Sören Henning

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• Dr.-Ing.
• Senior Researcher at Dynatrace

Cloud-native applications constitute a recent trend for designing large-scale software systems. However, even though several cloud-native tools and patterns have emerged to support scalability, there is no commonly accepted method to empirically benchmark their scalability. In this study, we present a benchmarking method, allowing researchers and p...

Distributed stream processing engines are designed with a focus on scalability to process big data volumes in a continuous manner. We present the Theodolite method for benchmarking the scalability of distributed stream processing engines. Core of this method is the definition of use cases that microservices implementing stream processing have to fu...

Performance benchmarking is a common practice in software engineering, particularly when building large-scale, distributed, and data-intensive systems. While cloud environments offer several advantages for running benchmarks, it is often reported that benchmark results can vary significantly between repetitions -- making it difficult to draw reliab...

Distributed stream processing frameworks help building scalable and reliable applications that perform transformations and aggregations on continuous data streams. This paper introduces ShuffleBench, a novel benchmark to evaluate the performance of modern stream processing frameworks. In contrast to other benchmarks, it focuses on use cases where s...

Context: The combination of distributed stream processing with microservice architectures is an emerging pattern for building data-intensive software systems. In such systems, stream processing frameworks such as Apache Flink, Apache Kafka Streams, Apache Samza, Hazelcast Jet, or the Apache Beam SDK are used inside microservices to continuously pro...

Logs are crucial for analyzing large-scale software systems, offering insights into system health, performance, security threats, potential bugs, etc. However, their chaotic nature$\unicode{x2013}$characterized by sheer volume, lack of standards, and variability$\unicode{x2013}$makes manual analysis complex. The use of clustering algorithms can ass...

Several software systems are built upon stream processing architectures to process large amounts of data in near real-time. Today’s distributed stream processing systems (DSPSs) spread the processing among multiple machines to provide scalable performance. However, high-performance and Quality of Service (QoS) in distributed stream processing are c...

Cloud-native applications constitute a recent trend for designing large-scale software systems. This thesis introduces the Theodolite benchmarking method, allowing researchers and practitioners to conduct empirical scalability evaluations of cloud-native applications, their frameworks, configurations, and deployments. The benchmarking method is ap...

Event-driven microservices are an emerging architectural style for data-intensive software systems. In such systems, stream processing frameworks such as Apache Flink, Apache Kafka Streams, Apache Samza, Hazelcast Jet, or the Apache Beam SDK are used inside microservices to continuously process massive amounts of data in a distributed fashion. Whil...

Theodolite is a framework for benchmarking the scal-ability of cloud-native applications such as microser-vices. It automates deployment and monitoring of a cloud-native application for different load intensities and provisioned cloud resources and assesses whether specified service level objectives (SLOs) are fulfilled. Provided as a Kubernetes Op...

Theodolite is a framework for benchmarking the scalability of cloud-native applications. It automates deployment and monitoring of a cloud-native application for different load intensities and provisioned cloud resources and assesses whether specified service level objectives (SLOs) are fulfilled. Provided as a Kubernetes Operator, Theodolite allow...

In cloud event processing, data generated at the edge is processed in real-time by cloud resources. Both distributed stream processing (DSP) and Function-as-a-Service (FaaS) have been proposed to implement such event processing applications. FaaS emphasizes fast development and easy operation, while DSP emphasizes efficient handling of large data v...

In cloud event processing, data generated at the edge is processed in real-time by cloud resources. Both distributed stream processing (DSP) and Function-as-a-Service (FaaS) have been proposed to implement such event processing applications. FaaS emphasizes fast development and easy operation, while DSP emphasizes efficient handling of large data v...

Reproducibility is often mentioned as a core requirement for benchmarking studies of software systems and services. "Cloud-native" is an emerging style for building large-scale software systems, which leads to an increasing amount of benchmarks for cloud-native tools and architectures. However, the complex nature of cloud-native deployments makes t...

Scalability is promoted as a key quality feature of modern big data stream processing engines. However, even though research made huge efforts to provide precise definitions and corresponding metrics for the term scalability, experimental scalability evaluations or benchmarks of stream processing engines apply different and inconsistent metrics. Wi...

The Internet of Things adoption in the manufacturing industry allows enterprises to monitor their electrical power consumption in real time and at machine level. In this paper, we follow up on such emerging opportunities for data acquisition and show that analyzing power consumption in manufacturing enterprises can serve a variety of purposes. In t...

The Titan Control Center is a software platform supporting research on industrial big data analytics.Building upon a scalable and extensible architecture, the Titan Control Center analyzes and visualizes data streams from Internet of Things sensors in industrial production. It performs different types of aggregations,correlation, forecasting, and a...

Over the past years, an increase in software archi-tectures containing microservices, which process data streams of a messaging system, can be observed. We present Theodolite, a method accompanied by an open source implementation for benchmarking the scalability of such microservices as well as their employed stream processing frameworks and deploy...

Ever-increasing amounts of data and requirements to process them in real time lead to more and more analytics platforms and software systems designed according to the concept of stream processing. A common area of application is processing continuous data streams from sensors, for example, IoT devices or performance monitoring tools. In addition to...

The Internet of Things adoption in the manufacturing industry allows enterprises to monitor their electrical power consumption in real time and at machine level. In this paper, we follow up on such emerging opportunities for data acquisition and show that analyzing power consumption in manufacturing enterprises can serve a variety of purposes. Apar...

Distributed stream processing engines are designed with a focus on scalability to process big data volumes in a continuous manner. We present the Theodolite method for benchmarking the scalability of distributed stream processing engines. Core of this method is the definition of use cases that microservices implementing stream processing have to fu...

We study Parallel Task Scheduling Pm|sizej|Cmax with a constant number of machines. This problem is known to be strongly NP-complete for each m ≥ 5, while it is solvable in pseudo-polynomial time for each m ≤ 3. We give a positive answer to the long-standing open question whether this problem is strongly NP-complete for m = 4. As a second result, w...

Ever-increasing amounts of data and requirements to process them in real time lead to more and more analytics platforms and software systems being designed according to the concept of stream processing. A common area of application is the processing of continuous data streams from sensors, for example, IoT devices or performance monitoring tools. I...

Ever-increasing amounts of data and requirements to process them in real time lead to more and more analytics platforms and software systems being designed according to the concept of stream processing. A common area of application is the processing of continuous data streams from sensors, for example, IoT devices or performance monitoring tools. I...

The visions and ideas of Industry 4.0 require a profound interconnection of machines, plants, and IT systems in industrial production environments. This significantly increases the importance of software, which is coincidentally one of the main obstacles to the introduction of Industry 4.0. Lack of experience and knowledge, high investment and main...

Detailed knowledge about the electrical power consumption in industrial production environments is a prerequisite to reduce and optimize their power consumption. Today's industrial production sites are equipped with a variety of sensors that, inter alia, monitor electrical power consumption in detail. However, these environments often lack an autom...

Detailed knowledge about the electrical power consumption in industrial production environments is a prerequisite to reduce and optimize their power consumption. Today's industrial production sites are equipped with a variety of sensors that, inter alia, monitor electrical power consumption in detail. However, these environments often lack an autom...

The visions and ideas of Industry 4.0 require a profound interconnection of machines, plants, and IT systems in industrial production environments. This significantly increases the importance of software, which is coincidentally one of the main obstacles to the introduction of Industry 4.0. Lack of experience and knowledge, high investment and main...

Maintaining a long-living software system is substantially related to the quality of the code the system is built from. In this experience report we describe how a set of practices and tools has been established and used on the early stages of a project. The approach is based on Clean Code and the use of well known static code analysis tools. The t...

Detailed knowledge about the electrical power consumption is a prerequisite for reducing it in manufacturing enterprises. Therefore, a continuous monitoring and analysis of the power usage of the overall business as well as of its individual devices, machines, and production plants is demanded. In this paper, we present an approach for such a monit...

We study Parallel Task Scheduling with a constant number of machines. This problem is known to be strongly NP-complete for each , while it is solvable in pseudo-polynomial time for each . We give a positive answer to the long-standing open question whether this problem is strongly NP-complete for . As a second result, we improve the lower bound of...

We present requirements on a performance testing framework to distinguish it from a functional testing framework and a benchmarking framework. Based on these requirements, we propose such a performance testing framework for Java, called RadarGun. Radar-Gun can be included into a continuous integration server, such as Jenkins, so that performance te...

We study the Parallel Task Scheduling problem $Pm|size_j|C_{\max}$ with a constant number of machines. This problem is known to be strongly NP-complete for each $m \geq 5$, while it is solvable in pseudo-polynomial time for each $m \leq 3$. We give a positive answer to the long-standing open question whether this problem is strongly $NP$-complete f...