I am a Software Architect, Research Associate, and PhD Student with a Master’s Degree in Information Systems Management.

Currently, I’m working in the Mobile Cloud Computing Research Group at TU Berlin where my academic interests focus on Benchmarking, Microservices, and Fog Computing.

info <at> martingrambow.com

Martin Grambow

Research Associate

Select Publi­cations

Using Application Benchmark Call Graphs to Quantify and Improve the Practical Relevance of Microbenchmark Suites

M. Grambow, C. Laaber, P. Leitner, D. Bermbach

PeerJ Computer Science - PeerJ

2021

Performance problems in applications should ideally be detected as soon as they occur, i.e., directly when the causing code modification is added to the code repository. To this end, complex and cost-intensive application benchmarks or lightweight but less relevant microbenchmarks can be added to existing build pipelines to ensure performance goals. In this paper, we show how the practical relevance of microbenchmark suites can be improved and verified based on the application flow during an application benchmark run. We propose an approach to determine the overlap of common function calls between application and microbenchmarks, describe a method which identifies redundant microbenchmarks, and present a recommendation algorithm which reveals relevant functions that are not covered by microbenchmarks yet. A microbenchmark suite optimized in this way can easily test all functions determined to be relevant by application benchmarks after every code change, thus, significantly reducing the risk of undetected performance problems. Our evaluation using two time series databases shows that, depending on the specific application scenario, application benchmarks cover different functions of the system under test. Their respective microbenchmark suites cover between 35.62% and 66.29% of the functions called during the application benchmark, offering substantial room for improvement. Through two use cases — removing redundancies in the microbenchmark suite and recommendation of yet uncovered functions — we decrease the total number of microbenchmarks and increase the practical relevance of both suites. Removing redundancies can significantly reduce the number of microbenchmarks (and thus the execution time as well) to ~10% and ~23% of the original microbenchmark suites, whereas recommendation identifies up to 26 and 14 newly, uncovered functions to benchmark to improve the relevance. By utilizing the differences and synergies of application benchmarks and microbenchmarks, our approach potentially enables effective software performance assurance with performance tests of multiple granularities.

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pdf cite code Benchmarking Time Series Databases Microbenchmarking Performance Testing

Benchmarking the Performance of Microservice Applications

M. Grambow, E. Wittern, D. Bermbach

ACM SIGAPP Applied Computing Review (ACR) - ACM

2020

Application performance is crucial for end user satisfaction. It has therefore been proposed to benchmark new software releases as part of the build process. While this can easily be done for system classes which come with a standard interface, e.g., database or messaging systems, benchmarking microservice applications is hard because each application requires its own custom benchmark and benchmark implementation due to interface heterogeneity. Furthermore, even minor interface changes will easily break an existing benchmark implementation. In previous work, we proposed a benchmarking approach for single microservices: Assuming a REST-based microservice interface, developers describe the benchmark workload based on abstract interaction patterns. At runtime, our approach uses an interface description such as the OpenAPI specification to automatically resolve and bind the workload patterns to the concrete endpoint before executing the benchmark and collecting results. In this extended paper, we enhance our approach with the capabilities necessary for benchmarking entire microservice applications, especially the ability to resolve complex data dependencies across microservice endpoints. We evaluate our approach through our proof-of-concept prototype OpenISBT and demonstrate that it can be used to benchmark an open source microservice application with little manual effort.

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pdf cite code Benchmarking Microservices API QoS RESTful OpenAPI Swagger SLA

Continuous Benchmarking: Using System Benchmarking in Build Pipelines

M. Grambow, F. Lehmann, D. Bermbach

Proc. of IEEE International Conference on Cloud Engineering (IC2E '19) - IEEE

2019

Continuous integration and deployment are established paradigms in modern software engineering. Both intend to ensure the quality of software products and to automate the testing and release process. Today's state of the art, however, focuses on functional tests or small microbenchmarks such as single method performance while the overall quality of service (QoS) is ignored. In this paper, we propose to add a dedicated benchmarking step into the testing and release process which can be used to ensure that QoS goals are met and that new system releases are at least as "good" as the previous ones. For this purpose, we present a research prototype which automatically deploys the system release, runs one or more benchmarks, collects and analyzes results, and decides whether the release fulfills predefined QoS goals. We evaluate our approach by replaying two years of Apache Cassandra's commit history.

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pdf cite code Benchmarking Continuous Integration Software Development QoS YCSB Cassandra Jenkins

MockFog: Emulating Fog Computing Infrastructure in the Cloud

J. Hasenburg, M. Grambow, E. Grünewald, S. Huk, D. Bermbach

Proc. of IEEE International Conference on Fog Computing (ICFC '19) - IEEE

2019

Fog computing is an emerging computing paradigm that uses processing and storage capabilities located at the edge, in the cloud, and possibly in between. Testing fog applications, however, is hard since runtime infrastructures will typically be in use or may not exist, yet. In this paper, we propose an approach that emulates such infrastructures in the cloud. Developers can freely design emulated fog infrastructures, configure their performance characteristics, and inject failures at runtime to evaluate their application in various deployments and failure scenarios. We also present our proof-of-concept implementation MockFog and show that application performance is comparable when running on MockFog or a small fog infrastructure testbed.

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pdf cite code Fog Computing Infrastructure Emulation Testbed

Public Video Surveillance: Using the Fog to Increase Privacy

M. Grambow, J. Hasenburg, D. Bermbach

Proc. of the 5th Workshop on Middleware and Applications for the Internet of Things (M4IoT '18) - ACM

2018

In public video surveillance, there is an inherent conflict between public safety goals and privacy needs of citizens. Generally, societies tend to decide on middleground solutions that sacrifice neither safety nor privacy goals completely. In this paper, we propose an alternative to existing approaches that rely on cloud-based video analysis. Our approach leverages the inherent geo-distribution of fog computing to preserve privacy of citizens while still supporting camera-based digital manhunts of law enforcement agencies.

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pdf cite code Fog Computing Edge Computing Privacy Public Video Surveillance Face Recognition

Public Software Projects

BeFaaS

2020

BeFaaS is an extensible open-source benchmarking framework for FaaS environments which comes with two built-in benchmark suites and respective load profiles: An E-commererce application (web shop) and an IoT application (smart traffic light). BeFaas is the first benchmarking framework which enables the federated benchmark of FaaS providers: Users can split their application and define which function(s) should run on which provider. Currently, BeFaaS supports six FaaS providers on which (parts of) the test suite applications can be deployed and evaluated: AWS Lambda, Google Cloud Functions, Azure Functions, TinyFaaS, OpenFaaS, and OpenWhisk.

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code MIT, Apache-2.0 4 3 JavaScript Python Go Benchmarking FaaS

OpenISBT

2020

OpenISBT is an Intelligent Service Benchmark Tool to benchmark microservice-based applications based on their OpenAPI 3.0 interface description files. It implements a pattern-based approach to reduce the efforts for defining microservice benchmarks, while still allowing to measure qualities of complex interactions. It assumes that microservices expose a REST API, described in a machine-understandable way, and allows developers to model interaction patterns from abstract operations that can be mapped to that API. Possible data dependencies between operations are resolved at runtime.

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code MIT 2 1 Kotlin OpenAPI Benchmarking Microservices RESTful

MockFog2

2020

Fog computing is an emerging computing paradigm that uses processing and storage capabilities located at the edge, in the cloud, and possibly in between. Testing fog applications, however, is hard since runtime infrastructures will typically be in use or may not exist, yet. MockFog is a tool that can be used to emulate such infrastructures in the cloud. Developers can freely design emulated fog infrastructures and configure their performance characteristics. With MockFog 2.0, developers can also manage application components and do experiment orchestration.

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code Apache-2.0 1 8 JavaScript Fog Computing Emulation

Continuous Benchmarking Plug-In

2019

Continuous integration and deployment are established paradigms in modern software engineering. Both intend to ensure the quality of software products and to automate the testing and release process. Today’s state of the art, however, focuses on functional tests or small microbenchmarks such as single method performance while the overall quality of service(QoS) is ignored. We want to extend these pipelines with an additional application benchmark step which ensures QoS requirements and prevents performance regressions. This Plugin analyzes results and detects the violation of fixed performance metrics (e.g., defined in SLAs), sudden significant performance fluctuations, and long lasting regression trends.

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code MIT 3 2 Java JavaScript Jenkins SLA QoS

Experience

Research Associate

Technische Universität Berlin

2018 - now

Member of the Mobile Cloud Computing research group and supervisor of the courses "Introduction to Programming in Java", "Fog Computing", and "(Advanced) Distributed Systems Prototyping".

Software Developer

LucaNet AG

2017 - 18

Design, planning, and implementation of software for financial consolidation of companies, analysis of controlling data, and reporting.

Teaching Assistant

Technische Universität Berlin

2013 - 17

Tutorials for the courses "Technical Foundations of Computer Science" ('13 - '14), "Business Processes" ('14), "Application Systems" ('15), and "Theoretical Foundations of Computer Science" ('15 - '17)

Software Tester

Beta Systems Software AG

2012 - 13

Test automation using Jenkins and Selenium WebDriver.

Apprentice

Beta Systems Software AG

2009 - 12

Introduction to all departments of the company.

Education

Informatics (Ph.D.)

Technische Universität Berlin

2018 - now

Focus on benchmarking microservices, continuous integration/deployment pipelines, and fog computing.

Information Systems Management (M.Sc.)

Technische Universität Berlin

2015 - 17

Advanced courses in distributed systems, software security, quality assurance, standardization, and controlling.

Information Systems Management (B.Sc.)

Technische Universität Berlin

2012 - 15

Fundamentals of mathematics, computer science, and business administration. Personal focus on project management, business processes, and software development.

Computer Science Expert (Subject Area: Software Development)

OSZ IMT - Berlin College of Further Education for Information Technology and Medical Equipment Technology

2009 - 12

Certified by Chamber of Industry and Commerce.

Skills

  • All
  • Programming
  • Tools/Technologies
  • Industry Knowledge
  • Soft Skills