical studies on two real-world applications, online recommendations on movies and news, demonstrate both the effectiveness and efficiency of our proposed approach.

An efficient delivery of IT services for increasingly complex IT environments demands an intelligent automated solution for resolving existing and potential issues. An automation recommender system, promptly suggesting the most proper scripted resolution to an arriving IT incident ticket, would play a significant role in IT automation services. Hence, developing a comprehensive framework supporting becomes imperative for continuous improvement of automation recommendation.In this paper, we first identify the challenges of IT services followed by a discussion on AISTAR (an intelligent system for online IT ticket automation recommendation) designed and developed to provide them. Specifically, we define and formalize automation recommendation procedure as a multi-armed bandit problem with dependent arms, which is capable of achieving the optimal tradeoff between exploitation of the system for the best automation recommendation and exploration of automation execution information for future recommendation. Two novel multi-armed bandit models are proposed and integrated to handle the aforementioned challenges in IT automation services. Empirical studies on a large ticket dataset from IBM Global Services demonstrate both the effectiveness and efficiency of our intelligent integrated system. AISTAR is earmarked for Cognitive Event Automation for IBM Service delivery.

The increasing complexity of IT environments urgently requires the use of analytical approaches and automated problem resolution for more efficient delivery of IT services. In this paper, we model the automation recommendation procedure of IT automation services as a contextual bandit problem with dependent arms, where the arms are in the form of hierarchies. Intuitively, different automations in IT automation services, designed to automatically solve the corresponding ticket problems, can be organized into a hierarchy by domain experts according to the types of ticket problems. We introduce a novel hierarchical multi-armed bandit algorithms leveraging the hierarchies, which can match the coarse-to-fine feature space of arms. Empirical experiments on a real large-scale ticket dataset have demonstrated substantial improvements over the conventional bandit algorithms. In addition, a case study of dealing with the cold-start problem is conducted to clearly show the merits of our proposed algorithms.

In large scale and complex IT service environments, a problematic incident is logged as a ticket and contains the ticket summary (system status and problem description). The system administrators log the step-wise resolution description when such tickets are resolved. The repeating service events are most likely resolved by inferring similar historical tickets. With the availability of reasonably large ticket datasets, we can have an automated system to recommend the best matching resolution for a given ticket summary. In this paper, we first identify the challenges in real-world ticket analysis and develop an integrated framework to efficiently handle those challenges. The framework first quantifies the quality of ticket resolutions using a regression model built on carefully designed features. The tickets, along with their quality scores obtained from the resolution quality quantification, are then used to train a deep neural network ranking model that outputs the matching scores of ticket summary and resolution pairs. This ranking model allows us to leverage the resolution quality in historical tickets when recommending resolutions for an incoming incident ticket. In addition, the feature vectors derived from the deep neural ranking model can be effectively used in other ticket analysis tasks, such as ticket classification and clustering. The proposed framework is extensively evaluated with a large real-world dataset.

Large-scale time series data are prevalent across diverse application domains including system management, biomedical informatics, social networks, finance, etc. Temporal dependency discovery performs an essential part to identify the hidden interactions among the observed time series and helps to gain more insight into the behavior of the applications. However, the time-varying sparsity of the interactions among time series often poses a big challenge to temporal dependency discovery in practice. This paper formulates the temporal dependency problem with a novel Bayesian model allowing for both the sparsity and evolution of the hidden interactions among the observed time series. Taking advantage of the Bayesian modeling, an online inference method is proposed for time-varying temporal dependency discovery. Extensive empirical studies on both the synthetic and real application time series data are conducted to demonstrate the effectiveness and the efficiency of the proposed method.

In disaster management, people are interested in the development and the evolution of the disasters. If they intend to track the information of the disaster, they will be overwhelmed by the large number of disaster-related documents, microblogs, and news, etc. To support disaster management and minimize the loss during the disaster, it is necessary to efficiently and effectively collect, deliver, summarize, and analyze the disaster information, letting people in affected area quickly gain an overview of the disaster situation and improve their situational awareness.

Contextual multi-armed bandit problems have gained increasing popularity and attention in recent years due to their capability of leveraging contextual information to deliver online personalized recommendation services (e.g., online advertising and news article selection). To predict the reward of each arm given a particular context, existing relevant research studies for contextual multi-armed bandit problems often assume the existence of a fixed yet unknown reward mapping function. However, this assumption rarely holds in practice, since real-world problems often involve underlying processes that are dynamically evolving over time.