Benjamin Erb

A conceptual model for event-sourced graph computing

Systems for highly interconnected application domains are increasingly taking advantage of graph-based computing platforms. Existing platforms employ a batch-oriented computing model and neglect near-realtime processing or temporal analysis. We suggest an extended conceptual model for event-driven computing on graphs. It takes into account the evolution of a graph and enables temporal analyses, processing on previous graph states, and retroactive modifications.

Tales from the Dark Side: Privacy Dark Strategies and Privacy Dark Patterns

Abstract Privacy strategies and privacy patterns are fundamental concepts of the privacy-by-design engineering approach. While they support a privacy-aware development process for IT systems, the concepts used by malicious, privacy-threatening parties are generally less understood and known. We argue that understanding the “dark side”, namely how personal data is abused, is of equal importance. In this paper, we introduce the concept of privacy dark strategies and privacy dark patterns and present a framework that collects, documents, and analyzes such malicious concepts. In addition, we investigate from a psychological perspective why privacy dark strategies are effective. The resulting framework allows for a better understanding of these dark concepts, fosters awareness, and supports the development of countermeasures. We aim to contribute to an easier detection and successive removal of such approaches from the Internet to the benefit of its users.

Is elasticity of scalable databases a Myth

The age of cloud computing has introduced all the mechanisms needed to elastically scale distributed, cloud-enabled applications. At roughly the same time, NoSQL databases have been proclaimed as the scalable alternative to relational databases. Since then, NoSQL databases are a core component of many large-scale distributed applications. This paper evaluates the scalability and elasticity features of the three widely used NoSQL database systems Couchbase, Cassandra and MongoDB under various workloads and settings using throughput and latency as metrics. The numbers show that the three database systems have dramatically different baselines with respect to both metrics and also behave unexpected when scaling out. For instance, while Couchbases throughput increases by 17% when scaled out from 1 to 4 nodes, MongoDBs throughput decreases by more than 50%. These surprising results show that not all tested NoSQL databases do scale as expected and even worse, in some cases scaling harms performances.

Reliability and Availability Properties of Distributed Database Systems

Distributed database systems represent an essential component of modern enterprise application architectures. If the overall application needs to provide reliability and availability, the database has to guarantee these properties as well. Entailing non-functional database features such as replication, consistency, conflict management, and partitioning represent subsequent challenges for successfully designing and operating an available and reliable database system. In this document, we identify why these concepts are important for databases and classify their design options. Moreover, we survey how eleven modern database systems implement these reliability and availability properties.

A Comparison of TCP Congestion Control Algorithms in 10G Networks

The increasing availability of 10G Ethernet network capabilities challenges existing transport layer protocols. As 10G connections gain momentum outside of backbone networks, the choice of appropriate TCP congestion control algorithms becomes even more relevant for networked applications running in environments such as data centers. Therefore, we provide an extensive overview of relevant TCP congestion control algorithms for high-speed environments leveraging 10G. We analyzed and evaluated six TCP variants using a physical network testbed, with a focus on the effects of propagation delay and significant drop rates. The results indicate that of the algorithms compared, BIC is most suitable when no legacy variant is present, CUBIC is suggested otherwise.

On the Potential of Event Sourcing for Retroactive Actor-based Programming

The actor model is an established programming model for distributed applications. Combining event sourcing with the actor model allows the reconstruction of previous states of an actor. When this event sourcing approach for actors is enhanced with additional causality information, novel types of actor-based, retroactive computations are possible. A globally consistent state of all actors can be reconstructed retrospectively. Even retroactive changes of actor behavior, state, or messaging are possible, with partial recomputations and projections of changes in the past. We believe that this approach may provide beneficial features to actor-based systems, including retroactive bugfixing of applications, decoupled asynchronous global state reconstruction for recovery, simulations, and exploration of distributed applications and algorithms.

Chronograph: A Distributed Processing Platform for Online and Batch Computations on Event-sourced Graphs

Several data-intensive applications take streams of events as a continuous input and internally map events onto a dynamic, graph-based data model which is then used for processing. The differences between event processing, graph computing, as well as batch processing and near-realtime processing yield a number of specific requirements for computing platforms that try to unify theses approaches. By combining an altered actor model, an event-sourced persistence layer, and a vertex-based, asynchronous programming model, we propose a distributed computing platform that supports event-driven, graph-based applications in a single platform. Our Chronograph platform concept enables online and offline computations on event-driven, history-aware graphs and supports different processing models on the evolving graph.

Consistent retrospective snapshots in distributed event-sourced systems

An increasing number of distributed, event-based systems adopt an architectural style called event sourcing, in which entities keep their entire history in an event log. Event sourcing enables data lineage and allows entities to rebuild any previous state. Restoring previous application states is a straight-forward task in event-sourced systems with a global and totally ordered event log. However, the extraction of causally consistent snapshots from distributed, individual event logs is rendered non-trivial due to causal relationships between communicating entities. High dynamicity of entities increases the complexity of such reconstructions even more. We present approaches for retrospective and global state extraction of event-sourced applications based on distributed event logs. We provide an overview on historical approaches towards distributed debugging and breakpointing, which are closely related to event log-based state reconstruction. We then introduce and evaluate our approach for non-local state extraction from distributed event logs, which is specifically adapted for dynamic and asynchronous event-sourced systems.

Setting Up a High-Speed TCP Benchmarking Environment - Lessons Learned

There are many high-speed TCP variants with different congestion control algorithms, which are designed for specific settings or use cases. Distinct features of these algorithms are meant to optimize different aspects of network performance, and the choice of TCP variant strongly influences application performance. However, setting up tests to help with the decision of which variant to use can be problematic, as many systems are not designed to deal with high bandwidths, such as 10 Gbps or more. This paper provides an overview of pitfalls and challenges of realistic network analysis to help in the decision making process.

Graphtides: a framework for evaluating stream-based graph processing platforms

Stream-based graph systems continuously ingest graph-changing events via an established input stream, performing the required computation on the corresponding graph. While there are various benchmarking and evaluation approaches for traditional, batch-oriented graph processing systems, there are no common procedures for evaluating stream-based graph systems. We, therefore, present GraphTides, a generic framework which includes the definition of an appropriate system model, an exploration of the parameter space, suitable workloads, and computations required for evaluating such systems. Furthermore, we propose a methodology and provide an architecture for running experimental evaluations. With our framework, we hope to systematically support system development, performance measurements, engineering, and comparisons of stream-based graph systems.