Toward Self-Healing Multitier Services

Are self-healing database-centric multitier services utopia or just a hard puzzle? We argue for the latter and aim to identify the missing pieces of this puzzle. We advocate robust and scalable learning-based approaches to self-healing that we expect to work well for a large class of multitier services. We identify performance-availability problems (PAPs) as the most relevant target for self-healing, and argue that PAPs are best addressed macroscopically, outside the realm of individual tiers. Finally, we lay out a research agenda for learning-based approaches to self-healing, to enable wider deployment of self-healing multi-tier services

Quadcopter Flight Control Using a Non-invasive Multi-Modal Brain Computer Interface

Brain-Computer Interfaces (BCIs) translate neuronal information into commands to control external software or hardware, which can improve the quality of life for both healthy and disabled individuals. Here, a multi-modal BCI which combines motor imagery (MI) and steady-state visual evoked potential (SSVEP) is proposed to achieve stable control of a quadcopter in three-dimensional physical space. The complete information common spatial pattern (CICSP) method is used to extract two MI features to control the quadcopter to fly left-forward and right-forward, and canonical correlation analysis (CCA) is employed to perform the SSVEP classification for rise and fall. Eye blinking is designed to switch these two modes while hovering. Real-time feedback is provided to subjects by a global camera. Two flight tasks were conducted in physical space in order to certify the reliability of the BCI system. Subjects were asked to control the quadcopter to fly forward along the zig-zag pattern to pass through a gate in the relatively simple task. For the other complex task, the quadcopter was controlled to pass through two gates successively according to an S-shaped route. The performance of the BCI system is quantified using suitable metrics and subjects are able to acquire 86.5% accuracy for the complicated flight task. It is demonstrated that the multi-modal BCI has the ability to increase the accuracy rate, reduce the task burden, and improve the performance of the BCI system in the real world

Simplifying System Management Through Automated Forecasting, Diagnosis, and Configuration Tuning

<p>Large-scale networked computing systems are widely deployed to run business-critical applications in environments where changes are frequent. Manual management of these complex systems can be tedious and error-prone. Meanwhile, the high costs of application downtime make it critical to ensure system availability and reliability. Recent progress in monitoring tools enables system administrators to collect fine-grained data about system activity with low overhead. This data provides valuable information for system management. However, the monitoring data collected from production systems is massive in size and noisy; which makes it hard for system administrators to fully utilize this data for effective system management.</p> <p>This dissertation describes a data-management platform, called Fa, where system administrators can pose declarative queries over system monitoring data. Fa automatically finds fairly accurate and efficient execution plans for given queries, and returns query results in easy-to-interpret formats. Fa supports three key query types, namely, forecasting queries (for predicting or detecting performance problems), diagnosis queries (for finding the cause of performance problems), and tuning queries (for recommending changes to system configuration to resolve diagnosed problems):</p> <p>(a) For processing diagnosis queries, Fa constructs problem signatures from system monitoring data to identify recurrent problems and to reuse past diagnostic information. For a rare or new problem, Fa employs an anomaly-based clustering technique to generate performance baselines and to characterize the deviation from baselines to pinpoint root causes. Fa also incorporates an active-learning component that identifies diagnosis queries whose results, if provided or confirmed by system administrators, can be used to update problem signatures and to improve the accuracy and efficiency for processing future queries.</p> <p>(b) For processing tuning queries to resolve problems caused by system misconfiguration, Fa employs an adaptive sampling algorithm that plans experiments to efficiently identify high-impact configuration parameters and high-performance settings. These experiments bring in information---required for generating accurate query results---that is missing in the monitoring data collected so far.</p> <p>(c) For both one-time and continuous forecasting queries, Fa automatically searches for efficient execution plans in a large space of plans composed of data-transformation operators as well as synopsis-learning and prediction operators. Forecasting queries can be composed with diagnosis and tuning queries to enable proactive system management that avoids potential problems.</p> <p>We have evaluated the Fa platform with monitoring data collected from database-backed multitier services, and with synthetic data that models the noisy nature of monitoring data from production systems. Our evaluation shows that Fa's query plan selection and execution strategies provide actionable information for system management automatically, accurately, and efficiently. Critical features like reliable confidence estimates, robustness to noise, and providing supporting evidence for query results make Fa a practical and useful platform.</p>Dissertatio

Processing forecasting queries

Forecasting future events based on historic data is useful in many domains like system management, adaptive query processing, environmental monitoring, and financial planning. We describe the Fa system where users and applications can pose declarative forecasting queries—both onetime queries and continuous queries—and get forecasts in real-time along with accuracy estimates. Fa supports efficient algorithms to generate execution plans automatically for forecasting queries from a novel plan space comprising operators for transforming data, learning statistical models from data, and doing inference using the learned models. In addition, Fa supports adaptive query-processing algorithms that adapt plans for continuous forecasting queries to the time-varying properties of input data streams. We report an extensive experimental evaluation of Fa using synthetic datasets, datasets collected on a testbed, and two real datasets from production settings. Our experiments give interesting insights on plans for forecasting queries, and demonstrate the effectiveness and scalability of our planselection algorithms. 1

Proactive Identification of Performance Problems

We propose to demonstrate Fa, an automated tool for timely and accurate prediction of Service-Level-Agreement (SLA) violations caused by performance problems in database systems. Fa periodically collects performance data at three levels: applications, database server, and operating system. This data is used to construct probabilistic models for predicting SLA violations. Fa currently uses graphical Bayesian network models because of their ability to support a wide range of inferences, including prediction and diagnosis, as well as their support for interactive visualization and presentation of complex system behavior in intuitive ways. 1

Toward Self-Healing Multitier Services

Are self-healing database-centric multitier services utopia or just a hard puzzle? We argue for the latter and aim to identify the missing pieces of this puzzle. We advocate robust and scalable learning-based approaches to self-healing that we expect to work well for a large class of multitier services. We identify performance-availability problems (PAPs) as the most relevant target for self-healing, and argue that PAPs are best addressed macroscopically, outside the realm of individual tiers. Finally, we lay out a research agenda for learning-based approaches to selfhealing, to enable wider deployment of self-healing multitier services.

Fa: A system for automating failure diagnosis

Failures of Internet services and enterprise systems lead to user dissatisfaction and considerable loss of revenue. Since manual diagnosis is often laborious and slow, there is considerable interest in tools that can diagnose the cause of failures quickly and automatically from system-monitoring data. This paper identifies two key data-mining problems arising in a platform for automated diagnosis called Fa. Fa uses monitoring data to construct a database of failure signatures against which data from undiagnosed failures can be matched. Two novel challenges we address are to make signatures robust to the noisy monitoring data in production systems, and to generate reliable confidence estimates for matches. Fa uses a new technique called anomalybased clustering when the signature database has no highconfidence match for an undiagnosed failure. This technique clusters monitoring data based on how it differs from the failure data, and pinpoints attributes linked to the failure. We show the effectiveness of Fa through a comprehensive experimental evaluation based on failures from a production setting, a variety of failures injected in a testbed, and synthetic data. I