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Automation in Software Performance Engineering Based on a Declarative Specification of Concerns
Abstract and Figures
Software performance is of particular relevance to software system design, operation, and evolution because it has a significant impact on key business indicators. During the life-cycle of a software system, its implementation, configuration, and deployment are subject to multiple changes that may affect the end-to-end performance characteristics. Consequently, performance analysts continually need to provide answers to and act based on performance-relevant concerns. To ensure a desired level of performance, software performance engineering provides a plethora of methods, techniques, and tools for measuring, modeling, and evaluating performance properties of software systems. However, the answering of performance concerns is subject to a significant semantic gap between the level on which performance concerns are formulated and the technical level on which performance evaluations are actually conducted. Performance evaluation approaches come with different strengths and limitations concerning, for example, accuracy, time-to-result, or system overhead. For the involved stakeholders, it can be an elaborate process to reasonably select, parameterize and correctly apply performance evaluation approaches, and to filter and interpret the obtained results. An additional challenge is that available performance evaluation artifacts may change over time, which requires to switch between different measurement-based and model-based performance evaluation approaches during the system evolution. At model-based analysis, the effort involved in creating performance models can also outweigh their benefits. To overcome the deficiencies and enable an automatic and holistic evaluation of performance throughout the software engineering life-cycle requires an approach that: (i) integrates multiple types of performance concerns and evaluation approaches, (ii) automates performance model creation, and (iii) automatically selects an evaluation methodology tailored to a specific scenario. This thesis presents a declarative approach —called Declarative Performance Engineering (DPE)— to automate performance evaluation based on a humanreadable specification of performance-related concerns. To this end, we separate the definition of performance concerns from their solution. The primary scientific contributions presented in this thesis are: A declarative language to express performance-related concerns and a corresponding processing framework: We provide a language to specify performance concerns independent of a concrete performance evaluation approach. Besides the specification of functional aspects, the language allows to include non-functional tradeoffs optionally. To answer these concerns, we provide a framework architecture and a corresponding reference implementation to process performance concerns automatically. It allows to integrate arbitrary performance evaluation approaches and is accompanied by reference implementations for model-based and measurement-based performance evaluation. Automated creation of architectural performance models from execution traces: The creation of performance models can be subject to significant efforts outweighing the benefits of model-based performance evaluation. We provide a model extraction framework that creates architectural performance models based on execution traces, provided by monitoring tools.The framework separates the derivation of generic information from model creation routines. To derive generic information, the framework combines state-of-the-art extraction and estimation techniques. We isolate object creation routines specified in a generic model builder interface based on concepts present in multiple performance-annotated architectural modeling formalisms. To create model extraction for a novel performance modeling formalism, developers only need to write object creation routines instead of creating model extraction software from scratch when reusing the generic framework. Automated and extensible decision support for performance evaluation approaches: We present a methodology and tooling for the automated selection of a performance evaluation approach tailored to the user concerns and application scenario. To this end, we propose to decouple the complexity of selecting a performance evaluation approach for a given scenario by providing solution approach capability models and a generic decision engine. The proposed capability meta-model enables to describe functional and non-functional capabilities of performance evaluation approaches and tools at different granularities. In contrast to existing tree-based decision support mechanisms, the decoupling approach allows to easily update characteristics of solution approaches as well as appending new rating criteria and thereby stay abreast of evolution in performance evaluation tooling and system technologies. Time-to-result estimation for model-based performance prediction: The time required to execute a model-based analysis plays an important role in different decision processes. For example, evaluation scenarios might require the prediction results to be available in a limited period of time such that the system can be adapted in time to ensure the desired quality of service. We propose a method to estimate the time-to-result for modelbased performance prediction based on model characteristics and analysis parametrization. We learn a prediction model using performancerelevant features thatwe determined using statistical tests. We implement the approach and demonstrate its practicability by applying it to analyze a simulation-based multi-step performance evaluation approach for a representative architectural performance modeling formalism. We validate each of the contributions based on representative case studies. The evaluation of automatic performance model extraction for two case study systems shows that the resulting models can accurately predict the performance behavior. Prediction accuracy errors are below 3% for resource utilization and mostly less than 20% for service response time. The separate evaluation of the reusability shows that the presented approach lowers the implementation efforts for automated model extraction tools by up to 91%. Based on two case studies applying measurement-based and model-based performance evaluation techniques, we demonstrate the suitability of the declarative performance engineering framework to answer multiple kinds of performance concerns customized to non-functional goals. Subsequently, we discuss reduced efforts in applying performance analyses using the integrated and automated declarative approach. Also, the evaluation of the declarative framework reviews benefits and savings integrating performance evaluation approaches into the declarative performance engineering framework. We demonstrate the applicability of the decision framework for performance evaluation approaches by applying it to depict existing decision trees. Then, we show how we can quickly adapt to the evolution of performance evaluation methods which is challenging for static tree-based decision support systems. At this, we show how to cope with the evolution of functional and non-functional capabilities of performance evaluation software and explain how to integrate new approaches. Finally, we evaluate the accuracy of the time-to-result estimation for a set of machinelearning algorithms and different training datasets. The predictions exhibit a mean percentage error below 20%, which can be further improved by including performance evaluations of the considered model into the training data. The presented contributions represent a significant step towards an integrated performance engineering process that combines the strengths of model-based and measurement-based performance evaluation. The proposed performance concern language in conjunction with the processing framework significantly reduces the complexity of applying performance evaluations for all stakeholders. Thereby it enables performance awareness throughout the software engineering life-cycle. The proposed performance concern language removes the semantic gap between the level on which performance concerns are formulated and the technical level on which performance evaluations are actually conducted by the user.
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... One of the main properties of Baloo is that it relies on real benchmarking measurements. This is a conscious choice, as the theoretical and the measured performance of a cloud system might substantially differ [SD17;Wal19]. However, the downsides of the required measurement series are the increased cost implications and time investments. ...
... Deriving confidence values for model predictions All of the developed models represent an abstracted view of reality. Naturally, all modeling approaches introduce a certain amount of prediction error and uncertainty [Wal19]. Therefore, many users are reluctant to trust the predictions of performance models blindly. ...
One consequence of the recent coronavirus pandemic is increased demand and use of online services around the globe. At the same time, performance requirements for modern technologies are becoming more stringent as users become accustomed to higher standards. These increased performance and availability requirements, coupled with the unpredictable usage growth, are driving an increasing proportion of applications to run on public cloud platforms as they promise better scalability and reliability. With data centers already responsible for about one percent of the world's power consumption, optimizing resource usage is of paramount importance. Simultaneously, meeting the increasing and changing resource and performance requirements is only possible by optimizing resource management without introducing additional overhead. This requires the research and development of new modeling approaches to understand the behavior of running applications with minimal information. However, the emergence of modern software paradigms makes it increasingly difficult to derive such models and renders previous performance modeling techniques infeasible. Modern cloud applications are often deployed as a collection of fine-grained and interconnected components called microservices. Microservice architectures offer massive benefits but also have broad implications for the performance characteristics of the respective systems. In addition, the microservices paradigm is typically paired with a DevOps culture, resulting in frequent application and deployment changes. Such applications are often referred to as cloud-native applications. In summary, the increasing use of ever-changing cloud-hosted microservice applications introduces a number of unique challenges for modeling the performance of modern applications. These include the amount, type, and structure of monitoring data, frequent behavioral changes, or infrastructure variabilities. This violates common assumptions of the state of the art and opens a research gap for our work. In this thesis, we present five techniques for automated learning of performance models for cloud-native software systems. We achieve this by combining machine learning with traditional performance modeling techniques. Unlike previous work, our focus is on cloud-hosted and continuously evolving microservice architectures, so-called cloud-native applications. Therefore, our contributions aim to solve the above challenges to deliver automated performance models with minimal computational overhead and no manual intervention. Depending on the cloud computing model, privacy agreements, or monitoring capabilities of each platform, we identify different scenarios where performance modeling, prediction, and optimization techniques can provide great benefits. Specifically, the contributions of this thesis are as follows: Monitorless: Application-agnostic prediction of performance degradations. To manage application performance with only platform-level monitoring, we propose Monitorless, the first truly application-independent approach to detecting performance degradation. We use machine learning to bridge the gap between platform-level monitoring and application-specific measurements, eliminating the need for application-level monitoring. Monitorless creates a single and holistic resource saturation model that can be used for heterogeneous and untrained applications. Results show that Monitorless infers resource-based performance degradation with 97% accuracy. Moreover, it can achieve similar performance to typical autoscaling solutions, despite using less monitoring information. SuanMing: Predicting performance degradation using tracing. We introduce SuanMing to mitigate performance issues before they impact the user experience. This contribution is applied in scenarios where tracing tools enable application-level monitoring. SuanMing predicts explainable causes of expected performance degradations and prevents performance degradations before they occur. Evaluation results show that SuanMing can predict and pinpoint future performance degradations with an accuracy of over 90%. SARDE: Continuous and autonomous estimation of resource demands. We present SARDE to learn application models for highly variable application deployments. This contribution focuses on the continuous estimation of application resource demands, a key parameter of performance models. SARDE represents an autonomous ensemble estimation technique. It dynamically and continuously optimizes, selects, and executes an ensemble of approaches to estimate resource demands in response to changes in the application or its environment. Through continuous online adaptation, SARDE efficiently achieves an average resource demand estimation error of 15.96% in our evaluation. DepIC: Learning parametric dependencies from monitoring data. DepIC utilizes feature selection techniques in combination with an ensemble regression approach to automatically identify and characterize parametric dependencies. Although parametric dependencies can massively improve the accuracy of performance models, DepIC is the first approach to automatically learn such parametric dependencies from passive monitoring data streams. Our evaluation shows that DepIC achieves 91.7% precision in identifying dependencies and reduces the characterization prediction error by 30% compared to the best individual approach. Baloo: Modeling the configuration space of databases. To study the impact of different configurations within distributed DBMSs, we introduce Baloo. Our last contribution models the configuration space of databases considering measurement variabilities in the cloud. More specifically, Baloo dynamically estimates the required benchmarking measurements and automatically builds a configuration space model of a given DBMS. Our evaluation of Baloo on a dataset consisting of 900 configuration points shows that the framework achieves a prediction error of less than 11% while saving up to 80% of the measurement effort. Although the contributions themselves are orthogonally aligned, taken together they provide a holistic approach to performance management of modern cloud-native microservice applications. Our contributions are a significant step forward as they specifically target novel and cloud-native software development and operation paradigms, surpassing the capabilities and limitations of previous approaches. In addition, the research presented in this paper also has a significant impact on the industry, as the contributions were developed in collaboration with research teams from Nokia Bell Labs, Huawei, and Google. Overall, our solutions open up new possibilities for managing and optimizing cloud applications and improve cost and energy efficiency.
... Analytic solutions are usually much faster to compute; however, they might not be able to provide the same detail as a simulation or are only applicable to certain scenarios or system types. Therefore, a variety of approaches have been developed to enable dynamically switching between different methods (see, e.g., Walter et al. [116,117] for computer systems or Rygielski et al. [118,119] for networks) based on the current demand, or alternatively altering the prediction model itself [120,121]. These approaches accomplish this by working on a higher abstraction layer, e.g., the white-box descriptions of Section 5.2.3. ...
Failure prediction is an important aspect of self-aware computing systems. Therefore, a multitude of different approaches has been proposed in the literature over the past few years. In this work, we propose a taxonomy for organizing works focusing on the prediction of Service Level Objective (SLO) failures. Our taxonomy classifies related work along the dimensions of the prediction target (e.g., anomaly detection, performance prediction, or failure prediction), the time horizon (e.g., detection or prediction, online or offline application), and the applied modeling type (e.g., time series forecasting, machine learning, or queueing theory). The classification is derived based on a systematic mapping of relevant papers in the area. Additionally, we give an overview of different techniques in each sub-group and address remaining challenges in order to guide future research.
This chapter looks at the different measurement strategies techniques that can be used in practice to derive the values of common metrics, including event-driven, tracing, sampling, and indirect measurement. While most presented techniques are useful for performance metrics, some of them can also be applied generally for other types of metrics. The chapter is wrapped up with an overview of commercial and open-source monitoring tools for performance profiling and call path tracing.
DevOps is a modern software engineering paradigm that is gaining widespread adoption in industry. The goal of DevOps is to bring software changes into production with a high frequency and fast feedback cycles. This conflicts with software quality assurance activities, particularly with respect to performance. For instance, performance evaluation activities --- such as load testing --- require a considerable amount of time to get statistically significant results. We conducted an industrial survey to get insights into how performance is addressed in industrial DevOps settings. In particular, we were interested in the frequency of executing performance evaluations, the tools being used, the granularity of the obtained performance data, and the use of model-based techniques. The survey responses, which come from a wide variety of participants from different industry sectors, indicate that the complexity of performance engineering approaches and tools is a barrier for wide-spread adoption of performance analysis in DevOps. The implication of our results is that performance analysis tools need to have a short learning curve, and should be easy to integrate into the DevOps pipeline in order to be adopted by practitioners.
Performance predictions enable software architects to optimize the performance of a software system early in the development cycle. Architectural performance models and statistical response time models are commonly used to derive these performance predictions. However, both methods have significant downsides: Statistical response time models can only predict scenarios for which training data is available, making the prediction of previously unseen system configurations infeasible. In contrast, the time required to simulate an architectural performance model increases exponentially with both system size and level of modeling detail, making the analysis of large, detailed models challenging. Existing approaches use statistical response time models in architectural performance models to avoid modeling subsystems that are difficult or time-consuming to model, yet they do not consider simulation time. In this paper, we propose to model software systems using classical queuing theory and statistical response time models in parallel. This approach allows users to tailor the model for each analysis run, based on the performed adaptations and the requested performance metrics. Our approach enables faster model solution compared to traditional performance models while retaining their ability to predict previously unseen scenarios. In our experiments we observed speedups of up to 94.8%, making the analysis of much larger and more detailed systems feasible.
DDoS attacks are becoming increasingly frequent and violent. A typical type of attack is the TCP SYN flood, inhibiting a server from opening new TCP connections. Current countermeasures to this attack introduce inefficiencies by either reducing computing resources on the service host or creating new network bottlenecks. In this work, we present a novel approach to mitigate TCP SYN flood attacks using software-defined networking. We perform an initial evaluation of a proof-of-concept implementation that exhibits performance measures close to existing countermeasures while circumventing their inefficiencies.
Model-based performance analysis can be lever-aged to explore performance properties of software systems. To capture the behavior of varying workload mixes, configurations , and deployments of a software system requires formal modeling of the impact of configuration parameters and user input on the system behavior. Such influences are represented as parametric dependencies in software performance models. Existing modeling approaches focus on modeling paramet-ric dependencies at design-time. This paper identifies run-time specific parametric dependency features, which are not supported by existing work. Therefore, this paper proposes a novel modeling methodology for parametric dependencies and a corresponding graph-based resolution algorithm. This algorithm enables the solution of models containing component instance-level dependencies, variables with multiple descriptions in parallel, and correlations modeled as parametric dependencies. We integrate our work into the Descartes Modeling Language (DML), allowing for accurate and efficient modeling and analysis of parametric dependencies. These performance predictions are valuable for various purposes such as capacity planning, bottleneck analysis, configuration optimization and proactive auto-scaling. Our evaluation analyzes a video store application. The prediction for varying language mixes and video sizes shows a mean error below 5% for utilization and below 10% for response time.
Performance models are necessary components of self-aware computing systems, as they allow such systems to reason about their own state and behavior. Research in this field has developed a multitude of approaches to create, maintain, and solve performance models. In this paper, we propose a meta-self-aware computing approach making the processes of model creation, maintenance and solution themselves self-aware. This enables the automated selection and adaption of software performance engineering approaches specifically tailored to the system under study.
Performance is of particular relevance to software system design, operation, and evolution. However, the application of performance engineering approaches to solve a given user concern is challenging and requires expert knowledge. In this tutorial paper, we guide the reader step-by-step through the answering of performance concerns following the idea of declarative performance engineering. We explain tools available online, which can be used for automating huge parts of the software performance engineering process. In particular, we present a performance concern language, for which we provide automated answering and visualization referring to measurement-based and model-based analysis. We also detail how to derive performance models using automated extraction of architectural performance models and modeling of parametric dependencies.
Services provided online are subject to various types of attacks. Security appliances can be chained to protect a system against multiple types of network attacks. The sequence of appliances has a significant impact on the efficiency of the whole chain. While the operation of security appliance chains is currently based on a static order, traffic-aware reordering of security appliances may significantly improve efficiency and accuracy. In this paper, we present the vision of a self-aware system to automatically reorder security appliances according to incoming traffic. To achieve this, we propose to apply a model-based learning, reasoning, and acting (LRA-M) loop. To this end, we describe a corresponding system architecture and explain its building blocks.
Software performance engineering (SPE) provides a plethora of methods and tooling for measuring, modeling, and evaluating performance properties of software systems. The solution approaches come with different strengths and limitations concerning, for example, accuracy, time-to-result, or system overhead. While approaches allow for interchangeability, the choice of an appropriate approach and tooling to solve a given performance concern still relies on expert knowledge. Currently, there is no automated and extensible approach for decision support. In this paper, we present a methodology for the automated selection of performance engineering approaches tailored to user concerns. We decouple the complexity of selecting an SPE approach for a given scenario providing a decision engine and solution approach capability models. This separation allows to easily append additional solution approaches and rating criteria. We demonstrate the applicability by presenting decision engines that compare measurement- and model-based analysis approaches.
Applying model-based performance prediction requires that an up-to-date Performance Model (PM) is available throughout the development process. Creating such a model manually is an expensive process that is unsuitable for agile software development aiming to produce rapid releases in short cycles. Existing approaches automate the extraction of a PM based on reverse engineering and/or measurements techniques. However, these approaches require to monitor and analyse the whole application. Thus, they are too costly to be applied frequently, up to after each code change. Moreover, keeping potential manual changes of the PM is another challenge as long the PM is regenerated from scratch every time. To address these problems, this paper envisions an approach for efficient continuous integration of a parametrised performance model in an agile development process. Our work will combine static code analysis with adaptive, automatic, dynamic analysis covering updated parts of code to update the PM with parameters, like resource demands and branching probabilities. The benefit of our approach will be to automatically keep the PM up-to-date throughout the development process which enables the proactive identification of upcoming performance problems and provides a foundation for evaluating design alternatives at low costs.