MSc Daniel Sokolowski
daniel.sokolowski@unisg.ch
+49 163 6949690
0000-0003-2911-8304
Soko2D
DSoko2
https://dsoko.de

School of Computer Science
Torstrasse 25
9000 St. Gallen, Switzerland

I am interested in distributed systems and cloud technology from a Programming Languages and Software Engineering perspective. At the moment, I mainly focus on advanced solutions for DevOps leveraging Infrastructure as Code.

I recently moved to the University of St. Gallen. Before I was part of the Software Factory 4.0 project at the Technical University of Darmstadt.

Publications

1. ESEC/FSE
   µs
   

   Automating Serverless Deployments for DevOps Organizations

   Daniel Sokolowski, Pascal Weisenburger, Guido Salvaneschi

   In Proceedings of the 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE, 2021

   Abstract PDF Supp Code

   DevOps unifies software development and operations in cross-functional teams to improve software delivery and operations (SDO) performance. Ideally, cross-functional DevOps teams independently deploy their services, but the correct operation of a service often demands other services, requiring coordination to ensure the correct deployment order. This issue is currently solved either with a central deployment or manual out-of-band communication across teams, e.g., via phone, chat, or email. Unfortunately, both contradict the independence of teams, hindering SDO performance – the reason why DevOps is adopted in the first place.

   In this work, we conduct a study on 73 IT professionals, showing that, in practice, they resort to manual coordination for correct deployments even if they expect better SDO performance with fully automated approaches. To address this issue, we propose μs ([mju:z] “muse”), a novel IaC system automating deployment coordination in a fully decentralized fashion, still retaining compatibility with DevOps practice – in contrast to today’s solutions. We implement µs, demonstrate that it effectively enables automated coordination, introduces negligible definition overhead, has no performance overhead, and is broadly applicable, as shown by the migration of 64 third-party IaC projects.

2. ESEC/FSE DS
   µs
   

   Deployment Coordination for Cross-Functional DevOps Teams

   Daniel Sokolowski

   In Proceedings of the 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE, 2021

   Abstract PDF

   Software stability and reliability are the core concerns of DevOps. They are improved by tightening the collaboration between developers and operators in cross-functional teams on the one hand and by automating operations through continuous integration (CI) and infrastructure as code (IaC) on the other hand. Ideally, teams in DevOps are fully independent. Still, their applications often depend on each other in practice, requiring them to coordinate their deployment through centralization or manual coordination.

   With this work, we propose and implement the novel IaC solution µs ([mju:z] “muse”), which automates deployment coordination in a decentralized fashion. µs is the first approach that is compatible with the DevOps goals as it enables truly independent operations of the DevOps teams. We define our research problem through a questionnaire survey with IT professionals and evaluate the solution by comparing it to other modern IaC approaches, assessing its performance, and applying it to existing IaC programs.

3. AISTA
   

   Impact of Programming Languages on Machine Learning Bugs

   Sebastian Sztwiertnia, Maximilian Grübel, Amine Chouchane, Daniel Sokolowski, Krishna Narasimhan, Mira Mezini

   In Proceedings of the 1st ACM International Workshop on AI and Software Testing/Analysis, AISTA, 2021

   Abstract PDF

   Machine learning (ML) is on the rise to be ubiquitous in modern software. Still, its use is challenging for software developers. So far, research has focused on the ML libraries to find and mitigate these challenges. However, there is initial evidence that programming languages also add to the challenges, identifiable in different distributions of bugs in ML programs. To fill this research gap, we propose the first empirical study on the impact of programming languages on bugs in ML programs. We plan to analyze software from GitHub and related discussions in GitHub issues and Stack Overflow for bug distributions in ML programs, aiming to identify correlations with the chosen programming language, its features and the application domain. This study’s results enable better-targeted use of available programming language technology in ML programs, preventing bugs, reducing errors and speeding up development.

4. SuperCompCloud
   ScalaLoci
   

   Leveraging Hybrid Cloud HPC with Multitier Reactive Programming

   Daniel Sokolowski, Jan-Patrick Lehr, Christian Bischof, Guido Salvaneschi

   In Proceedings of the 3rd IEEE/ACM International Workshop on Interoperability of Supercomputing and Cloud Technologies, SuperCompCloud, 2020

   Abstract PDF

   The advent of cloud computing has enabled large-scale availability of on-demand computing and storage resources. However, these benefits are not yet at the fingertips of HPC developers: Typical HPC applications use on-premise computing resources and rely on static deployment setups, reliable hardware, and rather homogeneous resources. This hinders (partial) execution in the cloud, even though applications could benefit from scaling beyond on-premise resources and from the variety of hardware available in the cloud to speed up execution.

   To address this issue, we orchestrate computationally intensive kernels using a high-level programming language that ensures advanced optimization and improves execution flexibility-enabling hybrid cloud/on-premise HPC deployments. Our approach is based on multitier reactive programming, where distributed code is defined within the same compilation unit and computations are placed explicitly using placement types. We adjust placement based on performance characteristics measured before execution, apply our approach to a shortest vector problem (SVP) solver from cryptanalysis, and evaluate it to be effective.

5. OOPSLA
   

   Language-Integrated Privacy-Aware Distributed Queries

   Guido Salvaneschi, Mirko Köhler, Daniel Sokolowski, Philipp Haller, Sebastian Erdweg, Mira Mezini

   Proceedings of the ACM on Programming Languages 3 (OOPSLA), 2019

   Abstract

   Distributed query processing is an effective means for processing large amounts of data. To abstract from the technicalities of distributed systems, algorithms for operator placement automatically distribute sequential data queries over the available processing units. However, current algorithms for operator placement focus on performance and ignore privacy concerns that arise when handling sensitive data.

   We present a new methodology for privacy-aware operator placement that both prevents leakage of sensitive information and improves performance. Crucially, our approach is based on an information-flow type system for data queries to reason about the sensitivity of query subcomputations. Our solution unfolds in two phases. First, placement space reduction generates deployment candidates based on privacy constraints using a syntax-directed transformation driven by the information-flow type system. Second, constraint solving selects the best placement among the candidates based on a cost model that maximizes performance. We verify that our algorithm preserves the sequential behavior of queries and prevents leakage of sensitive data. We implemented the type system and placement algorithm for a new query language SecQL and demonstrate significant performance improvements in benchmarks.

6. REBLS
   ScalaLoci
   

   Multitier Reactive Programming in High Performance Computing

   Daniel Sokolowski, Philipp Martens, Guido Salvaneschi

   Presentation at the 6th Workshop on Reactive and Event-based Languages and Systems (REBLS), 2019

   Abstract PDF

   High Performance Computing (HPC) is crucial in a number of sectors, including weather forecasts, particle simulations and fluid dynamics. Existing programming frameworks for HPC expose developers to low-level details such as message passing and explicit memory management, which are hard to program and error-prone.

   In this paper, we present ongoing work on increasing the level of abstraction for HPC. We tackle this problem with an approach based on a combination of multitier programming and reactive programming which enables the development of complex processor configurations in a uniform way using event streams as communication pattern. We report our experience with LULESH, a well known HPC benchmark, and we outline our research roadmap.