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Papadopoulos, AlessandroORCID iD iconorcid.org/0000-0002-1364-8127
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Publications (10 of 30) Show all publications
Ioli, D., Falsone, A., Papadopoulos, A. & Prandini, M. (2019). A compositional modeling framework for the optimal energy management of a district network. Journal of Process Control, 74, 160-176
Open this publication in new window or tab >>A compositional modeling framework for the optimal energy management of a district network
2019 (English)In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 74, p. 160-176Article in journal (Refereed) Published
Abstract [en]

This paper proposes a compositional modeling framework for the optimal energy management of a district network. The focus is on cooling of buildings, which can possibly share resources to the purpose of reducing maintenance costs and using devices at their maximal efficiency. Components of the network are described in terms of energy fluxes and combined via energy balance equations. Disturbances are accounted for as well, through their contribution in terms of energy. Different district configurations can be built, and the dimension and complexity of the resulting model will depend both on the number and type of components and on the adopted disturbance description. Control inputs are available to efficiently operate and coordinate the district components, thus enabling energy management strategies to minimize the electrical energy costs or track some consumption profile agreed with the main grid operator.

Place, publisher, year, edition, pages
Elsevier Ltd, 2019
Keywords
Building thermal regulation, Compositional systems, Energy management, Smart grid modeling, Electric power transmission networks, Compositional modeling, Cooling of buildings, District networks, Electrical energy costs, Energy balance equations, Energy management strategies, Smart grid, Thermal regulation, Smart power grids
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-43055 (URN)10.1016/j.jprocont.2017.10.005 (DOI)000465050900015 ()2-s2.0-85033227619 (Scopus ID)
Available from: 2019-04-10 Created: 2019-04-10 Last updated: 2019-05-09Bibliographically approved
Struhar, V., Ashjaei, S. M., Behnam, M., Craciunas, S. & Papadopoulos, A. (2019). DART: Dynamic Bandwidth Distribution Framework for Virtualized Software Defined Networks. In: IEEE 45th Annual Conference of the Industrial Electronics Society IECON'19: . Paper presented at IEEE 45th Annual Conference of the Industrial Electronics Society IECON'19, 14 Oct 2019, Lisbon, Portugal.
Open this publication in new window or tab >>DART: Dynamic Bandwidth Distribution Framework for Virtualized Software Defined Networks
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2019 (English)In: IEEE 45th Annual Conference of the Industrial Electronics Society IECON'19, 2019Conference paper, Published paper (Refereed)
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-45054 (URN)
Conference
IEEE 45th Annual Conference of the Industrial Electronics Society IECON'19, 14 Oct 2019, Lisbon, Portugal
Projects
Future factories in the CloudFORA - Fog Computing for Robotics and Industrial Automation
Available from: 2019-08-22 Created: 2019-08-22 Last updated: 2019-08-22Bibliographically approved
Miloradović, B., Curuklu, B., Ekström, M. & Papadopoulos, A. (2019). Extended colored traveling salesperson for modeling multi-agent mission planning problems. In: ICORES 2019 - Proceedings of the 8th International Conference on Operations Research and Enterprise Systems: . Paper presented at 8th International Conference on Operations Research and Enterprise Systems, ICORES 2019, 19 February 2019 through 21 February 2019 (pp. 237-244). SciTePress
Open this publication in new window or tab >>Extended colored traveling salesperson for modeling multi-agent mission planning problems
2019 (English)In: ICORES 2019 - Proceedings of the 8th International Conference on Operations Research and Enterprise Systems, SciTePress , 2019, p. 237-244Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, multi-agent systems have been widely used in different missions, ranging from underwater to airborne. A mission typically involves a large number of agents and tasks, making it very hard for the human operator to create a good plan. A search for an optimal plan may take too long, and it is hard to make a time estimate of when the planner will finish. A Genetic algorithm based planner is proposed in order to overcome this issue. The contribution of this paper is threefold. First, an Integer Linear Programming (ILP) formulation of a novel Extensive Colored Traveling Salesperson Problem (ECTSP) is given. Second, a new objective function suitable for multi-agent mission planning problems is proposed. Finally, a reparation algorithm to allow usage of common variation operators for ECTSP has been developed. 

Place, publisher, year, edition, pages
SciTePress, 2019
Keywords
Colored traveling salesperson (CTSP), Genetic algorithms, Multi-agent mission planning, Integer programming, Operations research, Software agents, Human operator, Integer Linear Programming, Mission planning, Mission planning problem, Objective functions, Traveling salesperson problem, Variation operator, Multi agent systems
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-43305 (URN)2-s2.0-85064712559 (Scopus ID)9789897583520 (ISBN)
Conference
8th International Conference on Operations Research and Enterprise Systems, ICORES 2019, 19 February 2019 through 21 February 2019
Available from: 2019-05-09 Created: 2019-05-09 Last updated: 2019-10-01Bibliographically approved
Salman, C. A., Struhar, V., Papadopoulos, A., Behnam, M. & Nolte, T. (2019). Fogification of industrial robotic systems: Research challenges. In: IoT-Fog 2019 - Proceedings of the 2019 Workshop on Fog Computing and the IoT: . Paper presented at 2019 Workshop on Fog Computing and the IoT, IoT-Fog 2019, 15 April 2019 (pp. 41-45). Association for Computing Machinery, Inc
Open this publication in new window or tab >>Fogification of industrial robotic systems: Research challenges
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2019 (English)In: IoT-Fog 2019 - Proceedings of the 2019 Workshop on Fog Computing and the IoT, Association for Computing Machinery, Inc , 2019, p. 41-45Conference paper, Published paper (Refereed)
Abstract [en]

To meet the demands of future automation systems, the architecture of traditional control systems such as the industrial robotic systems needs to evolve and new architectural paradigms need to be investigated. While cloud-based platforms provide services such as computational resources on demand, they do not address the requirements of real-time performance expected by control applications. Fog computing is a promising new architectural paradigm that complements the cloud-based platform by addressing its limitations. In this paper, we analyse the existing robot system architecture and propose a fog-based solution for industrial robotic systems that addresses the needs of future automation systems. We also propose the use of Time-Sensitive Networking (TSN) services for real-time communication and OPC-UA for information modelling within this architecture. Additionally, we discuss the main research challenges associated with the proposed architecture.

Place, publisher, year, edition, pages
Association for Computing Machinery, Inc, 2019
Keywords
Automation, Computer architecture, Fog, Industrial research, Internet of things, Robotics, Cloud based platforms, Computational resources, Control applications, Industrial robotic systems, Information modelling, Proposed architectures, Real time performance, Real-time communication, Fog computing
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-43888 (URN)10.1145/3313150.3313225 (DOI)000473542200009 ()2-s2.0-85066045184 (Scopus ID)9781450366984 (ISBN)
Conference
2019 Workshop on Fog Computing and the IoT, IoT-Fog 2019, 15 April 2019
Available from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-10-11Bibliographically approved
Wang, W., Mosse, D. & Papadopoulos, A. (2019). Packet priority assignment for wireless control systems of multiple physical systems. In: Proceedings - 2019 IEEE 22nd International Symposium on Real-Time Distributed Computing, ISORC 2019: . Paper presented at 22nd IEEE International Symposium on Real-Time Distributed Computing, ISORC 2019, 7 May 2019 through 9 May 2019 (pp. 143-150). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Packet priority assignment for wireless control systems of multiple physical systems
2019 (English)In: Proceedings - 2019 IEEE 22nd International Symposium on Real-Time Distributed Computing, ISORC 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 143-150Conference paper, Published paper (Refereed)
Abstract [en]

Wireless control systems (WCSs) have gained much attention lately, due to their easy deployment and flexibility compared to wired control systems. However, this comes at the cost of possibly increased network delay and packet losses, that can significantly impact the control system performance, and possibly its stability. Such problems become even more relevant if the network is shared among different control systems, and thus becomes a scarce resource, like in Industrial Internet of Things applications. In this paper, we describe how to assign packet priorities dynamically when there are many physical systems sharing a given network, aiming at minimizing the performance degradation of the WCS. Towards that, we present a network model including both delay and packet losses, both of which are very important for the control system performance. Our solution is evaluated over two different use cases to show the generality of the approach: the WCS for a set of inverted pendula, and the WCS for small modular reactors in a nuclear power plant. The results show that the proposed approach allows for a more stable performance even in presence of highly nonlinear systems, sensitive to time-varying delays, as well as in presence of high network interference.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
Distributed computer systems, Nuclear fuels, Nuclear power plants, Packet loss, Control system performance, Delay and packet loss, Network interferences, Performance degradation, Priority assignment, Small modular reactors, Stable performance, Time varying- delays, Control systems
National Category
Communication Systems Telecommunications Control Engineering Embedded Systems
Identifiers
urn:nbn:se:mdh:diva-45015 (URN)10.1109/ISORC.2019.00036 (DOI)000490865800025 ()2-s2.0-85070349885 (Scopus ID)9781728101507 (ISBN)
Conference
22nd IEEE International Symposium on Real-Time Distributed Computing, ISORC 2019, 7 May 2019 through 9 May 2019
Available from: 2019-08-15 Created: 2019-08-15 Last updated: 2019-10-31Bibliographically approved
Faragardi, H. R., Dehnavi, S., Kargahi, M., Papadopoulos, A. & Nolte, T. (2018). A Time-Predictable Fog-Integrated Cloud Framework: One Step Forward in the Deployment of a Smart Factory. In: CSI International Symposium on Real-Time and Embedded Systems and Technologies REST'18: . Paper presented at CSI International Symposium on Real-Time and Embedded Systems and Technologies REST'18, 09 May 2018, Tehran, Iran (pp. 54-62).
Open this publication in new window or tab >>A Time-Predictable Fog-Integrated Cloud Framework: One Step Forward in the Deployment of a Smart Factory
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2018 (English)In: CSI International Symposium on Real-Time and Embedded Systems and Technologies REST'18, 2018, p. 54-62Conference paper, Published paper (Refereed)
Abstract [en]

This paper highlights cloud computing as one of the principal building blocks of a smart factory, providing a huge data storage space and a highly scalable computational capacity. The cloud computing system used in a smart factory should be time-predictable to be able to satisfy hard real-time requirements of various applications existing in manufacturing systems. Interleaving an intermediate computing layer-called fog-between the factory and the cloud data center is a promising solution to deal with latency requirements of hard real-time applications. In this paper, a time-predictable cloud framework is proposed which is able to satisfy end-to-end latency requirements in a smart factory. To propose such an industrial cloud framework, we not only use existing real-time technologies such as Industrial Ethernet and the Real-time XEN hypervisor, but we also discuss unaddressed challenges. Among the unaddressed challenges, the partitioning of a given workload between the fog and the cloud is targeted. Addressing the partitioning problem not only provides a resource provisioning mechanism, but it also gives us a prominent design decision specifying how much computing resource is required to develop the fog platform, and how large should the minimum communication bandwidth be between the fog and the cloud data center.

National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-38638 (URN)10.1109/RTEST.2018.8397079 (DOI)000467076600008 ()2-s2.0-85050457708 (Scopus ID)9781538614754 (ISBN)
Conference
CSI International Symposium on Real-Time and Embedded Systems and Technologies REST'18, 09 May 2018, Tehran, Iran
Projects
PREMISE - Predictable Multicore Systems
Available from: 2018-02-12 Created: 2018-02-12 Last updated: 2019-05-24Bibliographically approved
Frasheri, M., Curuklu, B., Ekström, M. & Papadopoulos, A. (2018). Adaptive Autonomy in a Search and Rescue Scenario. In: International Conference on Self-Adaptive and Self-Organizing Systems, SASO, Volume 2018-September, 15 January 2019: . Paper presented at 12th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2018; Trento; Italy; 3 September 2018 through 7 September 2018 (pp. 150-155).
Open this publication in new window or tab >>Adaptive Autonomy in a Search and Rescue Scenario
2018 (English)In: International Conference on Self-Adaptive and Self-Organizing Systems, SASO, Volume 2018-September, 15 January 2019, 2018, p. 150-155Conference paper, Published paper (Refereed)
Abstract [en]

Adaptive autonomy plays a major role in the design of multi-robots and multi-agent systems, where the need of collaboration for achieving a common goal is of primary importance. In particular, adaptation becomes necessary to deal with dynamic environments, and scarce available resources. In this paper, a mathematical framework for modelling the agents' willingness to interact and collaborate, and a dynamic adaptation strategy for controlling the agents' behavior, which accounts for factors such as progress toward a goal and available resources for completing a task among others, are proposed. The performance of the proposed strategy is evaluated through a fire rescue scenario, where a team of simulated mobile robots need to extinguish all the detected fires and save the individuals at risk, while having limited resources. The simulations are implemented as a ROS-based multi agent system, and results show that the proposed adaptation strategy provides a more stable performance than a static collaboration policy. 

National Category
Engineering and Technology Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-40254 (URN)10.1109/SASO.2018.00026 (DOI)000459885200016 ()2-s2.0-85061910844 (Scopus ID)9781538651728 (ISBN)
Conference
12th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2018; Trento; Italy; 3 September 2018 through 7 September 2018
Available from: 2018-07-18 Created: 2018-07-18 Last updated: 2019-03-14Bibliographically approved
Papadopoulos, A., Bini, E., Baruah, S. & Burns, A. (2018). AdaptMC: A control-theoretic approach for achieving resilience in mixed-criticality systems. In: Leibniz International Proceedings in Informatics, LIPIcs: . Paper presented at 30th Euromicro Conference on Real-Time Systems, ECRTS 2018, 3 June 2018 through 6 June 2018. Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
Open this publication in new window or tab >>AdaptMC: A control-theoretic approach for achieving resilience in mixed-criticality systems
2018 (English)In: Leibniz International Proceedings in Informatics, LIPIcs, Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing , 2018Conference paper, Published paper (Refereed)
Abstract [en]

A system is said to be resilient if slight deviations from expected behavior during run-time does not lead to catastrophic degradation of performance: minor deviations should result in no more than minor performance degradation. In mixed-criticality systems, such degradation should additionally be criticality-cognizant. The applicability of control theory is explored for the design of resilient run-time scheduling algorithms for mixed-criticality systems. Recent results in control theory have shown how appropriately designed controllers can provide guaranteed service to hardreal- time servers; this prior work is extended to allow for such guarantees to be made concurrently to multiple criticality-cognizant servers. The applicability of this approach is explored via several experimental simulations in a dual-criticality setting. These experiments demonstrate that our control-based run-time schedulers can be synthesized in such a manner that bounded deviations from expected behavior result in the high-criticality server suffering no performance degradation and the lower-criticality one, bounded performance degradation.

Place, publisher, year, edition, pages
Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 2018
Keywords
Bounded overloads, Control theory, Mixed criticality, Run-time resilience, Criticality (nuclear fission), Interactive computer systems, Scheduling algorithms, Catastrophic degradation, Control-theoretic approach, Experimental simulations, Mixed criticalities, Mixed-criticality systems, Performance degradation, Runtimes, Real time systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-40237 (URN)10.4230/LIPIcs.ECRTS.2018.14 (DOI)2-s2.0-85049304309 (Scopus ID)9783959770750 (ISBN)
Conference
30th Euromicro Conference on Real-Time Systems, ECRTS 2018, 3 June 2018 through 6 June 2018
Available from: 2018-07-12 Created: 2018-07-12 Last updated: 2018-07-12Bibliographically approved
Ilyushkin, A., Ali-Eldin, A., Herbst, N., Bauer, A., Papadopoulos, A., Epema, D. & Iosup, A. (2018). An Experimental Performance Evaluation of Autoscalers for Complex Workflows. ACM TRANSACTIONS ON MODELING AND PERFORMANCE EVALUATION OF COMPUTING SYSTEMS, 3(2), Article ID UNSP 8.
Open this publication in new window or tab >>An Experimental Performance Evaluation of Autoscalers for Complex Workflows
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2018 (English)In: ACM TRANSACTIONS ON MODELING AND PERFORMANCE EVALUATION OF COMPUTING SYSTEMS, ISSN 2376-3639, Vol. 3, no 2, article id UNSP 8Article in journal (Refereed) Published
Abstract [en]

Elasticity is one of the main features of cloud computing allowing customers to scale their resources based on the workload. Many autoscalers have been proposed in the past decade to decide on behalf of cloud customers when and how to provision resources to a cloud application based on the workload utilizing cloud elasticity features. However, in prior work, when a new policy is proposed, it is seldom compared to the state-of-the-art, and is often compared only to static provisioning using a predefined quality of service target. This reduces the ability of cloud customers and of cloud operators to choose and deploy an autoscaling policy, as there is seldom enough analysis on the performance of the autoscalers in different operating conditions and with different applications. In our work, we conduct an experimental performance evaluation of autoscaling policies, using as application model workflows, a popular formalism for automating resource management for applications with well-defined yet complex structures. We present a detailed comparative study of general state-of-the-art autoscaling policies, along with two new workflow-specific policies. To understand the performance differences between the seven policies, we conduct various experiments and compare their performance in both pairwise and group comparisons. We report both individual and aggregated metrics. As many workflows have deadline requirements on the tasks, we study the effect of autoscaling on workflow deadlines. Additionally, we look into the effect of autoscaling on the accounted and hourly based charged costs, and we evaluate performance variability caused by the autoscaler selection for each group of workflow sizes. Our results highlight the trade-offs between the suggested policies, how they can impact meeting the deadlines, and how they perform in different operating conditions, thus enabling a better understanding of the current state-of-the-art.

Place, publisher, year, edition, pages
ASSOC COMPUTING MACHINERY, 2018
Keywords
Autoscaling, elasticity, scientific workflows, benchmarking, metrics
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-39204 (URN)10.1145/3164537 (DOI)000430350200004 ()
Available from: 2018-05-11 Created: 2018-05-11 Last updated: 2018-05-11Bibliographically approved
Papadopoulos, A. & Maggio, M. (2018). Challenges in High Performance Big Data Frameworks. In: 4th International Workshop on Autonomic High Performance Computing AHPC 2018: . Paper presented at 4th International Workshop on Autonomic High Performance Computing AHPC 2018, 16 Jul 2018, Orleans, France (pp. 153-156).
Open this publication in new window or tab >>Challenges in High Performance Big Data Frameworks
2018 (English)In: 4th International Workshop on Autonomic High Performance Computing AHPC 2018, 2018, p. 153-156Conference paper, Published paper (Refereed)
Abstract [en]

Nowadays, we live in a society with billions of devices that are interconnected and interact together to improve the quality of our lives. The management and processing of information and knowledge have by now become our main resources, and the fundamental factors of economic and social development, and it is achieved through Big Data Frameworks (BDFs). The amount of such data is becoming larger every day, and this calls for scalable and reliable BDFs, that can process such data also with real-time requirements. For example, the data collected by an autonomous car should be processed, combined, and interpreted as fast as possible in order to guarantee a safe interaction with the surrounding environment, and of the passengers. 

This paper analyses the main limitations of current BDFs while providing some key challenges for increasing their flexibility. In particular, we focus on performance aspects, envisioning adaptation as a viable way to automate and improve performance in Big Data Applications.

National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-40859 (URN)10.1109/HPCS.2018.00039 (DOI)000450677700023 ()2-s2.0-85057371047 (Scopus ID)978-1-5386-7879-4 (ISBN)
Conference
4th International Workshop on Autonomic High Performance Computing AHPC 2018, 16 Jul 2018, Orleans, France
Projects
Future factories in the Cloud
Available from: 2018-09-20 Created: 2018-09-20 Last updated: 2019-01-04Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1364-8127

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