Industrial Edge Cloud for the Smart Factory

ABSTRACT

Industrial Edge Cloud in the Smart Factories

Cloud Computing has been used extensively for a number of purposes over the past few years. From everyday tasks like reading emails and watching videos to factory automation and device control, it has changed where data is processed and how it is accessed. However, the increasing number of connected devices brings problems such as low Quality of Service (QoS) due to infrastructure resources and high latency due to bandwidth constraints. The current trend to solve the problems posed by Cloud Computing is to perform computation as close to the field level as possible. This paradigm is called Edge Computing. There are several proposed architectures for Edge Computing, but so far there are no standards accepted by the community or industry. In addition, there is no common agreement on what the Edge Computing architecture physically looks like. In this paper, we describe the Industrial Edge Cloud, explain what Industrial Edge Cloud architecture looks like, what its requirements are, and what its capabilities are. We also define the key features that an Edge Node should support. Furthermore, we give a short insight into the challenges that come along with the networking of machines and plants and that it is not sufficient to just “secure” machines or plants, but that new interfaces of cybersecurity with operation, maintenance, safety etc. arise, which require corresponding security work.

Authors

• Volkan Gezer, Carsten Harms, Deutsches Forschungszentrum für Künstliche Intelligenz
• Carsten Brüggemann, Pfalzkom
• Michael Pfeifer, TÜV Süd
• Andreas Michael, TÜV Süd
• Simon Althoff, Weidmüller
• Torsten Runge, Deutsche Telekom/T-Systems
• Keran Sivalingam, Technologie-Initiative SmartFactory KL e.V.
• Martin Ruskowski, Deutsches Forschungszentrum für Künstliche Intelligenz

You may follow the PHYSICS project activities on Twitter and LinkedIn.

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Development and selection of suitable use cases and KPIs for the successful introduction of new high technology using the example of “Function-as-a-Service”

ABSTRACT

What distinguishes successful high-tech projects from failed ones? – Often not the quality of the technology itself, but rather misunderstood, unclear objectives, poor communication and often isolated project management.
As part of the EU research project “Physics” and the introduction of Function-as-a-Service at three pioneers from the fields of smart manufacturing, healthcare and agriculture, this paper aims to address an essential and often overlooked component of the successful introduction of high technology.
An interactive approach is introduced that explains how the success criterion of use case development can be positively designed, what important role stakeholder analysis plays and how direct and indirect business benefits can be identified and linked. Furthermore, the construction of suitable KPIs is discussed, which measure achievable goals and thus counteract wrong decisions. Finally, a list of individual problems with recommendations for practical implementation is given.

Authors

• Niklas, N.F., Franke
• Florian, F.M., Mohr
• André, A.H., Hennecke

You may follow the PHYSICS project activities on Twitter and LinkedIn.

The post Entwicklung und Auswahl geeigneter Use Cases und KPIs zur erfolgreichen Einführung neuer Hochtechnologie am Beispiel von “Function-as-a-Service” appeared first on PHYSICS.

Conference: 2023 14TH IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

ABSTRACT

Serverless computing has reshaped the cloud com- puting landscape by offering benefits such as auto-scalability, streamlined operational management, and granular billing. As its adoption grows, challenges related to performance optimization in hybrid architectures combining private servers and public cloud clusters have emerged. Central to these challenges are achieving optimal response latency and balancing performance and cost. To address these challenges, this paper introduces an adaptive routing service specifically designed for hybrid environments, proficient in leveraging real-time function metrics. Our proposed service pivots on three integral components: a monitor that captures performance metrics and raises alarms for predefined anomalies; a forecaster that predicts function latency across clusters, which includes wait and execution times, and produces request distributions for each cluster to equalize the overall function latency; and a router then processes incoming requests, taking cues from the forecaster’s predictions. Notably, based on user-defined objectives, the forecaster can be directed to either minimize latency or optimize execution costs. Comprehen- sive evaluations on AWS and Azure using Apache OpenWhisk clusters showcase our approach’s effectiveness, yielding a 9% improvement in average latency, a 45% decrease in standard deviation latency and a 17% cost reduction compared to con- ventional 50-50 routing. The advantages of elevated monitoring frequency are also illuminated, emphasizing quicker convergence times.

Authors

• Georgios Fatouros
• George Kousiouris
• Georgios Makridis
• John Soldatos
• Michael Filippakis
• Dimosthenis Kyriazis

You may follow the PHYSICS project activities on Twitter and LinkedIn.

The post Enhanced Routing for Serverless Functions: A Performance-based Approach with Runtime Adaptation appeared first on PHYSICS.

For Fujitsu, computational science research is one of the cornerstones of a successful IT service provider. Whether it is the development of quantum computers, new solutions for more sustainable and effective mobility, energy-saving HPC systems or the Digital Factory – alone and with international partners, Fujitsu always aims to create positive added value for society, the environment, and its customers. This is why Fujitsu regularly participates in European, more research-oriented tenders and projects with a wide range of partners.

As part of the FaaS paradigm that applies to the “PHYSICS” platform, Fujitsu has set itself the goal of contributing various optimization functions. The term optimization can be understood to cover a wide range of activities or functions: an optimized process, the provision of specific data to be able to make (optimized) decisions more quickly, the optimal use of time or the optimized packaging of content in a package. From a mathematical point of view, the latter is a real optimization problem, more precisely a problem from the field of combinatorial optimization. Combinatorial optimization problems include sequencing, assignment, grouping and selection problems. These include, for example, the traveling salesman problem, the Knapsack problem, graph similarity, portfolio optimization, (scheduling) planning, (task) assignment, software validation, AI model optimization and many others. Behind these somewhat stubborn names are everyday problems from logistics, the packaging industry, the manufacturing industry, shift planning and many others. What all these problems have in common is that there is not just one solution to them, but that there may well be many qualitatively different solutions. Furthermore, there is no known algorithm that can simply calculate these problems directly. Finding a good or the best solution in large problem spaces therefore requires an enormous amount of computing capacity and time.

As part of the “PHYSICS” project, Fujitsu has developed various optimization patterns that benefit from the performance of the quantum-inspired technology “Digital Annealer“. The Digital Annealer can solve combinatorial optimization problems particularly quickly and with particularly good results. To do this, it uses several ideas from annealing and quantum computing to solve the problems described above particularly well and quickly. But first, the problem in question is converted into a specific mathematical model, which the digital annealer can then solve. The highlight: if quantum computers are one day able to solve similarly large problems, the mathematical model already developed can simply be transferred to them and solved. The technology therefore not only provides a relevant advantage in business today, but also prepares the company in the best possible way for the age of quantum computers.

The physics project now provides two patterns for optimization problems and their solution with the help of the Digital Annealer in Node-Red. These processes can be transferred to the runtime environment with just a single click. To simplify the use of the Digital Annealer and its integration into the Node-Red ecosystem, we have implemented various backbone classes that enable the use of the Digital Annealer and the processing of optimization problems within Node-Red.

In this context, JavaScript classes were implemented that make it possible to formulate an optimization problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem that can be solved on the Digital Annealer. For this purpose, various classes have been implemented that support different operating modes of the Digital Annealer. Currently, the Digital Annealer has two operating modes for solving a QUBO problem, each offering different functionalities. In addition, classes are also provided to facilitate the interpretation and presentation of the results returned by the Digital Annealer. These classes are integrated into the Node Red ecosystem and are readily available as Node Red workflow blocks. Users have the option to use both the Backbone JavaScript classes and the Node Red blocks according to their own requirements and preferences.

To demonstrate the use of the provided JavaScript classes and workflow blocks, two solution patterns are provided in Node-Red: “Two Persons Assignment” (TPA) and the “Traveling Salesman Problem” (TSP). In the classic “Two Persons Assignment Problem”, a set of items representing tasks or projects must be assigned to two persons or machines in such a way that the difference in the workload or items assigned to the two is minimized. The TSP, on the other hand, asks the following question: “What is the shortest possible route that visits each city exactly once and then returns to the starting city?”, given a list of cities and their coordinates. The TSP is an NP-hard combinatorial optimization problem that plays an important role in theoretical computer science and operations research. The enormous power of the Digital Annealer can be used by Node-Red to solve the above-mentioned problems. If you want to implement further patterns, we would also like to recommend our Digital Annealer Tutorial. However, we know that solving a specific optimization problem on quantum or quantum-inspired hardware requires a QUBO formulation, which can be a very complex task. In this regard, the Fujitsu Digital Annealer team will be happy to assist you in formulating your problem and solving it through Digital Annealer.

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The PHYSICS project, held its 8th General Assembly meeting on November 8th – 9th, 2023, in Florence, Italy. Hosted by GFT, the meeting provided an opportunity for partners to present updates on task progress and deliverables, as well as discuss upcoming project results. The coordinator and leaders of the work packages (WP) led discussions on any outstanding issues, fostering active participation and constructive feedback.

The meeting was conducted using a hybrid format, accommodating both in-person and remote attendees. Among the meeting’s key objectives were the status of PHYSICS’ 2nd Iteration Pilot Implementation, the finalization of the Final Review demo, and the definition of the Final Review agenda and logistics.

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The German Research Center for Artificial Intelligence (DFKI) carried out the 3-week tests (from September 4 to 23, 2023) of the PHYSICS platform to assess the goals targeted by the Smart Manufacturing Use Cases:

1. Deployment of substitute service in the Cloud
2. High Confidence Quality Control

(see Blogpost: Industrial Use Cases of FaaS: The basics you need to know for more details)

The evaluation phase was held from 8:00 a.m. to 8:00 p.m., Monday through Saturday. The test consisted of calling the High Confidence Quality Control PHYSICS Cloud deployed function, resulting in approximately 20000 requests per week. This gave the total number of 60177 requests with 59425 successful responses (98.75%), even though PHYSICS is still in beta testing.

Only one major problem with the PHYSICS platform occurred on Monday afternoon during the first week of testing, which, once noticed, was fixed relatively quickly on Tuesday. This can be seen in the weekly results where the successful response rate was 96.47% in the first week, 99.82% in the second week and 99.96% in the third week.

Considering the first Smart Manufacturing Use Case PHYSICS Cloud Deployment is only meant to be used as a backup solution in case when the DFKI’s PHYSICS Edge Deployment goes out of service. Therefore, the availability of the PHYSICS platform (percentage of successful responses) at this stage of development was assessed as satisfactory.

In addition, for every single request the action time[1] and the total time[2] were recorded. Both Smart Manufacturing Use Cases required an average of less than 5 seconds for a Quality Check. The performance of PHYSICS fulfilled this condition which can be seen in Figure 1.

[1] only function execution time

[2] total request round-trip time

It is important to highlight that the total time for the successful request is consistent as shown in the histogram presented in Figure 2. The only outliers are the required cold starts of the functions each day after not being used overnight (not shown in the histogram, since they last longer than 10 seconds).

Because of the pay-as-you-go model of Function as a Service in general, our secondary goal of reducing costs was also achieved. About 20000 requests per week, each lasting less than 2 seconds would cost less than 1€ per month in operational costs[1]. Of course, if we need more requests or even more complex quality checks, operating time will increase, and therefore cost will scale linearly with it. Due to the simplified development and deployment, replacing our functions in the future will be straightforward, further reducing (development) costs.

To sum up, the tests conducted for Smart Manufacturing Use Cases proved the potential of the PHYSICS. Considering that the platform is still at the testing stage, its availability, performance, and chance to reduce development costs were evaluated as satisfactory.

[1] Estimated cost based on comparable FaaS platforms on the market.

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Conference: 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)

ABSTRACT

The integration of cloud computing and Internet of Things (IoT) technologies has brought significant advancements in the agriculture domain. However, the implementation of such systems often requires significant time and resources, making it challenging for smart agriculture providers to offer optimized yet affordable services for small and medium-sized farmers at scale. Low-code development platforms can be a viable solution to address these challenges, enabling non-experts to adapt or enhance existing applications with minimal coding. This paper presents a low-code approach to enhance smart agriculture scenarios with pattern-oriented functionality blocks for cloud/edge collaboration. It highlights the usage of a pattern collection for redesigning the implementation of smart agriculture applications that can enhance the data collection process as well as real-time decision-making and efficient resource management in the continuum. The effectiveness of the presented approach is demonstrated through the implementation of a case study in smart agriculture greenhouses. Evaluation results show that this approach can significantly reduce the time and effort required to deploy smart agriculture applications and provide data resilience.

Authors

• Georgios Fatouros
• George Kousiouris
• Theophile Lohier
• Georgios Makridis
• Ariana Polyviou
• John Soldatos
• Dimosthenis Kyriazis

You may follow the PHYSICS project activities on Twitter and LinkedIn.

The post Enhancing Smart Agriculture Scenarios with Low-code, Pattern-oriented functionalities for Cloud/Edge collaboration appeared first on PHYSICS.

ABSTRACT

This paper presents a comprehensive collection of reusable artifacts for addressing security and privacy issues in the context of flow-based programming in Function-as-a-Service (FaaS) environments. With the rapid adoption of FaaS platforms, it becomes important to guarantee the security and privacy of applications. The presented artifacts incorporate a wide variety of nodes and techniques into the popular Node-RED architecture. They intend to improve the security and privacy of applications by addressing critical aspects such as secure data flow management, code authenticity and validation, access control mechanisms, and runtime monitoring and anomaly detection. Using these artifacts, developers can construct more robust and resilient applications in FaaS environments while mitigating potential security and privacy risks.

Authors

• Thodoridis Ioannidis
• Vaios Bolgouras
• Christos Xenakis
• Ilias Politis

You may follow the PHYSICS project activities on Twitter and LinkedIn.

The post Securing the Flow: Security and Privacy Tools for Flow-based Programming appeared first on PHYSICS.

ABSTRACT

In this paper we report our experiences from the migration of an AI model inference process, used in the context of an E-health platform to the Function as a Service model. To that direction, a performance analysis is applied, across three available Cloud or Edge FaaS clusters based on the open source Apache Openwhisk FaaS platform. The aim is tExperimental data can aid in gaining insights about a system operation, as well as determining critical aspects of a modelling or simulation process. In this paper, we analyze the data acquired from an extensive experimentation process in a serverless Function as a Service system (based on the open source Apache Openwhisk) that has been deployed across 3 available cloud/edge locations with different system setups. Thus, they can be used to model distribution of functions through multi-location aware scheduling mechanisms. The experiments include different traffic arrival rates, different setups for the FaaS system, as well as different configurations for the hardware and platform used. We analyse the acquired data for the three FaaS system setups and discuss their differences presenting interesting conclusions with relation to transient effects of the system, such as the effect on wait and execution time. We also demonstrate interesting trade-offs with relation to system setup and indicate a number of factors that can affect system performance and should be taken under consideration in modelling attempts of such systems.

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The post Empirical Investigation of Factors influencing Function as a Service Performance in Different Cloud/Edge System Setups appeared first on PHYSICS.

Automatic understanding of patients to drive personalized interventions is becoming important in personalized healthcare, especially when patients are suffering from chronic conditions, ideally spending most of their patient journey away from clinical facilities. In most cases using models to infer on patients is a sporadic task, each patient being processed once or a few times per day. Deployed inference services are mostly inactive, occasionally serving bursts of requests. This is a clear indication of benefits that can be achieved if such services are deployed as functions. PHYSICS provides the tools for designing, testing, deploying and evaluating such Functions as a Service (FaaS). Engineers can use the provided Design Environment (DE) to first design their service. The PHYSICS DE integrates Node-RED and extends its palette of nodes, facilitating graphical service implementation as a flow. Some of the nodes do accept Javascript and Python scripts for advanced functionality. The flow can be tested locally, optimized and finalized.

The DE then provides the means to build the flow into an action that can be invoked within OpenWhisk. A Jenkins pipeline is executed, and as a result, the engineer comes up with a new action from their flow.

The deployed actions are then tested via flows dedicated to invoking deployed actions. The outputs of the deployed actions are compared to those of the local flows.

Finally, deployed actions can be evaluated under different scenarios implemented using dedicated load generator nodes.

The results of such tests can be as follows, where experiments last 2 minutes continuous requests, each request arriving at a fixed delay after the previous. As long as the delays are larger than the inference time, the achieved rate follows the increase of that of a system with infinite resources. When the delay drops below the execution time, then the achieved rate reaches a plateau. Even more frequent requests push Openwhisk beyond the accepted maximum request level, dropping the requests, resulting to a performance collapse.

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