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arxiv: 1907.01046 · v1 · pith:CTCRWSJ4new · submitted 2019-07-01 · 💻 cs.SE · cs.DC

A Scalable Architecture for Power Consumption Monitoring in Industrial Production Environments

S\"oren Henning , Wilhelm Hasselbring , Armin M\"obius This is my paper

Pith reviewed 2026-05-25 11:31 UTC · model grok-4.3

classification 💻 cs.SE cs.DC
keywords power consumption monitoringmicroservice architecturefog computingindustrial productionstream processingsensor integrationscalabilitydata visualization
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The pith

A microservice architecture with fog computing integrates sensors to monitor and aggregate power consumption data scalably up to 20,000 devices.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper describes a system architecture for collecting and analyzing electrical power consumption data from various sensors in industrial production sites. It proposes combining microservices with big data and stream processing, deployed partly in a central cloud and partly on-site using fog computing. This design targets requirements such as fault tolerance, extensibility, real-time processing, and resource efficiency across different environment sizes. A prototype integrates multiple sensor types, aggregates measurements continuously, and offers web-based visualizations, with tests on 16 real servers and simulations reaching 20,000 sensors.

Core claim

The architecture integrates different sensors and continuously aggregates their power consumption measurements while supporting scalability to 20,000 sensors. Parts of the system run in an elastic central cloud and other parts run decentralized in the production environment via the fog computing paradigm. A prototype implementation shows solutions for sensor integration and measurement aggregation, includes a single-page web application for data visualization, and was deployed to monitor 16 servers in a medium-sized enterprise data center.

What carries the argument

The microservice-based architecture augmented by big data and stream processing techniques, deployed using the fog computing paradigm, which integrates sensors and aggregates measurements continuously.

If this is right

  • Different kinds of sensors can be integrated and their measurements continuously aggregated.
  • Analyzed data can be made comprehensible through different forms of visualization in a single-page web application.
  • The system supports monitoring of devices, machines, and production plants in environments of various sizes.
  • Parts of the architecture can run decentralized directly in the production environment.
  • Scalability holds under simulation with 20,000 sensors.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Factories adopting this setup could identify specific high-consumption equipment and adjust operations to lower overall energy use.
  • The split between cloud and local processing might allow continued operation during temporary internet disruptions.
  • The same integration and aggregation approach could extend to tracking other resources such as water usage or compressed air in industrial sites.
  • Large-scale rollouts would likely need to handle data volume growth and security for sensor networks beyond what the prototype tested.

Load-bearing premise

The microservice architecture with fog computing will meet requirements for fault tolerance, extensibility, real-time data processing, and resource efficiency in production environments of various sizes without additional unaddressed challenges.

What would settle it

A deployment of the prototype with 20,000 real sensors in an actual production site that fails to maintain continuous real-time aggregation or experiences resource inefficiency or downtime would falsify the scalability claim.

Figures

Figures reproduced from arXiv: 1907.01046 by Armin M\"obius, S\"oren Henning, Wilhelm Hasselbring.

Figure 1
Figure 1. Figure 1: Microservice-based architecture not require to update the others. Furthermore, they do not share any implementation or database schema but communi￾cate via transaction-less protocols such as REST. This also facilitates an individual choice of programming language, database system and technology stack for each service. Loose coupling between microservices enables individual scaling of them and allows the sy… view at source ↗
Figure 2
Figure 2. Figure 2: Data flow between the different components when processing new measurements. The Record Bridge receives monitoring data from physical sensors [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Deployment architecture showing all possibilities how the Record Bridge and an additional edge component can be deployed. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Graphical visualization of our implemented Kafka Streams topology. Horizontal cylinders represent Kafka topics, the vertical one a database. Grayed-out [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Power consumption of one of the monitored servers in our pilot deployment. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Amount of processed records per second in relation to the number [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
read the original abstract

Detailed knowledge about the electrical power consumption in industrial production environments is a prerequisite to reduce and optimize their power consumption. Today's industrial production sites are equipped with a variety of sensors that, inter alia, monitor electrical power consumption in detail. However, these environments often lack an automated data collation and analysis. We present a system architecture that integrates different sensors and analyzes and visualizes the power consumption of devices, machines, and production plants. It is designed with a focus on scalability to support production environments of various sizes and to handle varying loads. We argue that a scalable architecture in this context must meet requirements for fault tolerance, extensibility, real-time data processing, and resource efficiency. As a solution, we propose a microservice-based architecture augmented by big data and stream processing techniques. Applying the fog computing paradigm, parts of it are deployed in an elastic, central cloud while other parts run directly, decentralized in the production environment. A prototype implementation of this architecture presents solutions how different kinds of sensors can be integrated and their measurements can be continuously aggregated. In order to make analyzed data comprehensible, it features a single-page web application that provides different forms of data visualization. We deploy this pilot implementation in the data center of a medium-sized enterprise, where we successfully monitor the power consumption of 16~servers. Furthermore, we show the scalability of our architecture with 20,000~simulated sensors.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes a microservice-based architecture augmented by big data and stream processing techniques and deployed via the fog computing paradigm to integrate heterogeneous sensors and continuously aggregate power-consumption measurements in industrial production environments. It argues that any scalable solution must satisfy fault tolerance, extensibility, real-time processing, and resource efficiency, and claims the proposed design meets these requirements at scales up to 20,000 sensors. A prototype is implemented, deployed in a medium-sized enterprise data center to monitor 16 servers, and augmented by a single-page web application for visualization; scalability is further asserted via a simulation involving 20,000 sensors.

Significance. If the architecture demonstrably satisfies the stated non-functional requirements at production scale, the work would supply a concrete, deployable template for automated power-monitoring systems that can be adapted to factories of different sizes. The explicit mapping of microservices, stream processing, and fog placement to the four requirements is a useful contribution; the concrete prototype deployment and the attempt to quantify scale via simulation are also positive. However, the limited real-world evidence and the absence of simulation details substantially weaken the evidential basis for the central scalability claim.

major comments (2)
  1. [Evaluation / Simulation] Evaluation / Simulation subsection: the claim that the architecture supports scalability to 20,000 sensors rests on a simulation whose workload model, sensor heterogeneity, failure-injection strategy, network conditions, and performance metrics are not described. Without these parameters it is impossible to verify that fault tolerance, real-time processing, and resource efficiency hold under load variation or component failure, which directly undermines the central scalability argument.
  2. [Prototype Implementation] Prototype Implementation section: the only real deployment monitors 16 servers in a single data center; no quantitative results (latency, throughput, error rates, resource utilization) or experiments exercising load variation or fault scenarios are reported. Consequently the requirements for fault tolerance and real-time processing lack empirical grounding from the prototype itself.
minor comments (2)
  1. [Abstract] Abstract: the notation '16~servers' is a typographic artifact; replace with '16 servers'.
  2. [Architecture Description] The paper would benefit from an explicit table mapping each architectural component (microservice, stream processor, fog node) to the four stated requirements, with references to the corresponding implementation or simulation evidence.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting gaps in the evaluation and prototype sections. We address each major comment below and will revise the manuscript to strengthen the evidential basis for the scalability claims.

read point-by-point responses

  1. Referee: [Evaluation / Simulation] Evaluation / Simulation subsection: the claim that the architecture supports scalability to 20,000 sensors rests on a simulation whose workload model, sensor heterogeneity, failure-injection strategy, network conditions, and performance metrics are not described. Without these parameters it is impossible to verify that fault tolerance, real-time processing, and resource efficiency hold under load variation or component failure, which directly undermines the central scalability argument.

    Authors: We agree that additional detail on the simulation is required to substantiate the scalability argument. In the revised manuscript we will expand the Evaluation / Simulation subsection to explicitly describe the workload model, the modeling of sensor heterogeneity, the failure-injection strategy employed, the network conditions assumed, and the performance metrics collected. These additions will allow readers to assess how fault tolerance, real-time processing, and resource efficiency were evaluated under the simulated load of 20,000 sensors. revision: yes

  2. Referee: [Prototype Implementation] Prototype Implementation section: the only real deployment monitors 16 servers in a single data center; no quantitative results (latency, throughput, error rates, resource utilization) or experiments exercising load variation or fault scenarios are reported. Consequently the requirements for fault tolerance and real-time processing lack empirical grounding from the prototype itself.

    Authors: The prototype section currently emphasizes successful integration and continuous aggregation across heterogeneous sensors in a live data-center setting. We acknowledge that quantitative metrics and explicit load/fault experiments are not reported. In the revision we will include all available deployment measurements (observed latency, throughput, resource utilization, and any error rates recorded during the 16-server run) and, where possible, describe any informal load-variation observations made during operation. revision: partial

Circularity Check

0 steps flagged

No circularity: architecture proposal supported by independent implementation and simulation

full rationale

The paper describes a microservice architecture, its requirements, a prototype deployment on 16 servers, and a separate scalability test with 20,000 simulated sensors. No equations, fitted parameters, or derivations are present. No self-citations are invoked as load-bearing premises for any claim. The architecture description, implementation details, and simulation results are independent of one another; none reduces to another by definition or construction. This is a standard non-circular systems paper whose central claims rest on external benchmarks (prototype run and simulation) rather than self-reference.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper relies on standard domain assumptions about industrial environments needing fault tolerance and real-time processing, with no free parameters or invented entities; no new entities postulated.

axioms (1)
  • domain assumption Industrial production environments require architectures that meet requirements for fault tolerance, extensibility, real-time data processing, and resource efficiency.
    Invoked in the abstract as the basis for the proposed solution.

pith-pipeline@v0.9.0 · 5786 in / 1150 out tokens · 38525 ms · 2026-05-25T11:31:23.234646+00:00 · methodology

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