Allocation Strategies for Data-Oriented Architectures

Datum

09.10.2015

Zeit

13:00 - 14:00

Sprecher

Dipl.-Inf. Tim Kiefer

Zugehörigkeit

Institut für Systemarchitektur, Professur Datenbanken

Sprache

en

Hauptthema

Informatik

Andere Themen

Informatik

Beschreibung

Data orientation is a common design principle in distributed data management systems. In contrast to process-oriented or transaction-oriented system designs, data-oriented architectures are based on data locality and function shipping. Data-oriented systems, i.e., systems that implement a data-oriented architecture, bundle data and operations together in tasks which are processed locally on the nodes of the distributed system. Allocation strategies map tasks to nodes and are core components in data-oriented systems. Optimal allocation strategies are hard to find given the complexity of the systems, the complicated interactions of tasks, and the huge solution space. In this thesis, we develop novel allocation strategies for data-oriented systems based on graph partitioning algorithms. We propose to extend classic graph partitioning to model non-linear performance by introducing vertex weights that do not linearly aggregate to partition weights. On top of the basic algorithms, we propose methods to incorporate heterogeneous infrastructures and to react to changing workloads and infrastructures by incrementally updating the partitioning. We evaluate all components of our allocation strategy algorithms and show their applicability and scalability with synthetic workload graphs. In end-to-end performance experiments in two actual data-oriented systems, a database-as-a-service system and a database management system for multiprocessor systems, we prove that our allocation strategies outperform alternative state-of-the-art methods.

Letztmalig verändert: 05.10.2015, 09:52:05