Publications

Over the last five years, all major cloud platform providers have increased their serverless offerings. Many early adopters report significant benefits for serverless-based over traditional applications, and many companies are considering moving to serverless themselves. However, currently there exist only few, scattered, and sometimes even conflicting reports on when serverless applications are well suited and what the best practices for their implementation are. We address this problem in the present study about the state of serverless applications. We collect descriptions of 89 serverless applications from open-source projects, academic literature, industrial literature, and domain-specific feedback. We analyze 16 characteristics that describe why and when successful adopters are using serverless applications, and how they are building them. We further compare the results of our characterization study to 10 existing, mostly industrial, studies and datasets; this allows us to identify points of consensus across multiple studies, investigate points of disagreement, and overall confirm the validity of our results. The results of this study can help managers to decide if they should adopt serverless technology, engineers to learn about current practices of building serverless applications, and researchers and platform providers to better understand the current landscape of serverless applications.

Resource demands are crucial parameters for modeling and predicting the performance of software systems. Currently, resource demand estimators are usually executed once for system analysis. However, the monitored system, as well as the resource demand itself, are subject to constant change in runtime environments. These changes additionally impact the applicability, the required parametrization as well as the resulting accuracy of individual estimation approaches. Over time, this leads to invalid or outdated estimates, which in turn negatively influence the decision-making of adaptive systems. In this article, we present SARDE, a framework for self-adaptive resource demand estimation in continuous environments. SARDE dynamically and continuously tunes, selects, and executes an ensemble of resource demand estimation approaches to adapt to changes in the environment. This creates an autonomous and unsupervised ensemble estimation technique, providing reliable resource demand estimations in dynamic environments. We evaluate SARDE using two realistic datasets. One set of different micro-benchmarks reflecting different possible system states and one dataset consisting of a continuously running application in a changing environment. Our results show that by continuously applying online optimization, selection and estimation, SARDE is able to efficiently adapt to the online trace and reduce the model error using the resulting ensemble technique.

Serverless computing shows good promise for efficiency and ease-of-use. Yet, there are only a few, scattered and sometimes conflicting reports on questions such as Why do so many companies adopt serverless?, When are serverless applications well suited?, and How are serverless applications currently implemented? To address these questions, we analyze 89 serverless applications from open-source projects, industrial sources, academic literature, and scientific computing—the most extensive study to date.

Microservices, containers, and serverless computing belong to a trend toward applications composed of many small, self-contained, and automatically managed components. Core to serverless computing, Function-as-a-Service (FaaS) platforms employ state-of-the-art container technology and microservices-based architectures to enable users to manage complex applications without the need for systems-level expertise. Victim of its own success, and partially due to proprietary technology, currently the community has a limited overview of these platforms. To address this, we propose a reference architecture and ecosystem for FaaS platforms. Based on a year-long survey of real-world platforms conducted within the SPEC-RG Cloud Group, we highlight specific components and identify common operational patterns.

Performance models are valuable and powerful tools for performance prediction. However, the creation of performance models usually requires significant manual effort. Furthermore, as the modeled structures are subject to frequent change in modern infrastructures, such performance models need to be adapted as well. We therefore propose a reference architecture for online model learning in virtualized environments, which enables the automatic extraction of the aforementioned performance models. We follow an agent-based approach, which enables us to incorporate the extraction of information about the application structure as well as the virtualization structures present in modern computing centers. Our evaluation shows that our collaborating agents are able to reduce the manual effort of performance model extraction by 85.4%. The resulting performance model is able to predict the system utilization with an absolute error of less than 4% and the end-to-end response time with a relative error of less than 21%.

Serverless functions are an emerging cloud computing paradigm that is being rapidly adopted by both industry and academia. In this cloud computing model, the provider opaquely handles resource management tasks such as resource provisioning, deployment, and auto-scaling. The only resource management task that developers are still in charge of is selecting how much resources are allocated to each worker instance. However, selecting the optimal size of serverless functions is quite challenging, so developers often neglect it despite its significant cost and performance benefits. Existing approaches aiming to automate serverless functions resource sizing require dedicated performance tests, which are time-consuming to implement and maintain. In this paper, we introduce an approach to predict the optimal resource size of a serverless function using monitoring data from a single resource size. As our approach does not require dedicated performance tests, it enables cloud providers to implement resource sizing on a platform level and automate the last resource management task associated with serverless functions. We evaluate our approach on four different serverless applications on AWS, where it predicts the execution time of the other memory sizes based on monitoring data for a single memory size with an average prediction error of 15.3%. Based on these predictions, it selects the optimal memory size for 79.0% of the serverless functions and the secondbest memory size for 12.3% of the serverless functions, which results in an average speedup of 39.7% while also decreasing average costs by 2.6%.

Application performance management (APM) tools are useful to observe the performance properties of an application during production. However, APM is normally purely reactive, that is, it can only report about current or past performance degradation. Although some approaches capable of predictive application monitoring have been proposed, they can only report a predicted degradation but cannot explain its root-cause, making it hard to prevent the expected degradation. In this paper, we present SuanMing---a framework for predicting performance degradation of microservice applications running in cloud environments. SuanMing is able to predict future root causes for anticipated performance degradations and therefore aims at preventing performance degradations before they actually occur. We evaluate SuanMing on two realistic microservice applications, TeaStore and TrainTicket, and we show that our approach is able to predict and pinpoint performance degradations with an accuracy of over 90%.

Correctly configuring a distributed database management system (DBMS) deployed in a cloud environment for maximizing performance poses many challenges to operators. Even if the entire configuration spectrum could be measured directly, which is often infeasible due to the multitude of parameters, single measurements are subject to random variations and need to be repeated multiple times. In this work, we propose Baloo, a framework for systematically measuring and modeling different performance-relevant configurations of distributed DBMS in cloud environments. Baloo dynamically estimates the required number of configurations, as well as the number of required measurement repetitions per configuration based on a desired target accuracy. We evaluate Baloo based on a data set consisting of 900 DBMS configuration measurements conducted in our private cloud setup. Our evaluation shows that the highly configurable framework is able to achieve a prediction error of up to 12% while saving 80% of the measurement effort. We also publish all code and the acquired data set to foster future research.

In recent years, there has been a shift in software development towards microservice-based architectures, which consist of small services that focus on one particular functionality. Many companies are migrating their applications to such architectures to reap the benefits of microservices, such as increased flexibility, scalability and a smaller granularity of the offered functionality by a service. On the one hand, the benefits of microservices for functional testing are often praised, as the focus on one functionality and their smaller granularity allow for more targeted and more convenient testing. On the other hand, using microservices has their consequences (both positive and negative) on other types of testing, such as performance testing. Performance testing is traditionally done by establishing the baseline performance of a software version, which is then used to compare the performance testing results of later software versions. However, as we show in this paper, establishing such a baseline performance is challenging in microservice applications. In this paper, we discuss the benefits and challenges of microservices from a performance testers point of view. Through a series of experiments on the TeaStore application, we demonstrate how microservices affect the performance testing process, and we demonstrate that it is not straightforward to achieve reliable performance testing results for a microservice application.

Emerging paradigms for network virtualization like Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) form new challenges for accurate performance modeling and analysis tools. Therefore, performance modeling and prediction approaches that support SDN or NFV technologies help system operators to analyze the performance of a data center and its corresponding network. The Descartes Network Infrastructures (DNI) offers a high-level descriptive language to model SDN-based networks, which can be transformed into various predictive modeling formalisms. However, these modeling concepts have not yet been evaluated in a realistic scenario. In this paper, we present an extensive case study evaluating the DNI modeling capabilities, the transformations to predictive models, and the performance prediction using the OMNeT++ and SimQPN simulation frameworks. We present five realistic scenarios of a content distribution network (CDN), compare the performance predictions with real-world measurements, and discuss modeling gaps and calibration issues causing mispredictions in some scenarios.

Function-as-a-Service (FaaS) platforms enable users to run arbitrary functions without being concerned about operational issues, while only paying for the consumed resources. Individual functions are often composed into workflows for complex tasks. However, the pay-per-use model and nontransparent reporting by cloud providers make it challenging to estimate the expected cost of a workflow, which prevents informed business decisions. Existing cost-estimation approaches assume a static response time for the serverless functions, without taking input parameters into account. In this paper, we propose a methodology for the cost prediction of serverless workflows consisting of input-parameter sensitive function models and a monte-carlo simulation of an abstract workflow model. Our approach enables workflow designers to predict, compare, and optimize the expected costs and performance of a planned workflow, which currently requires time-intensive experimentation. In our evaluation, we show that our approach can predict the response time and output parameters of a function based on its input parameters with an accuracy of 96.1%. In a case study with two audio-processing workflows, our approach predicts the costs of the two workflows with an accuracy of 96.2%.

Architectural performance models are a common approach to predict the performance properties of a software system. Parametric dependencies, which describe the relation between the input parameters of a component and its performance properties, significantly increase the prediction accuracy of architectural performance models. However, manually modeling parametric dependencies is time-intensive and requires expert knowledge. Existing automated extraction approaches require dedicated performance tests, which are often infeasible. In this paper, we introduce an approach to automatically identify parametric dependencies from monitoring data using feature selection techniques from the area of machine learning. We evaluate the applicability of three techniques selected from each of the three groups of feature selection methods: a filter method, an embedded method, and a wrapper method. Our evaluation shows that the filter technique outperforms the other approaches. Based on these results, we apply this technique to a distributed micro-service web-shop, where it correctly identifies 11 performance-relevant dependencies, achieving a precision of 91.7% based on a manually labeled gold-standard.

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.

Modern distributed applications offer complex performance behavior and many degrees of freedom regarding deployment and configuration. Researchers employ various methods of analysis, modeling, and management that leverage these degrees of freedom to predict or improve non-functional properties of the software under consideration. In order to demonstrate and evaluate their applicability in the real world, methods resulting from such research areas require test and reference applications that offer a range of different behaviors, as well as the necessary degrees of freedom. Existing production software is often inaccessible for researchers or closed off to instrumentation. Existing testing and benchmarking frameworks, on the other hand, are either designed for specific testing scenarios, or they do not offer the necessary degrees of freedom. Further, most test applications are difficult to deploy and run, or are outdated. In this paper, we introduce the TeaStore, a state-of-the-art micro-service-based test and reference application. TeaStore offers services with different performance characteristics and many degrees of freedom regarding deployment and configuration to be used as a benchmarking framework for researchers. The TeaStore allows evaluating performance modeling and resource management techniques; it also offers instrumented variants to enable extensive run-time analysis. We demonstrate TeaStore's use in three contexts: performance modeling, cloud resource management, and energy efficiency analysis. Our experiments show that TeaStore can be used for evaluating novel approaches in these contexts and also motivates further research in the areas of performance modeling and resource management.

Model-based performance analysis can be leveraged 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 parametric dependencies at design-time. This paper identifies runtime 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.

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.

A common approach to predict system performance are so-called architectural performance models. In these models, parametric dependencies describe the relation between the input parameters of a component and its performance properties and therefore significantly increase the model expressiveness. However, manually modeling parametric dependencies is often infeasible in practice. Existing automated extraction approaches require either application source code or dedicated performance tests, which are not always available. We therefore introduced one approach for identification and one for characterization of parametric dependencies, solely based on run-time monitoring data. In this paper, we propose our idea on combining both techniques in order to create a holistic approach for the identification and characterization of parametric dependencies. Furthermore, we discuss challenges we are currently facing and potential ideas on how to overcome them.

Resource demands are crucial parameters for modeling and predicting the performance of software systems. Direct measurement of these resource demands is usually infeasible due to instrumentation overheads causing measurement interferences and perturbation in production environments. Thus, a number of statistical estimation approaches (e.g., based on optimization, regression or Kalman filters) have been proposed in the literature. Most of these approaches are parameterized. These parameters influence the estimation quality and the required computation time. Existing work uses historical data as training sets to optimize those parameters and to minimize the estimation error of those approaches. However, if the data traces are fundamentally different, the optimal parameter settings are different as well. In this paper, we propose to use automated clustering in order to group training sets into groups of similar optimization behavior. This way, optimization can be specifically tailored to certain groups of traces in a self-aware manner. During run-time, every trace is first sorted into a cluster, where the respective cluster-wide parameter optimum can be applied. A preliminary case study shows that clustering can provide promising improvements.

With the advent of the micro-service paradigm, applications are divided into small, distributed parts. Knowledge of optimal resource configurations of such applications is required both for autonomic resource management as well as its assessment. Due to the high-dimensional search space of all possible configurations, the systematic measuring of the optimal configurations is challenging. To this end, we introduce a search algorithm based on hill-climbing for finding all optimal configurations in a feasible time and integrate it in an existing measuring framework. This approach enables the assessment, comparison and optimization of autonomic resource management approaches for micro-service applications. The evaluation shows that our approach is able to find all optimal configurations in the considered scenarios, while state-of-the-art multi-objective search algorithms do not.

Modeling parametric dependencies in architectural performance models increases performance prediction accuracy. However, manually modeling parametric dependencies is time-intensive and requires expert knowledge. Existing automated extraction approaches require dedicated performance tests, which is often infeasible. In this paper, we propose to characterize parametric dependencies based on monitoring data. We create a representative dataset and show that different machine learning approaches perform best, depending on the characteristics of the dependency. Based on these results, we introduce a meta-selector that chooses the most suitable machine learning approach based on the dependency characteristics. In our evaluation, the meta-selector reduces the prediction error compared to the best individual machine learning approach, SVR, by 30%. As a proof of concept, we show that our approach is capable of automatically characterizing a manually modeled dependency from a previous case-study, resulting in a response time prediction accuracy of 92.8%.

Architectural performance models can be leveraged to explore performance properties of software systems during design-time and run-time. We see a reluctance from industry to adopt model-based analysis approaches due to the required expertise and modeling effort. Building models from scratch in an editor does not scale for medium and large-scale systems in an industrial context. Existing open-source performance model extraction approaches imply signi cant initial efforts which might be challenging for layman users. To simplify usage, we provide the extraction of architectural performance models based on application monitoring traces as a web service. Model-Extraction-as-a-Service (MEaaS) solves the usability problem and lowers the initial effort of applying model-based analysis approaches.

Awareness of application performance can be derived through various quantitative analyses, model-based and measurement-based approaches. A suitable visualization supports the understanding of application performance. Usually, analysis tools include a tool specific visualization or no visualization at all. In this paper we proposed to decouple the result visualization from the analysis approach. We present the Performance VisualizatiOn (PAVO) framework which provides result visualization tailored to a given performance analysis result. To show benefits and usability of PAVO, we present our integration in Descartes Query Language (DQL).

Model-based performance predictions and reconfigurations enable optimizing resource efficiency while ensuring that Quality-of-Service demands are met in today's complex ITsystems. The Descartes Modeling Language (DML) and the Palladio Component Model (PCM) are two architectural performance modeling formalisms applied in this context. This paper compares DML to PCM concerning similarities, differences and semantic gaps. Based on this, we propose a mapping from DML to PCM for which we implemented a tool realizing an automated transformation.

Distributed Database Management Systems (DBMS) are a crucial component of modern IT applications. Understanding their performance and non-functional properties is of paramount importance. Yet, benchmarking distributed DBMS has proven to be difficult in practice. Either, a realistic workload is often mapped to a synthetic workload without knowing if this mapping is correct or available workload traces are replayed. While the latter approach provides more realistic results, real-world traces are hard to obtain and their scope is limited in time scale and variance.We propose collecting real-world traces and then applying data generation techniques to synthesize similar realistic traces based on it. Based in this approach, we can obtain workloads for benchmarking, exhibit variability with respect to different aspects of interest while still being similar to the original traces. Varying generation parameters, we are able to support benchmarking what-if scenarios with hypothetical workloads and introduced anomalies.

Serverless computing services, such as Function-as-a-Service (FaaS), hold the attractive promise of a high level of abstraction and high performance, combined with the minimization of operational logic. Several large ecosystems of serverless platforms, both open- and closed-source, aim to realize this promise. Consequently, a lucrative market has emerged. However, the performance trade-offs of these systems are not well-understood. Moreover, it is exactly the high level of abstraction and the opaqueness of the operational-side that make performance evaluation studies of serverless platforms challenging. Learning from the history of IT platforms, we argue that a benchmark for serverless platforms could help address this challenge. We envision a comprehensive serverless benchmark, which we contrast to the narrow focus of prior work in this area. We argue that a comprehensive benchmark will need to take into account more than just runtime overhead, and include notions of cost, realistic workloads, more (open-source) platforms, and cloud integrations. Finally, we show through preliminary real-world experiments how such a benchmark can help compare the performance overhead when running a serverless workload on state-of-the-art platforms.

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.

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 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.

As a key part of the serverless computing paradigm, Function-as-a-Service (FaaS) platforms enable users to run arbitrary functions without being concerned about operational issues. However, there are several performance-related issues surrounding the state-of-the-art FaaS platforms that can deter widespread adoption of FaaS, including sizeable overheads, unreliable performance, and new forms of the cost-performance trade-off. In this work we, the SPEC RG Cloud Group, identify six performance-related challenges that arise specifically in this FaaS model, and present our roadmap to tackle these problems in the near future. This paper aims at motivating the community to solve these challenges together.

Researchers propose and employ various methods to analyze, model, optimize and manage modern distributed cloud applications. In order to demonstrate and evaluate these methods in realistic scenarios, researchers rely on reference applications. These applications should offer a range of different behaviors, degrees of freedom allowing for customization and should use a modern and representative technology stack. Existing testing and benchmarking applications are either outdated, designed for specific testing scenarios, or do not offer the necessary degrees of freedom. Further, most cloud reference applications are difficult to deploy and run. In this paper, we present the TeaStore, a micro-service-based test and reference cloud application. TeaStore offers services with various performance characteristics and a high degree of freedom regarding its deployment and configuration to be used as a cloud reference application for researchers. The TeaStore is designed for the evaluation of performance modeling and resource management techniques. We invite cloud researchers to use the TeaStore and provide it open-source, extendable, easily deployable and monitorable.

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.

Researchers propose and employ various methods to analyze, model, optimize and manage modern distributed micro-service applications. In order to demonstrate and evaluate these methods in realistic scenarios, researchers rely on reference applications. These applications should offer a range of different behaviors, degrees of freedom allowing for customization and should use a modern and representative technology stack. Unfortunately, existing testing and benchmarking applications are either outdated, designed for specific testing scenarios, or do not offer the necessary degrees of freedom. In this talk, we introduce the TeaStore, a state-of-the-art test and reference application with a micro-service architecture. TeaStore offers services with different performance characteristics and many degrees of freedom regarding deployment and configuration to be used as a benchmarking framework for researchers. Based on three experiments, we demonstrate TeaStore's use in three different research contexts: performance modeling, cloud resource management, and energy efficiency analysis.

Modern distributed component and microservice applications can be deployed in various ways and configured using different settings and software stacks. These applications have complex performance characteristics, as the constituent services feature different bottlenecks that may even change over time, depending on the usage profile. In general, microservice architectures present new challenges in the areas of performance monitoring, performance evaluation, performance modeling and performance prediction. The performance community has proposed many approaches that utilize the degrees of freedom of such applications at different points of the software life-cycle to tackle these emerging challenges. Verifying, comparing, and evaluating the results of such research is difficult. To enable practical evaluation, researchers need a software application that they can deploy as reference, offers realistic degrees of freedom and sufficiently complex performance behavior. The software in question should be open source, provide sufficient instrumentation, and should produce results that enable analysis and comparison of research findings, all while being indicative of how the evaluated research would affect applications in production use. Real world distributed software is usually proprietary and cannot be used for experimentation. In addition, results from evaluations conducted using such software are difficult to reproduce and compare, as the software used remains inaccessible for other researchers. Existing and broadly used test software does not offer the necessary degrees of freedom and is often manually adapted. Some of the most widely used test and reference applications, such as RUBiS, Dell DVD Store or SPECjEnterprise2010 are outdated and not representative of modern real-world applications. Reference applications from software vendors such as the Sock Shop, on the other hand, use a state-of-the-art software stack that is representative of modern real-world applications. However, these applications do not contain complex business logic and therefore do not offer representative performance behavior. We present the TeaStore, a micro-services-based test and reference application that can be used as a benchmarking framework by researchers. The TeaStore consists of five services, each featuring unique performance characteristics and bottlenecks: • The WebUI service displays static HTML pages, which are enriched using calls to the REST APIs of the remaining services. • The Auth service handles session validation (SHA512) and password hashing (BCrypt). • A list of recommended products is supplied by the Recommender service based on the user’s previous purchases. • The product images are retrieved from the ImageProvider service, which either loads them from cache or from the HDD. • Access to the database is encapsulated by the Persistence service, which can be used with a variety of databases. Service discovery is implemented using the Netflix Ribbon client-side load balancer and a simplified implementation of the Netflix Eureka registry. This enables distributed deployment and high scalability. The TeaStore offers multiple deployment options, manual deployment of WAR files, public docker containers or deployment using container orchestration frameworks, such as Kubernetes. Deployment in a container orchestration framework enables dynamic autoscaling of service instances, container health monitoring and failure recovery. We provide docker containers with a tailored Kieker instrumentation and a central trace repository, which collects the monitoring traces from all service instances using a RabbitMQ Server. Using these monitoring traces, tools such as the Performance Model Extractor (PMX) [1] can be used to extract PCM models and evaluate the accuracy of these models. As the TeaStore is continuously developed, it can be used to evaluate approaches that attempt to incorporate architectural performance models into the DevOps cycle, such as [2]. In general, the services’ different resource usage profiles enable performance and efficiency optimization with non-trivial service placement and resource provisioning decisions. [1] Jürgen Walter, Christian Stier, Heiko Koziolek, and Samuel Kounev. An Expandable Extraction Framework for Architectural Performance Models. InProceedings of the 3rd International Workshop on Quality-Aware DevOps (QUDOS'17), L'Aquila, Italy, April 2017. ACM. April 2017 [2] Manar Mazkatli and Anne Koziolek. 2018. Continuous Integration of Performance Model. In Companion of the 2018 ACM/SPEC International Conference on Performance Engineering (ICPE '18). ACM, New York, NY, USA, 153-158. DOI: https://doi.org/10.1145/3185768.3186285

Performance modeling is a powerful tool to predict the performance of a software system, especially for complex software systems deployed in cloud enviroments. At run-time, these predictions can be used for a variety of purposes. A performance engineer can analyze them to find potential bottlenecks or investigate solutions to an existing performance issue. Alternatively, a design space exploration tool can utilize performance predictions to find an optimal system configuration for a given scenario. A relatively novel use case for performance predictions is proactive auto-scaling. Proactive auto-scaling uses load forecasts to predict load spikes and proactively adapt the system accordingly. Here performance predictions are required to find a suitable system configuration for the incoming load spike. However, all of these use cases have different requirements for the performance predictions in terms of time-to-result, accuracy, and the required metrics. For example during design space exploration accurate predictions of both utilization and response time are required, without major time limitations. An auto-scaler, on the other hand, requires only rough utilization predictions, but with strict time-to-result requirements. A performance engineer requires a balance of accuracy and time-to-result. He is however often only interested in specific metrics, not the performance of the whole system. While a vast number of performance prediction approaches has been proposed, there is as of yet no approach flexible enough to fit all use cases. In this talk, we present our vision for flexible performance predictions at run-time. We envision the usage of one model describing the architecture and performance relevant properties of the system. This model can then be transformed to a number of solution formalisms, such as Queuing Networks (QNs), Layered Queuing Networks (LQNs) or stochastic process models. For these solution formalisms, a series of solution approaches exist, ranging from simple approximations, analytical approaches to full-scale simulations. This ensures that for each use case a suitable solution approach is available without having to maintain multiple models of the system. However, manual selection requires expert knowledge and manual intervention. Therefore, we aim to design an intelligent selection algorithm. This algorithm selects a suitable solution approach for every performance query. We define a performance query as a collection of requested performance metrics, a lower limit for the prediction accuracy and an upper limit for the time-to-result. In order to select a suitable solution approach for a query we first filter out all approaches that are not capable of predicting the requested performance metrics. For example, many analytical approaches are not capable of predicting response time distributions. In a next step, the selection algorithm needs to predict the accuracy and time-to-result for every solution approach. To predict the accuracy we plan to combine expert knowledge about the solvers with smart transformations, that can estimate how well the initial model is represented in the transformation output. For each simplification a transformation makes a number of penalty points, depending the level of simplification is awarded. For example, if a transformation represents loops in a probabilistic fashion only two penalty points per loop would be awarded, while a transformation that ignores passive resources would award a larger number of penalty points for each passive resource in the original model. Additional penalty points are awarded in case the applied solution approach calculates approximations instead of exact results. These penalty scores are determined once using expert knowledge. The resulting penalty score represents an indicator for the expected accuracy of a solution approach. We plan to utilize machine learning approaches to estimate the time-to-result. In general, estimating the time-to-result for performance predictions is challenging. However, we do not need to predict the time-to-result for a previously unseen model. After the inception of the performance model, each solution approach can be benchmarked to generate training data. From this training data, the machine learning approaches can then estimate the time-to-result for a specific request. The accuracy and time-to-result estimations will enable the design of an automated selection algorithm, that selects a suitable solution approach for each performance query. This allows for flexible performance predictions using a single model.

The serverless computing paradigm promises many desirable properties for cloud applications - low-cost, fine-grained deployment, and management-free operation. Consequently, the paradigm has underwent rapid growth: there currently exist tens of serverless platforms and all global cloud providers host serverless operations. To help tune existing platforms, guide the design of new serverless approaches, and overall contribute to understanding this paradigm, in this work we present a long-term, comprehensive effort to identify, collect, and characterize serverless use cases. We survey 89 use cases, sourced from white and grey literature, and from consultations with experts in areas such as scientific computing. We study each use case using 24 characteristics, including general aspects, but also workload, application, and requirements. When the use cases employ workflows, we further analyze their characteristics. Overall, we hope our study will be useful for both academia and industry, and encourage the community to further share and communicate their use cases.