ALOJA-ML: a framework for automating characterization and knowledge discovery in Hadoop deployments

This article presents ALOJA-Machine Learning (ALOJA-ML) an extension to the ALOJA project that uses machine learning techniques to interpret Hadoop benchmark performance data and performance tuning; here we detail the approach, efficacy of the model and initial results. The ALOJA-ML project is the latest phase of a long-term collaboration between BSC and Microsoft, to automate the characterization of cost-effectiveness on Big Data deployments, focusing on Hadoop. Hadoop presents a complex execution environment, where costs and performance depends on a large number of software (SW) configurations and on multiple hardware (HW) deployment choices. Recently the ALOJA project presented an open, vendor-neutral repository, featuring over 16.000 Hadoop executions. These results are accompanied by a test bed and tools to deploy and evaluate the cost-effectiveness of the different hardware configurations, parameter tunings, and Cloud services. Despite early success within ALOJA from expert-guided benchmarking, it became clear that a genuinely comprehensive study requires automation of modeling procedures to allow a systematic analysis of large and resource-constrained search spaces. ALOJA-ML provides such an automated system allowing knowledge discovery by modeling Hadoop executions from observed benchmarks across a broad set of configuration parameters. The resulting empirically-derived performance models can be used to forecast execution behavior of various workloads; they allow a-priori prediction of the execution times for new configurations and HW choices and they offer a route to model-based anomaly detection. In addition, these models can guide the benchmarking exploration efficiently, by automatically prioritizing candidate future benchmark tests. Insights from ALOJA-ML's models can be used to reduce the operational time on clusters, speed-up the data acquisition and knowledge discovery process, and importantly, reduce running costs. In addition to learning from the methodology presented in this work, the community can benefit in general from ALOJA data-sets, framework, and derived insights to improve the design and deployment of Big Data applications.This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 re- search and innovation programme (grant agreement No 639595). This work is partially supported by the Ministry of Economy of Spain under contracts TIN2012-34557 and 2014SGR105Peer ReviewedPostprint (published version

A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning

Recently using machine learning (ML) based techniques to optimize modern database management systems has attracted intensive interest from both industry and academia. With an objective to tune a specific component of a DBMS (e.g., index selection, knobs tuning), the ML-based tuning agents have shown to be able to find better configurations than experienced database administrators. However, one critical yet challenging question remains unexplored -- how to make those ML-based tuning agents work collaboratively. Existing methods do not consider the dependencies among the multiple agents, and the model used by each agent only studies the effect of changing the configurations in a single component. To tune different components for DBMS, a coordinating mechanism is needed to make the multiple agents cognizant of each other. Also, we need to decide how to allocate the limited tuning budget among the agents to maximize the performance. Such a decision is difficult to make since the distribution of the reward for each agent is unknown and non-stationary. In this paper, we study the above question and present a unified coordinating framework to efficiently utilize existing ML-based agents. First, we propose a message propagation protocol that specifies the collaboration behaviors for agents and encapsulates the global tuning messages in each agent's model. Second, we combine Thompson Sampling, a well-studied reinforcement learning algorithm with a memory buffer so that our framework can allocate budget judiciously in a non-stationary environment. Our framework defines the interfaces adapted to a broad class of ML-based tuning agents, yet simple enough for integration with existing implementations and future extensions. We show that it can effectively utilize different ML-based agents and find better configurations with 1.4~14.1X speedups on the workload execution time compared with baselines.Comment: Accepted at 2023 International Conference on Management of Data (SIGMOD '23

A Research on Automatic Hyperparameter Recommendation via Meta-Learning

The performance of classification algorithms is mainly governed by the hyperparameter configurations deployed. Traditional search-based algorithms tend to require extensive hyperparameter evaluations to select the desirable configurations during the process, and they are often very inefficient for implementations on large-scale tasks. In this dissertation, we resort to solving the problem of hyperparameter selection via meta-learning which provides a mechanism that automatically recommends the promising ones without any inefficient evaluations. In its approach, a meta-learner is constructed on the metadata extracted from historical classification problems which directly determines the success of recommendations. Designing fine meta-learners to recommend effective hyperparameter configurations efficiently is of practical importance. This dissertation divides into six chapters: the first chapter presents the research background and related work, the second to the fifth chapters detail our main work and contributions, and the sixth chapter concludes the dissertation and pictures our possible future work. In the second and third chapters, we propose two (kernel) multivariate sparse-group Lasso (SGLasso) approaches for automatic meta-feature selection. Previously, meta-features were usually picked by researchers manually based on their preferences and experience or by wrapper method, which is either less effective or time-consuming. SGLasso, as an embedded feature selection model, can select the most effective meta-features during the meta-learner training and thus guarantee the optimality of both meta-features and meta-learner which are essential for successful recommendations. In the fourth chapter, we formulate the problem of hyperparameter recommendation as a problem of low-rank tensor completion. The hyperparameter search space was often stretched to a one-dimensional vector, which removes the spatial structure of the search space and ignores the correlations that existed between the adjacent hyperparameters and these characteristics are crucial in meta-learning. Our contributions are to instantiate the search space of hyperparameters as a multi-dimensional tensor and develop a novel kernel tensor completion algorithm that is applied to estimate the performance of hyperparameter configurations. In the fifth chapter, we propose to learn the latent features of performance space via denoising autoencoders. Although the search space is usually high-dimensional, the performance of hyperparameter configurations is usually correlated to each other to a certain degree and its main structure lies in a much lower-dimensional manifold that describes the performance distribution of the search space. Denoising autoencoders are applied to extract the latent features on which two effective recommendation strategies are built. Extensive experiments are conducted to verify the effectiveness of our proposed approaches, and various empirical outcomes have shown that our approaches can recommend promising hyperparameters for real problems and significantly outperform the state-of-the-art meta-learning-based methods as well as search algorithms such as random search, Bayesian optimization, and Hyperband