Transfer Learning for Improving Model Predictions in Highly Configurable Software

Modern software systems are built to be used in dynamic environments using configuration capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we are often interested in reasoning about the performance of the systems under different configurations. Usually, we learn a black-box model based on real measurements to predict the performance of the system given a specific configuration. However, as modern systems become more complex, there are many configuration parameters that may interact and we end up learning an exponentially large configuration space. Naturally, this does not scale when relying on real measurements in the actual changing environment. We propose a different solution: Instead of taking the measurements from the real system, we learn the model using samples from other sources, such as simulators that approximate performance of the real system at low cost. We define a cost model that transform the traditional view of model learning into a multi-objective problem that not only takes into account model accuracy but also measurements effort as well. We evaluate our cost-aware transfer learning solution using real-world configurable software including (i) a robotic system, (ii) 3 different stream processing applications, and (iii) a NoSQL database system. The experimental results demonstrate that our approach can achieve (a) a high prediction accuracy, as well as (b) a high model reliability.Comment: To be published in the proceedings of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS'17

Deep Reinforcement Learning (DRL)-based Methods for Serverless Stream Processing Engines: A Vision, Architectural Elements, and Future Directions

Streaming applications are becoming widespread across an extensive range of business domains as an increasing number of sources continuously produce data that need to be processed and analysed in real time. Modern businesses are aggressively using streaming data to generate valuable knowledge that can be used to automate processes, help decision-making, optimize resource usage, and ultimately generate revenue for the organization. Despite their increased adoption and tangible benefits, support for the automated deployment and management of streaming applications is yet to emerge. Although a plethora of stream management systems have flooded the open source community in recent years, all of the existing frameworks demand a considerably challenging and lengthy effort from human operators to manually and continuously tune their configuration and deployment environment in order to reach and maintain the desired performance goals. To address these challenges, this article proposes a vision for creating Deep Reinforcement Learning (DRL)-based methods for transforming stream processing engines into self-managed serverless solutions. This will lead to an increase in productivity as engineers can focus on the actual development process, an increase in application performance potentially leading to reduced response times and more accurate and meaningful results, and a considerable decrease in operational costs for organizations.Comment: 21 pages, 10 figure

Learning Very Large Configuration Spaces: What Matters for Linux Kernel Sizes

Linux kernels are used in a wide variety of appliances, many of them having strong requirements on the kernel size due to constraints such as limited memory or instant boot. With more than ten thousands of configuration options to choose from, obtaining a suitable trade off between kernel size and functionality is an extremely hard problem. Developers, contributors, and users actually spend significant effort to document, understand, and eventually tune (combinations of) options for meeting a kernel size. In this paper, we investigate how machine learning can help explain what matters for predicting a given Linux kernel size. Unveiling what matters in such very large configuration space is challenging for two reasons: (1) whatever the time we spend on it, we can only build and measure a tiny fraction of possible kernel configurations; (2) the prediction model should be both accurate and interpretable. We compare different machine learning algorithms and demonstrate the benefits of specific feature encoding and selection methods to learn an accurate model that is fast to compute and simple to interpret. Our results are validated over 95,854 kernel configurations and show that we can achieve low prediction errors over a reduced set of options. We also show that we can extract interpretable information for refining documentation and experts' knowledge of Linux, or even assigning more sensible default values to options

Improving web server efficiency on commodity hardware

El ràpid creixement de la Web requereix una gran quantitat de recursos computacionals que han de ser utilitzats eficientment. Avui en dia, els servidors basats en hardware estendard son les plataformes preferides per executar els servidors web, ja que són les plataformes amb millor relació rendiment/cost. El treball presentat en aquesta tesi esta dirigit a millorar la eficàcia en la gestió de recursos dels servidors web actuals. Per assolir els objectius d'aquesta tesis s'ha caracteritzat el funcionament dels servidors web en diverses entorns representatius, per tal de identificar el problemes i coll d'ampolla que limiten el rendiment del servidor web. Amb l'estudi dels servidors web s'ha identificat dos problemes principals que disminueixen l'eficiència dels servidors web en la utilització dels recursos hardware disponibles. El primer problema identificat és la evolució del protocol HTTP per incorporar connexions persistents i seguretat, que disminueix el rendiment e incrementa la complexitat de configuració dels servidors web. El segon problema és la naturalesa de algunes aplicacions web, les quals estan limitades per la memòria física o l'ample de banda amb el disc, que impedeix la correcta utilització dels recursos presents en les maquines multiprocessadors. Per solucionar aquests dos problemes dels servidors web hem proposat dues tècniques. En primer lloc, l'arquitectura hibrida, una evolució de l'arquitectura multi-threaded que es pot implementar fàcilment el els servidor web actuals i que millora notablement la gestió de les connexions i redueix la complexitat de configuració de tot el sistema. En segon lloc, hem implementat en el kernel del sistema operatiu Linux un comprensió de memòria principal per millorar el rendiment de les aplicacions que tenen la memòria com ha coll d'ampolla, millorant així la utilització dels recursos disponibles. Els resultats d'aquesta tesis estan avalats per una avaluació experimental exhaustiva que ha provat la efectivitat i viabilitat de les nostres propostes. Cal destacar que l'arquitectura de servidor web hybrida proposada en aquesta tesis ha estat implementada recentment per coneguts servidors web com és el cas de Apache, Tomcat i Glassfish.The unstoppable growth of the World Wide Web requires a huge amount of computational resources that must be used efficiently. Nowadays, commodity hardware is the preferred platform to run web server systems because it is the most cost-effective solution. The work presented in this thesis aims to improve the efficiency of current web server systems, allowing the web servers to make the most of hardware resources. To this end, we first characterize current web server system and identify the problems that hinder web servers from providing an efficient utilization of resources. From the study of web servers in a wide range of situations and environments, we have identified two main issues that prevents web servers systems from efficiently using current hardware resources. The first is the extension of the HTTP protocol to include connection persistence and security, which dramatically impacts the performance and configuration complexity of traditional multi-threaded web servers. The second is the memory-bounded or disk-bounded nature of some web workloads that prevents the full utilization of the abundant CPU resources available on current commodity hardware. We propose two novel techniques to overcome the main problems with current web server systems. Firstly, we propose a Hybrid web serverarchitecture which can be easily implemented in any multi-threaded web server to improve CPU utilization so as to provide better management of client connections. And secondly, we describe a main memory compression technique implemented in the Linux operating system that makes optimum use of current multiprocessor's hardware, in order to improve the performance of memory bound web applications. The thesis is supported by an exhaustive experimental evaluation that proves the effectiveness and feasibility of our proposals for current systems. It is worth noting that the main concepts behind the Hybrid architecture have recently been implemented in popular web servers like Apache, Tomcat and Glassfish