Polypheny-DB: Cost- and Workload-aware Adaptive Data Management (Ongoing)

In the last few years, it has become obvious that the "one-size-fits-all" paradigm, according to which database systems have been designed for several decades, has come to an end. The reason is that in the very broad spectrum of applications, ranging from business over science to the private life of individuals, demands are more and more heterogeneous. As a consequence, the data to be considered signi cantly differs in many ways, for example from immutable data to data that is frequently updated; from highly structured data to unstructured data; from applications that need precise data to applications that are fine with approximate and/or outdated results; from applications which demand always consistent data to applications that are fine with lower levels of consistency. Even worse, many applications feature heterogeneous data and/or workloads, i.e., they intrinsically come with data (sub-collections) with different requirements for data storage, access, consistency, etc. that have to be dealt with in the same system.

 This development can either be coped with a large zoo of specialized systems (for each subset of data with different properties), or by a new type of flexible database system that automatically adapts to the – potentially dynamically changing – needs and characteristics of applications. Such behaviour is especially important in the Cloud where several applications need to be hosted on a shared infrastructure, in order to make economic use of the available resources. In addition, the infrastructure costs that incur in a Cloud are made transparent in a very fine-grained way.

 The Polypheny-DB project will address these challenges by dynamically optimizing the data management layer by taking into account the resources needed and an estimation of the expected workload of applications. The core will be a comprehensive cost model that seamlessly addresses these criteria and that will be used, in two subprojects, to optimize

  1. data storage and access (Marco Vogt), and
  2. data distribution (Alexander Stiemer).

 

Since

01.05.2017

Funding Agencies

Funding

464'422 CHF

Staff

Research Topics

Publications