Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Time series analysis and its applications

Subsequently, an agreement on technical co-operation between ISO and CEN was approved by the ISO Council resolution 18/1990 and the CEN General Assembly resolution 3/1990. This agreement (called the Vienna Agreement) was published in June 1991. It is accompanied by common ISO-CEN “Guidelines for the TC/SC Chairmen and Secretariats for implementation”, approved in 1992 and revised in September 1998.

International Organization for Standardization (ISO)

In the production environment, a large part of microservice failures are related to the complex and dynamic interactions and runtime environments, such as those related to multiple instances, environmental configurations, and asynchronous interactions of microservices. Due to the complexity and dynamism of these failures, it is often hard to reproduce and diagnose them in testing environments. It is desirable yet still challenging that these failures can be detected and the faults can be located at runtime of the production environment to allow developers to resolve them efficiently. To address this challenge, in this paper, we propose MEPFL, an approach of latent error prediction and fault localization for microservice applications by learning from system trace logs. Based on a set of features defined on the system trace logs, MEPFL trains prediction models at both the trace level and the microservice level using the system trace logs collected from automatic executions of the target application and its faulty versions produced by fault injection. The prediction models thus can be used in the production environment to predict latent errors, faulty microservices, and fault types for trace instances captured at runtime. We implement MEPFL based on the infrastructure systems of container orchestrator and service mesh, and conduct a series of experimental studies with two opensource microservice applications (one of them being the largest open-source microservice application to our best knowledge). The results indicate that MEPFL can achieve high accuracy in intraapplication prediction of latent errors, faulty microservices, and fault types, and outperforms a state-of-the-art approach of failure diagnosis for distributed systems. The results also show that MEPFL can effectively predict latent errors caused by real-world fault cases.

Latent error prediction and fault localization for microservice applications by learning from system trace logs

A large-scale cloud data center needs to provide high service reliability and availability with low failure occurrence probability. However, current large-scale cloud data centers still face high failure rates due to many reasons such as hardware and software failures, which often result in task and job failures. Such failures can severely reduce the reliability of cloud services and also occupy huge amount of resources to recover the service from failures. Therefore, it is important to predict task or job failures before occurrence with high accuracy to avoid unexpected wastage. Many machine learning and deep learning based methods have been proposed for the task or job failure prediction by analyzing past system message logs and identifying the relationship between the data and the failures. In order to further improve the failure prediction accuracy of the previous machine learning and deep learning based methods, in this paper, we propose a failure prediction algorithm based on multi-layer Bidirectional Long Short Term Memory (Bi-LSTM) to identify task and job failures in the cloud. The goal of Bi-LSTM prediction algorithm is to predict whether the tasks and jobs are failed or completed. The trace-driven experiments show that our algorithm outperforms other state-of-art prediction methods with 93% accuracy and 87% for task failure and job failures respectively.

Task Failure Prediction in Cloud Data Centers Using Deep Learning

Hora: Architecture-aware online failure prediction

The performance of application systems has a direct impact on business metrics. For example, companies lose customers and revenue in case of poor performance such as high response times. Application performance management (APM) aims to provide the required processes and tools to have a continuous and up-to-date picture of relevant performance measures during operations, as well as to support the detection and resolution of performance-related incidents.In this tutorial paper, we provide an overview of the state of the art in APM in industrial practice and academic research, highlight current challenges, and outline future research directions.

Application Performance Management: State of the Art and Challenges for the Future

Over the past decades, various methods, techniques, and tools for modeling and evaluating performance properties of software systems have been proposed covering the entire software life cycle. However, the application of performance engineering approaches to solve a given user concern is still rather challenging and requires expert knowledge and experience. There are no recipes on how to select, configure, and execute suitable methods, tools, and techniques allowing to address the user concerns. In this paper, we describe our vision of Declarative Performance Engineering (DPE), which aims to decouple the description of the user concerns to be solved (performance questions and goals) from the task of selecting and applying a specific solution approach. The strict separation of "what" versus "how" enables the development of different techniques and algorithms to automatically select and apply a suitable approach for a given scenario. The goal is to hide complexity from the user by allowing users to express their concerns and goals without requiring any knowledge about performance engineering techniques. Towards realizing the DPE vision, we discuss the different requirements and propose a reference architecture for implementing and integrating respective methods, algorithms, and tooling.

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Asking "What"?, Automating the "How"?: The Vision of Declarative Performance Engineering

Execution traces capture information on a software system’s runtime behavior, including data on system-internal software control flows, performance, as well as request parameters and values. In research and industrial practice, execution traces serve as an important basis for model-based and measurement-based performance evaluation, e.g., for application performance monitoring (APM), extraction of descriptive and prescriptive models, as well as problem detection and diagnosis. A number of commercial and open-source APM tools that allow the capturing of execution traces within distributed software systems is available. However, each of the tools uses its own (proprietary) format, which means that each approach building on execution trace data is tool-specific.

Towards Performance Tooling Interoperability: An Open Format for Representing Execution Traces

Failures in software systems during operation are inevitable. They cause system downtime, which needs to be minimized to reduce or avoid unnecessary costs and customer dissatisfaction. Online failure prediction aims at identifying upcoming failures at runtime to enable proactive maintenance actions. Existing online failure prediction approaches focus on predicting failures of either individual components or the system as a whole. They do not take into account software architectural dependencies, which determine the propagation of failures. In this paper, we propose a hierarchical online failure prediction approach, HORA, which employs a combination of both failure predictors and architectural models. We evaluate our approach using a distributed RSS reader application by Netflix and investigate the prediction quality for two representative types of failures, namely memory leak and system overload. The results show that, overall, our approach improves the area under the ROC curve by 10.7% compared to a monolithic approach.

Dušan Okanović

Papers

Hora: Architecture-aware online failure prediction

Application Performance Management: State of the Art and Challenges for the Future

Asking "What"?, Automating the "How"?: The Vision of Declarative Performance Engineering

Towards Performance Tooling Interoperability: An Open Format for Representing Execution Traces

An Architecture-Aware Approach to Hierarchical Online Failure Prediction