Nowadays, there is an increasing need to develop data intensive applications able to manage more and more amounts of data coming from distributed and heterogeneous sources, e.g. IoT sensors and devices or mobile applications, effectively, quickly, correctly, and securely. In its current state, however, Cloud Computing paradigm does not perfectly fit for these kinds of applications; specifically, request latencies from a device to a cloud-based backend may exceed acceptable levels, or security and privacy regulations may disallow data movement from end devices to centralized cloud services. At the same time, Fog Computing — which combines end devices and cloud services with edge nodes — has emerged as a paradigm promising to solve these problems by moving parts of the computation from both devices and cloud services to edge nodes. It thereby allows to address, e.g. latency issues through increased spatial proximity. In matters of security and privacy, in turn, Fog Computing can be beneficial through carefully controlling which data subset is allowed to be processed on which node with or without additional steps such as encryption, data fragmentation, or anonymization and aggregation. Only in combination with cloud services, edge nodes can provide the reliability and scalability levels required by data-intensive applications. Today, such a setup is difficult to manage due to heterogeneity of runtime environments but also due to the sheer number of involved machines and the current lack of a solutions that hide this complexity. This is where the project DITAS comes into play.
The goal of DITAS is to propose a Cloud Platform to support information logistics for data-intensive applications where data processing does not occur only on cloud resources but also on devices at the edge of the network. The resulting heterogeneous and distributed infrastructure can be easily configured and managed by developers of data-intensive applications through the DITAS toolkit that proposes a common accessibility framework to provide a unified access to the data oblivious of location.
