GeneaLog: Fine-Grained Data Streaming Provenance at the Edge
Paper in proceedings, 2018

Fine-grained data provenance in data streaming allows linking each result tuple back to the source data that contributed to it, something beneficial for many applications (e.g., to find the conditions triggering a security- or safety-related alert). Further, when data transmission or storage has to be minimized, as in edge computing and cyber-physical systems, it can help in identifying the source data to be prioritized.
The memory and processing costs of fine-grained data provenance, possibly afforded by high-end servers, can be prohibitive for the resource-constrained devices deployed in edge computing and cyber-physical systems. Motivated by this challenge, we present GeneaLog, a novel fine-grained data provenance technique for data streaming applications. Leveraging the logical dependencies of the data, GeneaLog takes advantage of cross-layer properties of the software stack and incurs a minimal, constant size per-tuple overhead. Furthermore, it allows for a modular and efficient algorithmic implementation using only standard data streaming operators. This is particularly useful for distributed streaming applications since the provenance processing can be executed at separate nodes, orthogonal to the data processing. We evaluate an implementation of GeneaLog using vehicular and smart grid applications, confirming it efficiently captures fine-grained provenance data with minimal overhead.

Edge architectures

Fine-grained data provenance

Data streaming

Author

Dimitrios Palyvos-Giannas

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Vincenzo Massimiliano Gulisano

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Marina Papatriantafilou

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Middleware '18 Proceedings of the 19th International Middleware Conference

227-238

19th ACM/IFIP/USENIX International Middleware Conference, Middleware 2018
Rennes, France,

HARE: Self-deploying and Adaptive Data Streaming Analytics in Fog Architectures

Swedish Research Council (VR), 2017-01-01 -- 2020-12-31.

INDEED

Chalmers, 2016-01-01 -- 2020-12-31.

Future factories in the Cloud (FiC)

Swedish Foundation for Strategic Research (SSF), 2016-01-01 -- 2020-12-31.

STAMINA - GE

Göteborg Energi, Foundation for Research and Developmen, 2017-01-01 -- 2021-12-31.

Subject Categories

Computer Engineering

Computer Science

Computer Systems

Areas of Advance

Information and Communication Technology

Energy

DOI

10.1145/3274808.3274826

More information

Latest update

7/8/2019 4