TY  - CHAP
A1  - Grum, Marcus
A1  - Bender, Benedict
A1  - Alfa, Attahiru S.
T1  - The construction of a common objective function for analytical infrastructures
T2  - 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)
N2  - The paper deals with the increasing growth of embedded systems and their role within structures similar to the Internet (Internet of Things) as those that provide calculating power and are more or less appropriate for analytical tasks. Faced with the example of a cyber-physical manufacturing system, a common objective function is developed with the intention to measure efficient task processing within analytical infrastructures. A first validation is realized on base of an expert panel.
KW  - Analytic Infrastructures
KW  - Cyber-Physical Manufacturing Systems
KW  - Measuring Efficient Task Processing
Y1  - 2018
U6  - https://doi.org/10.1109/ICE.2017.8279892
SP  - 219
EP  - 225
PB  - IEEE
CY  - New York
ER  - 
TY  - CHAP
A1  - Grum, Marcus
A1  - Bender, Benedict
A1  - Gronau, Norbert
A1  - Alfa, Attahiru S.
T1  - Efficient task realizations in networked production infrastructures
T2  - Proceedings of the Conference on Production Systems and Logistics
N2  - As Industry 4.0 infrastructures are seen as highly evolutionary environment with volatile, and time-dependent workloads for analytical tasks, particularly the optimal dimensioning of IT hardware is a challenge for decision makers because the digital processing of these tasks can be decoupled from their physical place of origin. Flexible architecture models to allocate tasks efficiently with regard to multi-facet aspects and a predefined set of local systems and external cloud services have been proven in small example scenarios. This paper provides a benchmark of existing task realization strategies, composed of (1) task distribution and (2) task prioritization in a real-world scenario simulation. It identifies heuristics as superior strategies.
KW  - Industry 4.0
KW  - CPS
KW  - Decentral Decision Making
KW  - Industrial Analytics
KW  - Case Study
Y1  - 2020
U6  - https://doi.org/10.15488/9682
SP  - 397
EP  - 407
PB  - publish-Ing.
CY  - Hannover
ER  -