# An urgent computing oriented architecture for dynamic climate risk management framework

Shahrzad Pour, Daniel Balouek, Amir Masoumi, Martin Brynskov

► To cite this version:

Shahrzad Pour, Daniel Balouek, Amir Masoumi, Martin Brynskov. An urgent computing oriented architecture for dynamic climate risk management framework. UCC '23: IEEE/ACM 16th International Conference on Utility and Cloud Computing, Dec 2023, Taormina, Italy. pp.1-4, 10.1145/3603166.3632539. hal-04387718v2

HAL Id: hal-04387718

https://inria.hal.science/hal-04387718v2

Submitted on 30 Oct 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# An urgent computing oriented architecture for dynamic climate risk management framework

Shahrzad Pour

shmp@dtu.dk, shmp@maxint.dk

DTU Compute, Technical University of Denmark

Lyngby, Denmark

MAXINT, Denmark

Amir Masoumi

masoomi.am@gmail.com

Volunteer Researcher

Perth, Australia

Daniel Balouek

daniel.balouek@inria.fr

IMT Atlantique, INRIA, LS2N, UMR CNRS 6004, F-44307

Nantes, France

SCI Institute, University of Utah, USA

Martin Brynskov

mbryn@dtu.dk

DTU Compute, Technical University of Denmark

Lyngby, Denmark

## ABSTRACT

The Computing Continuum drives holistic system integration, especially in the road sector, where there's a growing need for comprehensive Decision Support Systems in operation and maintenance strategies based on sensor data. These systems offer dynamic assessments, critical for managing road assets vulnerable to climate change and extreme weather events. The resulting data uncertainties, including variations in data veracity and variability require timely decision-making for coordination and adaptiveness of the system as a whole. We introduce an urgent computing architecture for a dynamic risk management framework, discussing its related challenges and software design patterns.

## ACM Reference Format:

Shahrzad Pour, Daniel Balouek, Amir Masoumi, and Martin Brynskov. 2023. An urgent computing oriented architecture for dynamic climate risk management framework. In (UCC '23). ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3492323.3495590

## 1 INTRODUCTION

The impact of climate change heavily influences operation and maintenance activities of critical infrastructure at strategic, tactical, and operational decision levels [13], [11]. Extreme weather events impact transportation systems, including roads, rails, and electrical lines, to a significant degree. An illustration of this can be seen in the trends of economic losses caused by climate-related events in EEA member states from 1980 to 2020, which total over 500 million euros. A substantial 80% of this sum is attributable to severe weather phenomena, whereas insured losses comprised a mere 25% to 33% of the total losses [7]. Although the financial implications of climate change are well understood, there is still a lack of information concerning its operational implications, particularly with regard to precautionary measures against the inevitable changes that arise.

<figure><img src="image_3.png" /></figure>

This work is licensed under a Creative Commons Attribution International 4.0 License. UCC '23, December 6-9, 2023, Messina, Italy

© 2023 Association for Computing Machinery. ACM ISBN 978-1-4503-9163-4/21/12...$15.00 https://doi.org/10.1145/3492323.3495590

The Internet of Things has been one of the enablers of smart services across various domains. At a higher level of abstraction, it encourages a holistic approach to software design by fostering the integration of multiple isolated systems. In practice, the physical division of these systems into distinct sub-units results in the management of separate silos, to the detriments of cross-domain decision making. At the same time, modern applications are moving in the direction of the Computing Continuum, a distributed approach where Cloud, Edge and IoT aim to collaborate for publishing, cleaning and computing data while managing aspects of performance, security and QoS [6, 9].

A class of time-sensitive systems and applications known as Urgent Computing makes use of distributed data sources to speed up the making of important decisions [3]. Similar requirements apply to risk assessment for road infrastructure, specifically for predictive models, reactive/adaptive planning, and KPI monitoring, as outlined in [10]. In order to avoid or mitigate the adverse effects of climate change on road infrastructure, there is an opportunity to use distributed resources throughout the Computing Continuum and leverage urgent computing principles to predict the outcomes of scenarios [3]. In the context of road safety, the wide array of system capacities and varying service capabilities, alongside the significant uncertainty stemming from disparities in data quality and availability, present the primary challenges in constructing an adaptive Decision Support System (DSS) [21]. Addressing the vulnerability of road assets to weather conditions, the authors have incorporated a climate risk management framework in line with the definition provided in [18]. This comprehensive risk management framework is typically recognized as either an independent module or an essential component within a DSS, often implemented through iterative processes as outlined in [18].

In this paper, we propose a proactive, holistic architecture for risk management and road safety. This is a major shift in the way transport networks function, and it requires the ability to effectively estimate road conditions risk as a function of operating conditions and information technology. By integrating IoT and climate risk data into all decision-making levels, we move away from current approaches. Our method combines an architecture for data-driven workflows across systems with the needs of a big data pipeline for a risk management framework. The goal is to address the uncertainty surrounding climate data (veracity) and the demanding time and
quality constraints when making decisions based on significant variations in data patterns (*variability*).

The remainder of the paper is organized as follows. Section 2 discusses the motivation, background, and challenges for this work. Section 3 proposes an architectural vision for the risk management framework. Finally, in Section 4, we summarize this paper and discuss future works.

# 2 MOTIVATION, BACKGROUND AND CHALLENGES

## 2.1 Motivating Use Case

During severe weather events, there's a significant surge in data from diverse sensors and weather stations, especially upon surpassing specific thresholds. Urgent data preprocessing becomes crucial due to immediate actions required during these events and uncertainties regarding the number of interoperable subsystems necessitating data exchanges. This surge affects operation and maintenance activities, presenting two primary challenges: insufficient **coordination** and **adaptiveness** among agencies.

## 2.2 Background

**Holistic Decision Support Systems in road sector** The integration of decision-making processes within road domain literature has been relatively underexplored. Early studies, like [15], focused on integrating maintenance into Pavement Management Systems. Recent research, such as [16], delves into real-time GPS and GIS technology integration. While [19] offers a comprehensive survey of advancements in the road sector, rarely studies [21] explicitly addresses the vital requirement for adaptation mechanisms to handle uncertainties within Decision Support Systems (DSSs).

**Architecture-oriented approaches for addressing the Continuum.** Architectural approaches are vital for managing the Computing Continuum, introducing unique challenges concerning sub-component control and adaptation. The aggregation of architectural and algorithmic challenges within individual components [6, 9] poses complexities as these subcomponents inherently influence each other while conducting urgent analytics [1]. Predicting infrastructure volatility and meeting the demands of urgent analytics is challenging due to their unpredictable nature [5]. To best of our knowledge, leveraging the Computing Continuum for this application constitutes a novel direction in the field.

**Dynamic Risk Management framework** A dynamic risk management framework involves five iterative steps: context establishment, risk assessment using various techniques and algorithms, exploration of mitigation strategies (often in consultation with stakeholders), implementation of these measures, and continuous monitoring and review through KPI dashboards. This comprehensive definition is part of an overview paper on adaptive decision support systems in the context of climate change [18] and is depicted in Figure 1. Bayesian Believe Network (BBN) as a bottom-up approach [18] has been the chosen approach [21]. However, the details of risk assessment model is not the focus of this paper, the risk assessment model can continually mature over time using the BBN model, integrating stakeholders' beliefs or new inputs whenever necessary.

<figure><img src="image_4.png" /><figcaption>Figure 1: Dynamic Risk Management Framework</figcaption></figure>

## 2.3 Challenges

Urgent data-driven workflows must be efficiently coordinated by developers and service providers in order to construct time-sensitive decision-making pipelines. Accommodating the considerable infrastructure heterogeneity, this coordination must address uncertainties related to data source dependability and availability [17]. In the realm of road safety, three specific challenges stand out:

**Automated data collection** Currently, operational technologies in this domain are functionally silo applications [21]. Edge technologies can provide incentives to sanitize data and respect the privacy of individual systems while allowing for the sharing of information to make the system unbiased and flexible.

**Autonomics** Programming abstractions that allow the specification of application behaviors that can react to unforeseen events during runtime are therefore critical to the overall performance of applications deployed throughout the continuum [12, 14]. Furthermore, to enable resource and execution path adaptability across the continuum, autonomic runtime techniques are required [20].

**Integration of Smart Solvers:** Decision Systems are essentially alike to solving combinatorial problems. Constraint programming (CP), particularly in the form of Constraint Satisfaction Problem (CSP), is well-suited for generating feasible planning solutions and strategies swiftly in scenarios involving real-time uncertainties across various resource classes. Additionally, Constraint Optimization Problem (COP) is a viable option when time and resources permit for plan optimization.

**Online/Real-time Communication:** Modern data services and messaging protocols allows and simplifies the development of a holistic DSS [8]. The current state of the management systems used in the road sector (PMSs & MMSs) do not utilize direct data exchange and communication between the systems. Introducing online and real-time communication between systems in a unified architecture that includes both management components, will allow for knowledge-oriented communication.

# 3 ARCHITECTURE

## 3.1 Architecture Goals: Functionality and Interoperability Design

The design goals of this architecture are as follows:

3.1.1 **Functional Requirement:** The architecture should consider the required data sources, end-to-end process, actors, and deployment:

**Data sources:** Various static and dynamic data sources are involved
in the use case, including the meteorological and hydrological data, flood-sensitive areas (blue spot data model [2]), and road infrastructure condition data.

**End-to-end Process:** The pipeline should address the data collection, orchestrating operations, and demanded data operations i.e. modules of the risk management framework in uncertainty situations, including extreme weather events and climate changes.

**Actors:** Actors include infrastructure owners or maintainers, with road authorities and municipalities being predominant in the former category and consulting companies in the latter. Additional actors could include citizens, particularly if the system is used as an early warning system to notify them about extreme weather events.

**Deployment:** The system as an infrastructure as code per stakeholder or per region, while most subsystems are partly autonomous on the device edges or fog but able to receive the task upon decision by the system.

### 3.1.2 Interoperability as quality attribute
The required architecture for the dynamic risk management framework demands interoperability. As shown in Figure 2, there are three interoperability aspects involved in the problem:

**Level 3** - Horizontal interoperability (Over Actors – orange dotted line): Infrastructure owners, road authorities, municipalities, and consulting companies are key actors in the domain, with a focus on their involvement in the problem.

**Level 2** - Horizontal interoperability (Over systems in various geographical regions – violet dotted line): The architecture must support cross-sectorial applications in environmental and mobility/-transport domains across different geographical regions, ensuring Continuum software abstraction.

**Level 1** - Vertical interoperability (Over data spaces – blue dotted lines): The system should seamlessly integrate within a broad spectrum of digital solutions and sub-systems, particularly addressing urgent computing demands.

<figure><img src="image_5.png" /><figcaption>Figure 2: Horizontal and Vertical Interoperability of the Dynamic Risk Assessment Domain</figcaption></figure>

### 3.1.3 Urgent Services
The architecture should address the necessary services required for fulfilling urgent tasks, including identifying available computing resources and data sources within the computing environment as well as ensuring that resources are allocated efficiently to meet the immediate computation demand.

## 3.2 Proposed Architecture

Figure 3 depicts our software architecture, responsible for gathering data from diverse sources such as road asset conditions, flood-sensitive areas, and various climatological data including precipitation, zero-crossing (indicating fluctuations around zero degrees), and sea-level rise. This data is fed into the pipeline, triggering an 'event receiver' for extreme weather events and data changes. Orchestration manages essential services, communicating through a message broker. The resulting information is utilized by applications.

### 3.2.1 Addressing Functional requirements
We endorse an event-driven microservice architecture as our chosen architectural style, wherein 'event-driven' characterizes our coordination/communication model, and 'microservices' define the pattern for allocating responsibilities.

**Microservice architecture for allocation of responsibilities:** The module of orchestration service, and all the modules of Interaction, Discovery, and Optimization as part of urgent computing modules plus the modules of dynamic risk assessment are developed each as one separate microservice.

**Event-Driven Architecture as coordination/communication model:** Event-driven architecture utilizes a message broker to reduce coupling between producers and consumers. This intermediary element effectively handles high volumes of data at different speeds by storing data or notifications in a queue until resources are available for processing.

### 3.2.2 Interoperability tactics
Among two main categories of interoperability tactics i.e. locate and manage interfaces [4], we use below tactics:

**Urgent Discovery Service: a locate tactic:** When it comes to addressing urgent requirements for processing extreme weather events, the initial step involves activating a discovery service. This service is tasked with identifying and pinpointing accessible data sources residing in edge, fog, or cloud as data spaces.

**Interaction Service: an Interface tactic** This service acts as an interface to interact with multiple external subsystems and various actors. A RESTful API is designed for minimal interdependency between clients and servers. It is worth mentioning the efforts made to establish minimal interoperability standards and tools for enhancing data, system, and service interoperability between cities and supplier stakeholders [22].

**Orchestration Service: an interface tactic** The orchestrator acts as a middleware enabling the composition of services based on specific goals and content, rather than directly engaging with devices or names. In this scenario, the architecture becomes domain-agnostic, and interoperability is enhanced through the use of discovery services and optimization services to address resource uncertainty and data availability.

**Optimization Service: a locate tactic:** This service plays a crucial role in efficiently and effectively managing computational
<figure><img src="image_6.png" /><figcaption>Figure 3: Proposed Urgent Computing Architecture for Dynamic Risk Management Framework</figcaption></figure>

resources to meet the urgent demands of tasks in the whole data space environment to make sure all the systems and subsystems are interoperable resource-wise towards a common goal. The service is modeled as CSP, in case of urgency and is run as a COP upon having enough resources time-wise.

## 4 CONCLUSION AND FUTURE WORKS

In this study, we've integrated an urgent computing architecture into a dynamic risk management framework to manage time-sensitive tasks during extreme weather events. This approach addresses big data challenges posed by variability and reliability across IoT-Edge-Cloud resources. The architecture's implementation and evaluation will be based on real data from the Danish Meteorological and Hydrological Institutes (DMI, and DHI), coupled with road condition data from authorities. Furthermore, our plan includes enhancing the risk assessment model by integrating pavement bearing capacity data to evaluate pavement structure functionality.

## ACKNOWLEDGMENTS

Thanks to the ELEXIA (Horizon Europe No. 101075656) and IEA EBC - Annex 81 - Data-Driven Smart Buildings (EUDP Project No. 64019-0539) projects, which have supported partly this research. This research is supported in part by the NSF under OAC 2238064.

## REFERENCES

[1] Russell L Ackoff. 1971. Towards a system of systems concepts. *Management science* 17, 11 (1971), 661–671.

[2] Christian Axelsen and MR Larsen. 2014. Blue spot analysis: a key component in the climate adaptation of major Danish. In *TRA 5th Conference (CEDR)*.

[3] Daniel Balouek-Thomert, Ivan Rodero, and Manish Parashar. 2020. Harnessing the Computing Continuum for Urgent Science. *SIGMETRICS Perform. Eval. Rev.* 48, 2 (nov 2020), 41–46. https://doi.org/10.1145/3439602.3439618

[4] Len Bass, Paul Clements, and Rick Kazman. 2003. *Software architecture in practice*. Addison-Wesley Professional.

[5] Pete Beckman, Jack Dongarra, Nicola Ferrier, Geoffrey Fox, Terry Moore, Dan Reed, and Micah Beck. 2020. Harnessing the computing continuum for programming our world. *Fog Computing: Theory and Practice* (2020), 215–230.

[6] Victor Casamayor Pujol, Andrea Morichetta, Ilir Murturi, Praveen Kumar Donta, and Schahram Dustdar. 2023. Fundamental research challenges for distributed computing continuum systems. *Information* 14, 3 (2023), 198.

[7] European Environment Agency (EEA). 2022. Economic Losses and Fatalities from Weather-and Climate-Related Events in Europe.

[8] Al-Masri et al. 2020. Investigating messaging protocols for the Internet of Things (IoT). *IEEE Access* 8 (2020), 94880–94911.

[9] Balouek-Thomert et al. 2019. Towards a computing continuum: Enabling edge-to-cloud integration for data-driven workflows. *The International Journal of High Performance Computing Applications* 33, 6 (Nov. 2019), 1159–1174. https://doi.org/10.1177/1094342019877383

[10] Lasi et al. 2014. Industry 4.0. *Business & information systems engineering* 6, 4 (2014), 239–242.

[11] Qiao et al. 2020. Flexible pavements and climate change: A comprehensive review and implications. *Sustainability* 12, 3 (2020), 1057.

[12] Renart et al. 2019. An edge-based framework for enabling data-driven pipelines for iot systems. In *2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)*. IEEE, 885–894.

[13] Christopher B Field and Vicente R Barros. 2014. *Climate change 2014–Impacts, adaptation and vulnerability: Regional aspects*. Cambridge University Press.

[14] Gordon Gibb, Rupert Nash, Nick Brown, and Bianca Prodan. 2019. The Technologies Required for Fusing HPC and Real-Time Data to Support Urgent Computing. In *2019 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC)*. 24–34. https://doi.org/10.1109/UrgentHPC49580.2019.00009

[15] J Greenstein and S Hudson. 1998. Integrated Pavement and Maintenance Management for Jamaica. In *The 4th International Conference on Managing Pavements, Durban, South Africa*. Citeseer.

[16] AS Kachwalla and MI Hughes. 2019. Real time pavement asset management and integration with work order management. In *Airfield and highway pavements 2019: Design, construction, condition evaluation, and management of pavements*. ASCE Reston, VA.

[17] Sergey V. Kovalchuk, Pavel A. Smirnov, Sergey V. Maryin, Timofey N. Tchurov, and Vladislav A. Karbovskiy. 2013. Deadline-driven Resource Management within Urgent Computing Cyberinfrastructure. *Procedia Computer Science* 18 (2013), 2203–2212. https://doi.org/10.1016/j.procs.2013.05.391 2013 International Conference on Computational Science.

[18] Jean P Palutikof, Roger B Street, and Edward P Gardiner. 2019. Decision support platforms for climate change adaptation: an overview and introduction. *Climatic Change* (2019).

[19] Naga Siva Pavani Peraka and Krishna Prapoorna Biligiri. 2020. Pavement asset management systems and technologies: A review. *Automation in Construction* 119 (2020), 103336.

[20] Ioan et al. Petri. 2021. Autonomics at the Edge: Resource Orchestration for Edge Native Applications. *IEEE Internet Computing* 25, 4 (2021), 21–29. https://doi.org/10.1109/MIC.2020.3039551

[21] Shahrzad Pour, Amir Masoumi, and Niels Skov Dujardin. 2023. Towards a Technology-Driven Adaptive Decision Support System for Integrated Pavement and Maintenance strategies (TDADSS-IPM): focus on risk assessment framework for climate change adaptation. *arXiv preprint arXiv:2306.01769* (2023).

[22] UN Report. [n. d.]. Redefining smart city platforms: Setting the stage for Minimal Interoperability Mechanisms. A U4SSC deliverable on city platforms.