Towards General and Efficient Online Tuning for Spark

The distributed data analytic system -- Spark is a common choice for processing massive volumes of heterogeneous data, while it is challenging to tune its parameters to achieve high performance. Recent studies try to employ auto-tuning techniques to solve this problem but suffer from three issues: limited functionality, high overhead, and inefficient search. In this paper, we present a general and efficient Spark tuning framework that can deal with the three issues simultaneously. First, we introduce a generalized tuning formulation, which can support multiple tuning goals and constraints conveniently, and a Bayesian optimization (BO) based solution to solve this generalized optimization problem. Second, to avoid high overhead from additional offline evaluations in existing methods, we propose to tune parameters along with the actual periodic executions of each job (i.e., online evaluations). To ensure safety during online job executions, we design a safe configuration acquisition method that models the safe region. Finally, three innovative techniques are leveraged to further accelerate the search process: adaptive sub-space generation, approximate gradient descent, and meta-learning method. We have implemented this framework as an independent cloud service, and applied it to the data platform in Tencent. The empirical results on both public benchmarks and large-scale production tasks demonstrate its superiority in terms of practicality, generality, and efficiency. Notably, this service saves an average of 57.00% memory cost and 34.93% CPU cost on 25K in-production tasks within 20 iterations, respectively

On-the-fly tracing for data-centric computing : parallelization, workflow and applications

As data-centric computing becomes the trend in science and engineering, more and more hardware systems, as well as middleware frameworks, are emerging to handle the intensive computations associated with big data. At the programming level, it is crucial to have corresponding programming paradigms for dealing with big data. Although MapReduce is now a known programming model for data-centric computing where parallelization is completely replaced by partitioning the computing task through data, not all programs particularly those using statistical computing and data mining algorithms with interdependence can be re-factorized in such a fashion. On the other hand, many traditional automatic parallelization methods put an emphasis on formalism and may not achieve optimal performance with the given limited computing resources. In this work we propose a cross-platform programming paradigm, called on-the-fly data tracing , to provide source-to-source transformation where the same framework also provides the functionality of workflow optimization on larger applications. Using a big-data approximation computations related to large-scale data input are identified in the code and workflow and a simplified core dependence graph is built based on the computational load taking in to account big data. The code can then be partitioned into sections for efficient parallelization; and at the workflow level, optimization can be performed by adjusting the scheduling for big-data considerations, including the I/O performance of the machine. Regarding each unit in both source code and workflow as a model, this framework enables model-based parallel programming that matches the available computing resources. The techniques used in model-based parallel programming as well as the design of the software framework for both parallelization and workflow optimization as well as its implementations with multiple programming languages are presented in the dissertation. Then, the following experiments are performed to validate the framework: i) the benchmarking of parallelization speed-up using typical examples in data analysis and machine learning (e.g. naive Bayes, k-means) and ii) three real-world applications in data-centric computing with the framework are also described to illustrate the efficiency: pattern detection from hurricane and storm surge simulations, road traffic flow prediction and text mining from social media data. In the applications, it illustrates how to build scalable workflows with the framework along with performance enhancements

Optimizing the performance of optimization in the cloud environment–An intelligent auto-scaling approach

The cloud computing paradigm has gained wide acceptance in the scientific community, taking a significant share from fields previously reserved exclusively for High Performance Computing (HPC). On-demand access to a large amount of computing resources provided by Cloud makes it ideal for executing large-scale optimizations using evolutionary algorithms without the need for owning any computing infrastructure. In this regard, we extended WoBinGO, an existing parallel software framework for genetic algorithm based optimization, to be used in Cloud. With these extensions, the framework is capable of elastically and frugally utilizing the underlying cloud computing infrastructure for performing computationally expensive fitness evaluations. We studied two issues that are pertinent when dealing with large-scale optimization in the elastic cloud environment: the computing instance launching overhead and the price of engaging Cloud for solving optimization problems, in terms of the instances’ cumulative uptime. To explain the usability limits of WoBinGO framework running in the IaaS environment, a comprehensive analysis of the framework’s performance was given. Optimization of both total optimization time and total cumulative uptime, leads to minimizing the cost of cloud resources utilization. In this way, we are proposing an intelligent decision support engine based on artificial neural networks and metaheuristics to provide the user with an assessment of the framework’s behavior on the underlying infrastructure in terms of optimization duration and the cost of resource consumption. According to a given assessment, the user can decide upon faster delivery of results or lower infrastructure costs. The proposed software framework has been used to solve a complex real-world optimization problem of a subsurface rock mass model calibration. The results obtained from the private OpenStack deployment show that by using the proposed decision support engine, significant savings can be achieved in both optimization time and optimization cost

Learning workload behaviour models from monitored time-series for resource estimation towards data center optimization

In recent years there has been an extraordinary growth of the demand of Cloud Computing resources executed in Data Centers. Modern Data Centers are complex systems that need management. As distributed computing systems grow, and workloads benefit from such computing environments, the management of such systems increases in complexity. The complexity of resource usage and power consumption on cloud-based applications makes the understanding of application behavior through expert examination difficult. The difficulty increases when applications are seen as "black boxes", where only external monitoring can be retrieved. Furthermore, given the different amount of scenarios and applications, automation is required. To deal with such complexity, Machine Learning methods become crucial to facilitate tasks that can be automatically learned from data. Firstly, this thesis proposes an unsupervised learning technique to learn high level representations from workload traces. Such technique provides a fast methodology to characterize workloads as sequences of abstract phases. The learned phase representation is validated on a variety of datasets and used in an auto-scaling task where we show that it can be applied in a production environment, achieving better performance than other state-of-the-art techniques. Secondly, this thesis proposes a neural architecture, based on Sequence-to-Sequence models, that provides the expected resource usage of applications sharing hardware resources. The proposed technique provides resource managers the ability to predict resource usage over time as well as the completion time of the running applications. The technique provides lower error predicting usage when compared with other popular Machine Learning methods. Thirdly, this thesis proposes a technique for auto-tuning Big Data workloads from the available tunable parameters. The proposed technique gathers information from the logs of an application generating a feature descriptor that captures relevant information from the application to be tuned. Using this information we demonstrate that performance models can generalize up to a 34% better when compared with other state-of-the-art solutions. Moreover, the search time to find a suitable solution can be drastically reduced, with up to a 12x speedup and almost equal quality results as modern solutions. These results prove that modern learning algorithms, with the right feature information, provide powerful techniques to manage resource allocation for applications running in cloud environments. This thesis demonstrates that learning algorithms allow relevant optimizations in Data Center environments, where applications are externally monitored and careful resource management is paramount to efficiently use computing resources. We propose to demonstrate this thesis in three areas that orbit around resource management in server environmentsEls Centres de Dades (Data Centers) moderns són sistemes complexos que necessiten ser gestionats. A mesura que creixen els sistemes de computació distribuïda i les aplicacions es beneficien d’aquestes infraestructures, també n’augmenta la seva complexitat. La complexitat que implica gestionar recursos de còmput i d’energia en sistemes de computació al núvol fa difícil entendre el comportament de les aplicacions que s'executen de manera manual. Aquesta dificultat s’incrementa quan les aplicacions s'observen com a "caixes negres", on només es poden monitoritzar algunes mètriques de les caixes de manera externa. A més, degut a la gran varietat d’escenaris i aplicacions, és necessari automatitzar la gestió d'aquests recursos. Per afrontar-ne el repte, l'aprenentatge automàtic juga un paper cabdal que facilita aquestes tasques, que poden ser apreses automàticament en base a dades prèvies del sistema que es monitoritza. Aquesta tesi demostra que els algorismes d'aprenentatge poden aportar optimitzacions molt rellevants en la gestió de Centres de Dades, on les aplicacions són monitoritzades externament i la gestió dels recursos és de vital importància per a fer un ús eficient de la capacitat de còmput d'aquests sistemes. En primer lloc, aquesta tesi proposa emprar aprenentatge no supervisat per tal d’aprendre representacions d'alt nivell a partir de traces d'aplicacions. Aquesta tècnica ens proporciona una metodologia ràpida per a caracteritzar aplicacions vistes com a seqüències de fases abstractes. La representació apresa de fases és validada en diferents “datasets” i s'aplica a la gestió de tasques d'”auto-scaling”, on es conclou que pot ser aplicable en un medi de producció, aconseguint un millor rendiment que altres mètodes de vanguardia. En segon lloc, aquesta tesi proposa l'ús de xarxes neuronals, basades en arquitectures “Sequence-to-Sequence”, que proporcionen una estimació dels recursos usats per aplicacions que comparteixen recursos de hardware. La tècnica proposada facilita als gestors de recursos l’habilitat de predir l'ús de recursos a través del temps, així com també una estimació del temps de còmput de les aplicacions. Tanmateix, redueix l’error en l’estimació de recursos en comparació amb d’altres tècniques populars d'aprenentatge automàtic. Per acabar, aquesta tesi introdueix una tècnica per a fer “auto-tuning” dels “hyper-paràmetres” d'aplicacions de Big Data. Consisteix així en obtenir informació dels “logs” de les aplicacions, generant un vector de característiques que captura informació rellevant de les aplicacions que s'han de “tunejar”. Emprant doncs aquesta informació es valida que els ”Regresors” entrenats en la predicció del rendiment de les aplicacions són capaços de generalitzar fins a un 34% millor que d’altres “Regresors” de vanguàrdia. A més, el temps de cerca per a trobar una bona solució es pot reduir dràsticament, aconseguint un increment de millora de fins a 12 vegades més dels resultats de qualitat en contraposició a alternatives modernes. Aquests resultats posen de manifest que els algorismes moderns d'aprenentatge automàtic esdevenen tècniques molt potents per tal de gestionar l'assignació de recursos en aplicacions que s'executen al núvol.Arquitectura de computador

Automatic physical layer tuning of mapreduce-based query processing engines

Orientador: Eduardo Cunha de AlmeidaTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 29/06/2020Inclui referências: p. 98-109Área de concentração: Ciência da ComputaçãoResumo: A crescente necessidade de processar grandes quantidades de dados semi-estruturados e nãoestruturados levou ao desenvolvimento de mecanismos de processamento especializados como o MapReduce. O MapReduce é um modelo de programação projetado para processar grandes quantidades de dados semiestruturados de maneira distribuída e paralela. Os sistemas SQLon-Hadoop são interfaces SQL construídas sobre os mecanismos de processamento baseados em MapReduce para consultar grandes quantidades de dados semi-estruturados. No entanto, o número de máquinas, o número de sistemas na pilha de software e os mecanismos de controle fornecidos pelos mecanismos do MapReduce aumentam a complexidade e os custos operacionais de um cluster SQL-on-Hadoop. O aumento do desempenho dos motores de processamento MapReduce é um fator chave que pode ser alcançado delegando a quantidade certa de recursos físicos para suas tarefas. No entanto, usuários e até administradores especializados lutam para entender e ajustar as tarefas MapReduce para obter um desempenho melhor. A falta de conhecimento para ajustar as tarefas MapReduce deu origem a uma linha de pesquisa bem-sucedida sobre o ajuste automático dos parâmetros do MapReduce, originando vários Orientadores de Ajuste. No entanto, o problema de ajustar automaticamente as consultas SQL-no-Hadoop permanece amplamente inexplorado, pois a abordagem atual da aplicação dos Orientadores de Ajuste projetados para MapReduce em consultas SQL-on-Hadoop acarreta em vários problemas. Por exemplo, o processador de consultas do Hive, um sistema SQL-on-Hadoop popular, traduz consultas HiveQL em grafos de tarefas MapReduce, e seria fácil supor que, ajustando as configurações do motor de processamento MapReduce, as consultas HiveQL também se beneficiariam. Entretanto, essa suposição não se aplica quando os Orientadores de Ajuste existentes são aplicados ingenuamente às consultas HiveQL devido a arquitetura do Hive, Hadoop e dos Orientadores de Ajuste. Nesta tese tratamos da questão de como ajustar corretamente as consultas SQL-no-Hadoop. Por "corretamente", entendemos que, ao ajustar as configurações das consultas SQL-no-Hadoop, a geração das configurações deve considerar várias características que estão presentes apenas em tarefas geradas pelos sistemas SQL-no-Hadoop. Essas características incluem: (i) no caso de consultas individuais, todas as tarefas MapReduce que constituem o plano de consulta desta consulta são executadas com configurações idênticas. (ii) apesar da busca e geração das configurações de ajuste serem realizadas para cada tarefa MapReduce, apenas uma configuração de ajuste é selecionada e aplicada à consulta e as demais configurações de ajuste são simplesmente descartadas. (iii) Os Orientadores de Ajuste do Hadoop tratam as funções do MapReduce como caixas-pretas e fazem suposições de modelagem simplificadoras que podem valer para tarefas clássicas do MapReduce (Sort, Grep), mas não são verdadeiras para consultas do tipo SQL como o HiveQL, onde as tarefas contêm vários operadores de álgebra relacional como junções e agregadores. Estendemos o processador de consultas do Hive para ajustar as consultas SQL-no-Hadoop. Esta extensão compreende uma abordagem chamada de ajuste não-uniforme que permite que os sistemas SQL-on-Hadoop tenham um controle mais refinado da configuração das consultas, onde cada tarefa MapReduce recebe uma configuração especializada. Apresentamos um modelo conceitual, chamado assinatura de código, que usa informações estáticas disponíveis antes da execução de cada tafera para mapear tarefas que tenham padrões de consumo de recursos similares. Também apresentamos um cache que armazena configurações de ajuste, geradas por algum Orientadore de Ajuste, e as recicla entre tarefas que possuem consumo de recursos semelhantes. Nossa extensão funciona em conjunto como uma solução única para o ajuste automático de consultas SQL-no-Hadoop. Para validar nossa solução, realizamos um estudo experimental focado no Hive executando sobre o Hadoop porque (i) O Hive é um bom representante dos sistemas SQL-on-Hadoop nativos (como o System-R fez para os sistemas de bancos de dados relacionais); (ii) o Hive e o Hadoop são altamente populares para processamento analítico; e (iii) O ajuste de parâmetros do Hadoop foi estudado extensivamente nos últimos anos. Para preencher o cache de ajuste, empregamos o Starfish, o primeiro Orientador de Ajuste baseado em custo que encontra configurações (quase) ótimas e é o único Orientador de Ajuste disponível ao público para fins de pesquisa acadêmica. Em nossos experimentos, apresentamos que as consultas otimizadas com nossa abordagem de ajuste apresentaram acelerações de até 25%, contrastando com a abordagem atual que degradou o desempenho em várias ocasiões. Especificamente, a abordagem atual de ajuste pode causar variações no tempo de execução entre -171% e 27% em relação à configuração padrão. Mais importante ainda, nosso método de ajuste leva a uma melhor utilização de recursos, diminuindo o uso da CPU e a paginação de memória em até 40%. Nossa abordagem também reduziu a quantidade total de dados gravados em discos em 5×. Nossa abordagem de ajuste tem um cache usado para evitar a recriação de perfis de tarefas MapReduce semelhantes. Nosso cache reduziu a geração de perfils em 50% para a carga de trabalho TPC-H, permitindo até o ajuste parcial de consultas ad-hoc antes de sua execução. Palavras-chave: Sintonia da camada física. Processamento de consulta em MapReduce. SQL-On-Hadoop.Abstract: The increasing need to process large amounts of semi- and non-structured data has led to the development of specialized processing engines like MapReduce. MapReduce is a programming model designed to process large-scale semi-structured data in a distributed and parallel fashion. SQL-on-Hadoop systems are SQL-like interfaces build on top of MapReduce processing engines to query semi-structured data in large-scale. However, the number of computing nodes, the number of systems in the software stack, and the controlling mechanisms provided by MapReduce engines increase the complexity and the operational costs of maintaining a large SQL-on-Hadoop cluster. Increasing performance of such engines is a key factor that can be achieved by delegating the right amount of physical resources. Yet, regular users and even expert administrators struggle to understand and tune MapReduce jobs to achieve good performance. This skill gap has given rise to a successful line of research on automatically tuning MapReduce parameters, originating several tuning advisors. Yet, the problem of automatically tuning SQL-on-Hadoop queries remains largely unexplored today as the current approach of applying MapReduce tuning advisors direct to SQL-on-Hadoop queries entail a number of problems. For instance, the Hive SQL-on-Hadoop engine compiles HiveQL queries into a workflow of MapReduce jobs, and it would be straightforward to assume that by tuning the underlying Hadoop processing engine, HiveQL queries would benefit as well. However, this assumption does not hold when existing tuning advisors are naively applied to HiveQL queries due to the design choices of Hive, Hadoop, and the tuning advisors. This thesis addresses the question of how to properly tune SQL-on-Hadoop queries? By "properly" we mean, when tuning SQL-on-Hadoop queries, the generation of the tuning setups has to consider several characteristics that are only present in jobs generated by SQL-on-Hadoop systems. These characteristics include: (i) at the level of individual queries, all MapReduce jobs that constitute a query plan are executed with identical configuration settings. (ii) despite profiling and search heuristics being performed in a job-basis to generate tuning setups, only one tuning setup is applied to the query and the remaining tuning setups are simply discarded. (iii) Hadoop tuning advisors treat the MapReduce functions as black boxes and make simplifying modeling assumptions that may hold for classical MapReduce jobs (Sort, Grep), but they are not true for SQL-like queries like HiveQL where jobs contain multiple relational algebra operators like joins and aggregators. We extended the Hive query processor for tune SQL-on-Hadoop queries. This extension comprises an approach called non-uniform tuning that enables SQL-on-Hadoop systems to have a fine-grained control for tuning queries, where jobs receive specialized tuning setups. We present a conceptual model, called code-signature, that uses static information available upfront execution to match jobs with similar resource consumption patterns. We also present a tuning cache that stores tuning setups, generated by third part tuning advisors, and recycle them between jobs that have the similar resource consumption. The extension works together as a single solution for automatic tuning of SQL-on-Hadoop queries. In order to validate our solution, we conduct an experimental study focused on Hive over Hadoop because (i) Hive is a good representative of native SQL-on-Hadoop systems (like System-R did for relational database systems); (ii) both Hive and Hadoop are highly popular for analytical processing; and (iii) Hadoop parameter tuning has been studied extensively in recent years. For populate the Tuning Cache, we employ Starfish, the first cost-based optimizer for finding (near-) optimal configuration parameter settings and the only publicly available tuning advisor for academic research purposes. In our experiments, we present that queries optimized with our tuning approach always presented positive speed ups up to 25%, contrasting the current approach that degraded performance in several occasions. Specifically, the current tuning approach can cause variations in the execution run time between -171% and 27% over default configuration. Most importantly, our tuning method leads to considerable better resource utilization, decreasing CPU usage and Memory paging over 40%. Also reducing the total amount of data written to disks in 5×. Our tuning approach has a Tuning Cache used to avoid reprofiling similar jobs. Our Tuning Cache reduced the profilings in 50% for TPC-H queries, enabling upfront tuning of ad-hoc queries. Keywords: Physical-layer tuning. MapReduce query processing. SQL-On-Hadoop

Black or White? How to Develop an AutoTuner for Memory-based Analytics [Extended Version]

There is a lot of interest today in building autonomous (or, self-driving) data processing systems. An emerging school of thought is to leverage AI-driven "black box" algorithms for this purpose. In this paper, we present a contrarian view. We study the problem of autotuning the memory allocation for applications running on modern distributed data processing systems. For this problem, we show that an empirically-driven "white-box" algorithm, called RelM, that we have developed provides a close-to-optimal tuning at a fraction of the overheads compared to state-of-the-art AI-driven "black box" algorithms, namely, Bayesian Optimization (BO) and Deep Distributed Policy Gradient (DDPG). The main reason for RelM's superior performance is that the memory management in modern memory-based data analytics systems is an interplay of algorithms at multiple levels: (i) at the resource-management level across various containers allocated by resource managers like Kubernetes and YARN, (ii) at the container level among the OS, pods, and processes such as the Java Virtual Machine (JVM), (iii) at the application level for caching, aggregation, data shuffles, and application data structures, and (iv) at the JVM level across various pools such as the Young and Old Generation. RelM understands these interactions and uses them in building an analytical solution to autotune the memory management knobs. In another contribution, called GBO, we use the RelM's analytical models to speed up Bayesian Optimization. Through an evaluation based on Apache Spark, we showcase that RelM's recommendations are significantly better than what commonly-used Spark deployments provide, and are close to the ones obtained by brute-force exploration; while GBO provides optimality guarantees for a higher, but still significantly lower compared to the state-of-the-art AI-driven policies, cost overhead.Comment: Main version in ACM SIGMOD 202

Artificial intelligence driven anomaly detection for big data systems

The main goal of this thesis is to contribute to the research on automated performance anomaly detection and interference prediction by implementing Artificial Intelligence (AI) solutions for complex distributed systems, especially for Big Data platforms within cloud computing environments. The late detection and manual resolutions of performance anomalies and system interference in Big Data systems may lead to performance violations and financial penalties. Motivated by this issue, we propose AI-based methodologies for anomaly detection and interference prediction tailored to Big Data and containerized batch platforms to better analyze system performance and effectively utilize computing resources within cloud environments. Therefore, new precise and efficient performance management methods are the key to handling performance anomalies and interference impacts to improve the efficiency of data center resources. The first part of this thesis contributes to performance anomaly detection for in-memory Big Data platforms. We examine the performance of Big Data platforms and justify our choice of selecting the in-memory Apache Spark platform. An artificial neural network-driven methodology is proposed to detect and classify performance anomalies for batch workloads based on the RDD characteristics and operating system monitoring metrics. Our method is evaluated against other popular machine learning algorithms (ML), as well as against four different monitoring datasets. The results prove that our proposed method outperforms other ML methods, typically achieving 98–99% F-scores. Moreover, we prove that a random start instant, a random duration, and overlapped anomalies do not significantly impact the performance of our proposed methodology. The second contribution addresses the challenge of anomaly identification within an in-memory streaming Big Data platform by investigating agile hybrid learning techniques. We develop TRACK (neural neTwoRk Anomaly deteCtion in sparK) and TRACK-Plus, two methods to efficiently train a class of machine learning models for performance anomaly detection using a fixed number of experiments. Our model revolves around using artificial neural networks with Bayesian Optimization (BO) to find the optimal training dataset size and configuration parameters to efficiently train the anomaly detection model to achieve high accuracy. The objective is to accelerate the search process for finding the size of the training dataset, optimizing neural network configurations, and improving the performance of anomaly classification. A validation based on several datasets from a real Apache Spark Streaming system is performed, demonstrating that the proposed methodology can efficiently identify performance anomalies, near-optimal configuration parameters, and a near-optimal training dataset size while reducing the number of experiments up to 75% compared with naïve anomaly detection training. The last contribution overcomes the challenges of predicting completion time of containerized batch jobs and proactively avoiding performance interference by introducing an automated prediction solution to estimate interference among colocated batch jobs within the same computing environment. An AI-driven model is implemented to predict the interference among batch jobs before it occurs within system. Our interference detection model can alleviate and estimate the task slowdown affected by the interference. This model assists the system operators in making an accurate decision to optimize job placement. Our model is agnostic to the business logic internal to each job. Instead, it is learned from system performance data by applying artificial neural networks to establish the completion time prediction of batch jobs within the cloud environments. We compare our model with three other baseline models (queueing-theoretic model, operational analysis, and an empirical method) on historical measurements of job completion time and CPU run-queue size (i.e., the number of active threads in the system). The proposed model captures multithreading, operating system scheduling, sleeping time, and job priorities. A validation based on 4500 experiments based on the DaCapo benchmarking suite was carried out, confirming the predictive efficiency and capabilities of the proposed model by achieving up to 10% MAPE compared with the other models.Open Acces