An experimental evaluation of auto-scaling techniques for distributed stream processing systems

The introduction of cloud hosting has made it possible to elastically provision distributed stream processing systems (SPEs). By dynamically scaling the different operators of the system, resource consumption can be minimised while meeting the system service-level objectives. In the literature, many different auto-scaling techniques are proposed...

Attacks on Searchable Symmetric Encryption Systems: Revisiting Similar-data and File Injection Attacks

The amount of data individuals create keeps increasing every year to the point that the data cannot be stored on a single device anymore. Cloud storage provides a solution for this problem, but not everybody wants the cloud storage service providers to peek at their data and they thus encrypt their data before storing it on the service provider...

Privacy-Preserving Data Aggregation with Public Verifiability Against Internal Adversaries

Large amounts of data are continuously generated by individuals, apps, or dedicated devices. These data can be aggregated to compute useful statistics from multiple sources using data aggregation protocols. However, oftentimes these data contain private information that must be protected from misuse. Privacy-preserving protocols can help to...

Evaluating the Effect of Data Drift on Model Performance

Data drift refers to the variation in the production data compare to the training data and sometimes the machine learning model would decay because of it. Some machine learning models face the problem when in production: they receive drift data while there’s no ground truth to evaluate model performance, thus no way of determining the...

DiscoTest: Evolutionary Distributed Concurrency Testing of Blockchain Consensus Algorithms

Distributed concurrency bugs (DC bugs) are bugs that are triggered by a specific order of events in distributed systems. Traditional model checkers systematically or randomly test interleavings but suffer from the state-space explosion in long executions. This thesis presents DiscoTest, a testing tool for DC bugs in blockchain consensus...

The Financial Benefit of Energy Consumption Behavior Diversity Within an Energy Community

Energy communities are not yet fully self sufficient, mostly due to financial factors. Efforts are made to reduce these factors. Communities invest in community-owned assets, which provide more savings compared to individually-owned assets. Prosumers share their loads for better energy distribution, this can provide a significant impact. A good...

Battery Degradation in Control Algorithms for Redistribution of Benefits in a Community Energy Project

In a community energy project, batteries are the asset with the shortest lifespan and are therefore key contributors to cost. Understanding the influence of the battery state of health model on a control algorithm designed for redistribution of benefits in terms of financial gains in a community energy project can help elongate battery lifetime...

Improving the efficiency of renewable energy assets by optimizing the matching of supply and demand using a smart control algorithm

Given the fundamental profit gained by renewable energy assets in climate control, existing control algorithms are urged to be improved to match power supply and demand optimally. This paper explores various designed cases that lead toward an enhanced definition of a control algorithm with optimized behaviour. The core of improvement is...

Improving the efficiency of an energy management system with machine learning

This paper explores the possibility of using machine learning to improve the profits generated by an energy management system for so called prosumer households. Which are households with their own energy production, consumption, and storage. The commonly used deterministic algorithms only take the current data into account when deciding on how...

Systematically Applying High-Level Mutations for Fuzz Testing Big Data Applications

As the amount of data worldwide continues to grow the big data field is becoming increasingly important. Fuzz testing has shown to be an effective testing tool, and recent work has applied fuzz testing to big data applications. This study aims to contribute to knowledge on fuzz testing big data applications by extending on BigFuzz, a state-of...

Stacking High-Level Fuzz Mutations in Big Data Applications

The big data technology market size is expected to grow in the coming years. The advantages of having automated test tools for big data applications are becoming increasingly important. Fuzzing is an automated testing method which has been used in many different fields, but has not been frequently used in the big data domain as it poses several...

Guiding Big Data Fuzz Testing with Boosted Coverage-Based Input Selection

Big data applications are becoming increasingly popular. The importance of testing these applications increases with it. A recently proposed work called BigFuzz applies automated testing. The big data fuzzing tool shows very promising results. The aim of this research is to inspect how coverage guidance affects the performance of big data...

High-Level Mutations for JSON Typed Data in Big Data Fuzz Testing

Fuzzing in Big Data applications is a relatively new field which is still lacking effective tools to support automated testing. Recently, a framework called BigFuzz was published which made fuzz testing for big data systems feasible. But there was no solution to work with Big Data programs that use JSON typed data. Big Data systems often make...