34 citations · 70 across the 7 of their papers we have counts for
8 papers
The TensorFlow Partitioning and Scheduling Problem: It's the Critical Path!
Ruben Mayer, Christian Mayer, Larissa Laich
State-of-the-art data flow systems such as TensorFlow impose iterative calculations on large graphs that need to be partitioned on heterogeneous devices such as CPUs, GPUs, and TPU…
Knowledge is at the Edge! How to Search in Distributed Machine Learning Models
Thomas Bach, Muhammad Adnan Tariq, Ruben Mayer +1
With the advent of the Internet of Things and Industry 4.0 an enormous amount of data is produced at the edge of the network. Due to a lack of computing power, this data is current…
EmuFog: Extensible and Scalable Emulation of Large-Scale Fog Computing Infrastructures
Ruben Mayer, Leon Graser, Harshit Gupta +2
The diversity of Fog Computing deployment models and the lack of publicly available Fog infrastructure makes the design of an efficient application or resource management policy a…
FogStore: Toward a Distributed Data Store for Fog Computing
Ruben Mayer, Harshit Gupta, Enrique Saurez +1
Stateful applications and virtualized network functions (VNFs) can benefit from state externalization to increase their reliability, scalability, and inter-operability. To keep and…
SPECTRE: Supporting Consumption Policies in Window-Based Parallel Complex Event Processing
Ruben Mayer, Ahmad Slo, Muhammad Adnan Tariq +3
Distributed Complex Event Processing (DCEP) is a paradigm to infer the occurrence of complex situations in the surrounding world from basic events like sensor readings. In doing so…
StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge
Christian Mayer, Ruben Mayer, Majd Abdo
Today, massive amounts of streaming data from smart devices need to be analyzed automatically to realize the Internet of Things. The Complex Event Processing (CEP) paradigm promise…