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The trend for cloud computing has initiated a race towards data centres (DC) of an ever-increasing size. The largest DCs now contain many hundreds of thousands of virtual machine (VM) services. Given the finite lifespan of hardware, such…
Workflow management systems (WMS) support the composition and deployment of workflow-oriented applications in distributed computing environments. They hide the complexity of managing large-scale applications, which includes the controlling…
Analyzing large datasets with distributed dataflow systems requires the use of clusters. Public cloud providers offer a large variety and quantity of resources that can be used for such clusters. However, picking the appropriate resources…
Today's most prominent IT companies are built on the extraction of insight from data, and data processing has become crucial in data-intensive businesses. Nevertheless, the size of data which should be processed is growing significantly…
Containers, enabling lightweight environment and performance isolation, fast and flexible deployment, and fine-grained resource sharing, have gained popularity in better application management and deployment in addition to hardware…
Parallel task-based programming models, like OpenMP, allow application developers to easily create a parallel version of their sequential codes. The standard OpenMP 4.0 introduced the possibility of describing a set of data dependences per…
This paper presents the architecture and characteristics of a memory database intended to be used as a cache engine for web applications. Primary goals of this database are speed and efficiency while running on SMP systems with several CPU…
The dynamic load balancing algorithm based on the monitoring server load, self-similar characteristics of passing traffic have to provide a statistically uniform load distribution on servers, high performance, fault tolerance and capacity,…
The structures for the expression of fault-tolerance provisions into the application software are the central topic of this dissertation. Structuring techniques provide means to control complexity, the latter being a relevant factor for the…
Data distribution across different facilities offers benefits such as enhanced resource utilization, increased resilience through replication, and improved performance by processing data near its source. However, managing such data is…
This paper describes a service learning project used in an upper-level and graduate-level database systems course. Students complete a small database project for a real client. The final product must match the client specification and…
Most distributed storage systems provide limited abilities for querying data by attributes other than their primary keys. Supporting efficient search on secondary attributes is challenging as applications pose varying requirements to query…
Good database design is crucial to obtain a sound, consistent database, and - in turn - good database design methodologies are the best way to achieve the right design. These methodologies are taught to most Computer Science undergraduates,…
Modern distributed systems often rely on so called weakly-consistent databases, which achieve scalability by sacrificing the consistency guarantee of distributed transaction processing. Such databases have been formalised in two different…
Databases are an essential component of modern computing infrastructures and allow efficient manipulation of inherently structured data. The structure depends on the type and relationships of the individual data elements and on the access…
A benchmark study of modern distributed databases is an important source of information to select the right technology for managing data in the cloud-edge paradigms. To make the right decision, it is required to conduct an extensive…
The log-based analysis and trouble-shooting has remained prevalent and commonly used approach for centralized and time-haring systems. However, for parallel and distributed systems where happen-before relations are not directly available…
A number of popular systems, most notably Google's TensorFlow, have been implemented from the ground up to support machine learning tasks. We consider how to make a very small set of changes to a modern relational database management system…
A traditional database systems is organized around a single data model that determines how data can be organized, stored and manipulated. But the vision of this paper is to develop new principles and techniques to manage multiple data…
Databases are increasingly embracing AI to provide autonomous system optimization and intelligent in-database analytics, aiming to relieve end-user burdens across various industry sectors. Nonetheless, most existing approaches fail to…