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Östberg, Per-Olov
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Publications (10 of 58) Show all publications
Townend, P., Martí, A. P., De La Iglesia, I., Matskanis, N., Ohlson Timoudas, T., Hallmann, T., . . . Abdou, M. (2023). COGNIT: challenges and vision for a serverless and multi-provider cognitive cloud-edge continuum. In: 2023 IEEE International Conference on Edge Computing and Communications (EDGE): . Paper presented at 2023 IEEE International Conference on Edge Computing and Communications (EDGE), Chicago, Illinois, USA, July 2-8, 2023 (pp. 12-22). IEEE
Open this publication in new window or tab >>COGNIT: challenges and vision for a serverless and multi-provider cognitive cloud-edge continuum
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2023 (English)In: 2023 IEEE International Conference on Edge Computing and Communications (EDGE), IEEE, 2023, p. 12-22Conference paper, Published paper (Refereed)
Abstract [en]

Use of the serverless paradigm in cloud application development is growing rapidly, primarily driven by its promise to free developers from the responsibility of provisioning, operating, and scaling the underlying infrastructure. However, modern cloud-edge infrastructures are characterized by large numbers of disparate providers, constrained resource devices, platform heterogeneity, infrastructural dynamicity, and the need to orchestrate geographically distributed nodes and devices over public networks. This presents significant management complexity that must be addressed if serverless technologies are to be used in production systems. This position paper introduces COGNIT, a major new European initiative aiming to integrate AI technology into cloud-edge management systems to create a Cognitive Cloud reference framework and associated tools for serverless computing at the edge. COGNIT aims to: 1) support an innovative new serverless paradigm for edge application management and enhanced digital sovereignty for users and developers; 2) enable on-demand deployment of large-scale, highly distributed and self-adaptive serverless environments using existing cloud resources; 3) optimize data placement according to changes in energy efficiency heuristics and application demands and behavior; 4) enable secure and trusted execution of serverless runtimes. We identify and discuss seven research challenges related to the integration of serverless technologies with multi-provider Edge infrastructures and present our vision for how these challenges can be solved. We introduce a high-level view of our reference architecture for serverless cloud-edge continuum systems, and detail four motivating real-world use cases that will be used for validation, drawing from domains within Smart Cities, Agriculture and Environment, Energy, and Cybersecurity.

Place, publisher, year, edition, pages
IEEE, 2023
Series
IEEE International Conference on Edge Computing, E-ISSN 2767-9918
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-214140 (URN)10.1109/EDGE60047.2023.00015 (DOI)001063201700002 ()2-s2.0-85173547015 (Scopus ID)979-8-3503-0483-1 (ISBN)979-8-3503-0484-8 (ISBN)
Conference
2023 IEEE International Conference on Edge Computing and Communications (EDGE), Chicago, Illinois, USA, July 2-8, 2023
Funder
EU, Horizon Europe, 101092711
Available from: 2023-09-05 Created: 2023-09-05 Last updated: 2023-11-06Bibliographically approved
Patel, Y. S., Townend, P. & Östberg, P.-O. (2023). Formal models for the energy-aware cloud-edge computing continuum: analysis and challenges. In: Lisa O'Conner (Ed.), 2023 IEEE international conference on service-oriented system engineering (SOSE): proceedings. Paper presented at 2023 IEEE International Conference on Service-Oriented System Engineering (SOSE), Athens, Greece, July 17-20, 2023 (pp. 48-59). IEEE
Open this publication in new window or tab >>Formal models for the energy-aware cloud-edge computing continuum: analysis and challenges
2023 (English)In: 2023 IEEE international conference on service-oriented system engineering (SOSE): proceedings / [ed] Lisa O'Conner, IEEE, 2023, p. 48-59Conference paper, Published paper (Refereed)
Abstract [en]

Cloud infrastructures are rapidly evolving from centralised systems to geographically distributed federations of edge devices, fog nodes, and clouds. These federations (often referred to as the Cloud-Edge Continuum) are the foundation upon which most modern digital systems depend, and consume enormous amounts of energy. This consumption is becoming a critical issue as society's energy challenges grow, and is a great concern for power grids which must balance the needs of clouds against other users. The Continuum is highly dynamic, mobile, and complex; new methods to improve energy efficiency must be based on formal scientific models that identify and take into account a huge range of heterogeneous components, interactions, stochastic properties, and (potentially contradictory) service-level agreements and stakeholder objectives. Currently, few formal models of federated Cloud-Edge systems exist - and none adequately represent and integrate energy considerations (e.g. multiple providers, renewable energy sources, pricing, and the need to balance consumption over large-areas with other non-Cloud consumers, etc.). This paper conducts a systematic analysis of current approaches to modelling Cloud, Cloud-Edge, and federated Continuum systems with an emphasis on the integration of energy considerations. We identify key omissions in the literature, and propose an initial high-level architecture and approach to begin addressing these - with the ultimate goal to develop a set of integrated models that include data centres, edge devices, fog nodes, energy providers, software workloads, end users, and stakeholder requirements and objectives. We conclude by highlighting the key research challenges that must be addressed to enable meaningful energy-aware Cloud-Edge Continuum modelling and simulation.

Place, publisher, year, edition, pages
IEEE, 2023
Series
IEEE International Symposium on Service-Oriented System Engineering, ISSN 2640-8228, E-ISSN 2642-6587
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-214709 (URN)10.1109/SOSE58276.2023.00012 (DOI)2-s2.0-85174930280 (Scopus ID)979-8-3503-2239-2 (ISBN)979-8-3503-2240-8 (ISBN)
Conference
2023 IEEE International Conference on Service-Oriented System Engineering (SOSE), Athens, Greece, July 17-20, 2023
Funder
The Kempe FoundationsEU, Horizon EuropeEU, Horizon 2020
Available from: 2023-09-25 Created: 2023-09-25 Last updated: 2023-11-02Bibliographically approved
Castañé, G., Dolgui, A., Kousi, N., Meyers, B., Thevenin, S., Vyhmeister, E. & Östberg, P.-O. (2023). The ASSISTANT project: AI for high level decisions in manufacturing. International Journal of Production Research, 61(7), 2288-2306
Open this publication in new window or tab >>The ASSISTANT project: AI for high level decisions in manufacturing
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2023 (English)In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 61, no 7, p. 2288-2306Article in journal (Refereed) Published
Abstract [en]

This paper outlines the main idea and approach of the H2020 ASSISTANT (LeArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments) project. ASSISTANT is aimed at the investigation of AI-based tools for adaptive manufacturing environments, and focuses on the development of a set of digital twins for integration with, management of, and decision support for production planning and control. The ASSISTANT tools are based on the approach of extending generative design, an established methodology for product design, to a broader set of manufacturing decision making processes; and to make use of machine learning, optimisation, and simulation techniques to produce executable models capable of ethical reasoning and data-driven decision making for manufacturing systems. Combining human control and accountable AI, the ASSISTANT toolsets span a wide range of manufacturing processes and time scales, including process planning, production planning, scheduling, and real-time control. They are designed to be adaptable and applicable in a both general and specific manufacturing environments.

Place, publisher, year, edition, pages
Taylor & Francis, 2023
Keywords
Artificial intelligence, data analytics, digital twins, process and production planning, reconfigurable manufacturing systems, scheduling and real-time control
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-198345 (URN)10.1080/00207543.2022.2069525 (DOI)000828974100001 ()2-s2.0-85134609832 (Scopus ID)
Available from: 2022-08-01 Created: 2022-08-01 Last updated: 2023-07-14Bibliographically approved
Östberg, P.-O., Vyhmeister, E., Castañé, G. G., Meyers, B. & Van Noten, J. (2022). Domain Models and Data Modeling as Drivers for Data Management: The ASSISTANT Data Fabric Approach. In: 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022: Nantes, France, 22-24 June 2022. Paper presented at 10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022, Nantes, France, June 22-24, 2022 (pp. 19-24). Elsevier
Open this publication in new window or tab >>Domain Models and Data Modeling as Drivers for Data Management: The ASSISTANT Data Fabric Approach
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2022 (English)In: 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022: Nantes, France, 22-24 June 2022, Elsevier, 2022, p. 19-24Conference paper, Published paper (Refereed)
Abstract [en]

To develop AI-based models capable of governing or providing decision support to complex manufacturing environments, abstractions and mechanisms for unified management of data storage and processing capabilities are needed. Specifically, as such models tend to include and rely on detailed representations of systems, components, and tools with complex interactions, mechanisms for simplifying, integrating, and scaling management capabilities in the presence of complex data requirements (e.g., high volume, velocity, and diversity of data) are of particular interest. A data fabric is a system that provides a unified architecture for management and provisioning of data. In this work we present the background, design requirements, and high-level outline of the ASSISTANT data fabric - a flexible data management tool designed for use in adaptive manufacturing contexts. The paper outlines the implementation of the system with specific focus on the use of domain models and the data modeling approach used, as well as provides a generic use case structure reusable in many industrial contexts.

Place, publisher, year, edition, pages
Elsevier, 2022
Series
IFAC-PapersOnLine, E-ISSN 2405-8963 ; 55:10
Keywords
adaptive manufacturing, AI, Data Base, Data Fabric, Data Lake, Data Modeling, Domain Models, Knowledge Graph
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:umu:diva-202079 (URN)10.1016/j.ifacol.2022.09.362 (DOI)000881681700004 ()2-s2.0-85144487512 (Scopus ID)
Conference
10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022, Nantes, France, June 22-24, 2022
Available from: 2023-01-03 Created: 2023-01-03 Last updated: 2023-09-05Bibliographically approved
Vyhmeister, E., Castane, G. G., Buchholz, J. & Östberg, P.-O. (2022). Lessons learn on responsible AI implementation: the ASSISTANT use case. In: 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022: Nantes, France, 22-24 June 2022. Paper presented at 10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022, Nantes, France, June 22-24, 2022 (pp. 377-382). Elsevier
Open this publication in new window or tab >>Lessons learn on responsible AI implementation: the ASSISTANT use case
2022 (English)In: 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022: Nantes, France, 22-24 June 2022, Elsevier, 2022, p. 377-382Conference paper, Published paper (Refereed)
Abstract [en]

Currently, pioneer companies are working hard to construct applied ethical frameworks in different sectors for using AI components that generate trust in their clients and workforce. However, independent of these few companies, there is still a considerable gap between understanding the impact of using responsible AI components, the implications of the lack of use, and what is currently applied in the industrial sector. Given that industry has shown an increased commitment to incorporating AI components, works focus on broadening the understanding of manufacturing sector stakeholders of what approaches could be considered within AI life-cycle, reducing the gap between principles and actionable requirements, and defining fundamental considerations based on risk management for incorporating, and managing, AI-based on responsible AI are required. In this work, we present a summary of the most suitable approaches that can be used for implementation and the lessons learned from a European Funded project (ASSISTANT).

Place, publisher, year, edition, pages
Elsevier, 2022
Series
IFAC-PapersOnLine, E-ISSN 2405-8963 ; 55:10
Keywords
AI, AI en manufacturing, AI ethics, design methodology for HMS, Human centered automation, responsible AI, standardisation
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:umu:diva-202078 (URN)10.1016/j.ifacol.2022.09.422 (DOI)000881681700064 ()2-s2.0-85144495935 (Scopus ID)
Conference
10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022, Nantes, France, June 22-24, 2022
Available from: 2023-01-03 Created: 2023-01-03 Last updated: 2023-01-03Bibliographically approved
Tarneberg, W., Fitzgerald, E., Bhuyan, M. H., Townend, P., Arzen, K.-E., Östberg, P.-O., . . . Kihl, M. (2022). The 6G Computing Continuum (6GCC): Meeting the 6G computing challenges. In: 2022 1st International Conference on 6G Networking, 6GNet 2022: . Paper presented at 1st International Conference on 6G Networking, 6GNet, 6-8 July 2022, Paris, France. IEEE Computer Society
Open this publication in new window or tab >>The 6G Computing Continuum (6GCC): Meeting the 6G computing challenges
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2022 (English)In: 2022 1st International Conference on 6G Networking, 6GNet 2022, IEEE Computer Society, 2022Conference paper, Published paper (Refereed)
Abstract [en]

6G systems, such as Large Intelligent Surfaces, will require distributed, complex, and coordinated decisions through-out a very heterogeneous and cell free infrastructure. This will require a fundamentally redesigned software infrastructure accompanied by massively distributed and heterogeneous computing resources, vastly different from current wireless networks. To address these challenges, in this paper, we propose and motivate the concept of a 6G Computing Continuum (6GCC) and two research testbeds, to advance the rate and quality of research. 6G Computing Continuum is an end-to-end compute and software platform for realizing large intelligent surfaces and its tenant users and applications. One for addressing the challenges or orchestrating shared computational resources in the wireless domain, implemented on a Large Intelligent Surfaces testbed. Another simulation-based testbed is intended to address scalability and global-scale orchestration challenges.

Place, publisher, year, edition, pages
IEEE Computer Society, 2022
Keywords
6G, Computing at Scale, Computing Continuum, Distributed Orchestration, Large Intelligent Surfaces
National Category
Computer Sciences Computer Systems
Identifiers
urn:nbn:se:umu:diva-203080 (URN)10.1109/6GNet54646.2022.9830459 (DOI)000860313400032 ()2-s2.0-85136138862 (Scopus ID)9781665467636 (ISBN)
Conference
1st International Conference on 6G Networking, 6GNet, 6-8 July 2022, Paris, France
Available from: 2023-01-17 Created: 2023-01-17 Last updated: 2023-01-17Bibliographically approved
Beldiceanu, N., Dolgui, A., Gonnermann, C., Gonzalez-Castañé, G., Kousi, N., Meyers, B., . . . Östberg, P.-O. (2021). Assistant: Learning and robust decision support system for agile manufacturing environments. In: IFAC-PapersOnLine: . Paper presented at INCOM 2021, 17th IFAC Symposium on Information Control Problems in Manufacturing, Virtual, June 7-9, 2021 (pp. 641-646). Elsevier
Open this publication in new window or tab >>Assistant: Learning and robust decision support system for agile manufacturing environments
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2021 (English)In: IFAC-PapersOnLine, Elsevier, 2021, p. 641-646Conference paper, Published paper (Refereed)
Abstract [en]

The European project ASSISTANT will provide a set of AI-based digital twins that helps process engineers and production planners to operate collaborative mixed-model assembly lines based on the data collected from IoT devices and external data sources. Such a tool will help planners to design the assembly line, plan the production, operate the line, and improve process tuning. In addition, the system monitors the line in real-time, ensures that all required resources are available, and allows fast re-planning when necessary. ASSISTANT aims to make cost-effective decisions while ensuring product quality, safety and wellbeing of the workers, and managing the various sources of uncertainties. The resulting digital twin systems will be data-driven, agile, autonomous, collaborative and explainable, safe but reactive.

Place, publisher, year, edition, pages
Elsevier, 2021
Series
IFAC-PapersOnLine, ISSN 2405-8963 ; 54:1
Keywords
Artificial intelligence, Data analytics, Decision aid, Digital twins, Process and production planning, Real-time control, Reconfigurable manufacturing systems, Scheduling
National Category
Computer Sciences Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:umu:diva-191303 (URN)10.1016/j.ifacol.2021.08.074 (DOI)000716937600107 ()2-s2.0-85119693666 (Scopus ID)
Conference
INCOM 2021, 17th IFAC Symposium on Information Control Problems in Manufacturing, Virtual, June 7-9, 2021
Funder
EU, Horizon 2020, ICT-38-2020
Available from: 2022-01-13 Created: 2022-01-13 Last updated: 2023-03-24Bibliographically approved
Leznik, M., Michalsky, P., Willis, P., Schanzel, B., Östberg, P.-O. & Domaschka, J. (2021). Multivariate Time Series Synthesis Using Generative Adversarial Networks. In: ICPE 2021: Proceedings of the ACM/SPEC International Conference on Performance Engineering. Paper presented at 2021 ACM/SPEC International Conference on Performance Engineering, ICPE 2021 (pp. 43-50). Association for Computing Machinery, Inc
Open this publication in new window or tab >>Multivariate Time Series Synthesis Using Generative Adversarial Networks
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2021 (English)In: ICPE 2021: Proceedings of the ACM/SPEC International Conference on Performance Engineering, Association for Computing Machinery, Inc , 2021, p. 43-50Conference paper, Published paper (Refereed)
Abstract [en]

Collection and analysis of distributed (cloud) computing workloads allows for a deeper understanding of user and system behavior and is necessary for efficient operation of infrastructures and applications. The availability of such workload data is however often limited as most cloud infrastructures are commercially operated and monitoring data is considered proprietary or falls under GPDR regulations. This work investigates the generation of synthetic workloads using Generative Adversarial Networks and addresses a current need for more data and better tools for workload generation. Resource utilization measurements such as the utilization rates of Content Delivery Network (CDN) caches are generated and a comparative evaluation pipeline using descriptive statistics and time-series analysis is developed to assess the statistical similarity of generated and measured workloads. We use CDN data open sourced by us in a data generation pipeline as well as back-end ISP workload data to demonstrate the multivariate synthesis capability of our approach. The work contributes a generation method for multivariate time series workload generation that can provide arbitrary amounts of statistically similar data sets based on small subsets of real data. The presented technique shows promising results, in particular for heterogeneous workloads not too irregular in temporal behavior.

Place, publisher, year, edition, pages
Association for Computing Machinery, Inc, 2021
Keywords
generative adversarial networks, server workload, synthetic data, time series synthesis, workload generation
National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-182918 (URN)10.1145/3427921.3450257 (DOI)000744413800005 ()2-s2.0-85104516480 (Scopus ID)9781450381949 (ISBN)
Conference
2021 ACM/SPEC International Conference on Performance Engineering, ICPE 2021
Available from: 2021-05-27 Created: 2021-05-27 Last updated: 2023-09-05Bibliographically approved
Östberg, P.-O., Le Duc, T., Casari, P., García Leiva, R., Fernández Anta, A. & Domaschka, J. (2020). Application Optimisation: Workload Prediction and Autonomous Autoscaling of Distributed Cloud Applications. In: Theo Lynn; John G. Mooney; Jörg Domaschka; Keith A. Ellis (Ed.), Managing Distributed Cloud Applications and Infrastructure: A Self-Optimising Approach (pp. 51-68). Palgrave Macmillan
Open this publication in new window or tab >>Application Optimisation: Workload Prediction and Autonomous Autoscaling of Distributed Cloud Applications
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2020 (English)In: Managing Distributed Cloud Applications and Infrastructure: A Self-Optimising Approach / [ed] Theo Lynn; John G. Mooney; Jörg Domaschka; Keith A. Ellis, Palgrave Macmillan, 2020, , p. 18p. 51-68Chapter in book (Refereed)
Abstract [en]

Optimisation of (the configuration and deployment of) distributed cloud applications is a complex problem that requires understanding factors such as infrastructure and application topologies, workload arrival and propagation patterns, and the predictability and variations of user behaviour. This chapter outlines the RECAP approach to application optimisation and presents its framework for joint modelling of applications, workloads, and the propagation of workloads in applications and networks. The interaction of the models and algorithms developed is described and presented along with the tools that build on them. Contributions in modelling, characterisation, and autoscaling of applications, as well as prediction and generation of workloads, are presented and discussed in the context of optimisation of distributed cloud applications operating in complex heterogeneous resource environments.

Place, publisher, year, edition, pages
Palgrave Macmillan, 2020. p. 18
Series
Palgrave Studies in Digital Business & Enabling Technologies, ISSN 2662-1282, E-ISSN 2662-1290
Keywords
Application optimisation, Autoscaling, Distributed cloud, Resource provisioning, Workload modelling, Workload prediction, Workload propagation modelling
National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-190628 (URN)10.1007/978-3-030-39863-7_3 (DOI)2-s2.0-85110048584 (Scopus ID)978-3-030-39862-0 (ISBN)978-3-030-39863-7 (ISBN)
Available from: 2021-12-22 Created: 2021-12-22 Last updated: 2023-03-23Bibliographically approved
Krzywda, J., Meyer, V., Xavier, M. G., Ali-Eldin, A., Östberg, P.-O., De Rose, C. A. F. & Elmroth, E. (2020). Modeling and Simulation of QoS-Aware Power Budgeting in Cloud Data Centers. In: 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP): . Paper presented at 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), (Västerås, Sweden), Virtual Meeting, March 11-13, 2020 (pp. 88-93). IEEE conference proceedings
Open this publication in new window or tab >>Modeling and Simulation of QoS-Aware Power Budgeting in Cloud Data Centers
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2020 (English)In: 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), IEEE conference proceedings, 2020, p. 88-93Conference paper, Published paper (Refereed)
Abstract [en]

Power budgeting is a commonly employed solution to reduce the negative consequences of high power consumption of large scale data centers. While various power budgeting techniques and algorithms have been proposed at different levels of data center infrastructures to optimize the power allocation toservers and hosted applications, testing them has been challengingwith no available simulation platform that enables such testingfor different scenarios and configurations. To facilitate evaluationand comparison of such techniques and algorithms, we introducea simulation model for Quality-of-Service aware power budgetingand its implementation in CloudSim. We validate the proposedsimulation model against a deployment on a real testbed, showcase simulator capabilities, and evaluate its scalability.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2020
Keywords
cloud computing, power budgeting, quality of service, simulation
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-169816 (URN)10.1109/PDP50117.2020.00020 (DOI)000582555800013 ()2-s2.0-85085515174 (Scopus ID)
Conference
28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), (Västerås, Sweden), Virtual Meeting, March 11-13, 2020
Available from: 2020-04-21 Created: 2020-04-21 Last updated: 2023-03-23Bibliographically approved
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