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Projects Topics

The following project topics can be adapted for Diploma Theses, Master Theses, Bachelor Theses, Research Projects (Studienarbeiten), and Student Reports (Fachstudien). Contact Prof. Dr. Marco Aiello in case you have further questions or would like to submit your own proposals.

If you are interested in any of the following project topics, please send your inquiry to the following email: scsp@iaas.uni-stuttgart.de

Quantum Computing for Smart Energy Optimizations

The advent of practical Quantum Computing promises to speed up many computation processes, especially in the field of optimization. This means that some large scale simulations and optimizations that were unfeasible with traditional von Neumann architectures become practically available. The proposed research topic focuses on optimisations in the fields of energy distribution under modern models of Smart Grids [2]. The project is open ended. Possible goals include: feasibility of using quantum computing for energy optimization [1], encoding MILP energy optimisations as quantum problems, energy use cases implemented on quantum computers. The interested student can also propose his/her own topic within the sub-field of Quantum Computing for Smart Energy Optimizations.

Keywords: Quantum Computing, Smart Energy Systems, Energy Distribution, MILP

Literature:

[1] Ajagekar, Akshay, and Fengqi You. “Quantum Computing for Energy Systems Optimization: Challenges and Opportunities.” Energy 179 (2019): 76–89. Crossref. Web.

[2] M. Aiello and G.A. Pagani (2016) How energy distribution will change: an ICT perspective, In Smart Grids from a Global Perspective, J. de Wilde, A. Beaulieu, and J. Scherpen (eds), Springer.

Supervisors: Prof. Dr. Marco Aiello / Daniel Vietz, M. Sc.


 

The Integration of Electric Vehicles in the Smart Grid

Electric Vehicles (eV) have a battery size that can vary from few KWh to almost 100 KWh. These are frequently connected to the power grid to charge. Several researchers have proposed the idea that these batteries can also be used to store grid electricity and provided it back to grid during peak demand periods. The research proposed here is to assess technical solutions to make the eV an integral part of the power grid, to study individual strategies based on dynamic pricing for incentivising eV integration, and to assess the economic viability of such approaches.

Literature

P. P. Malya, L. Fiorini, M. Rouhani, and M. Aiello (2021) Electric Vehicles as Distribution Grid Batteries: A Reality Check, Energy Informatics, Springer, 4(Suppl 2):2.

A. P. Lopes, F. J. Soares and P. M. R. Almeida, "Integration of Electric Vehicles in the Electric Power System," in Proceedings of the IEEE, vol. 99, no. 1, pp. 168-183, Jan. 2011.

Francis Mwasilu, Jackson John Justo, Eun-Kyung Kim, Ton Duc Do, Jin-Woo Jung, “Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration,” in Renewable and Sustainable Energy Reviews, vol. 34, no. 6, pp 501-516, Jun. 2014.

Supervisor: Prof. Dr. Marco Aiello


 

IoT Plant Monitoring

IoT technology can be used to monitor and automate the taking care of a plant. In this project, you will be researching the state of the art in the field of IoT for garden management. You will design and implement a cloud-based infrastructure to monitor two pots and manage them. Finally, you are going to compare the results for four pots: two with your designed IoT based solution and two with manual management.

Supervisor: Prof. Dr. Marco Aiello


 

Bluetooth Broadcasting

In 2009 we ran an experiment to see if Bluetooth could be used as a general, proximity-based broadcasting technology. The results were published in [1]. In this project, you are asked to repeat the experiments of that paper and compare the results. You will also have to study the state of the art and how Bluetooth and its use has changed in the last decade with respect to broadcasting.

Literature

[1] M. Aiello, R. de Jong, and J. de Nes (2009) Bluetooth Broadcasting: How far can we go? An experimental study. Int. Conference on Pervasive Computing and Applications (ICPCA'09) In Pervasive Computing (JCPC), 2009 Joint Conferences on IEEE, pages 471–476. 

Supervisor: Prof. Dr. Marco Aiello


 

On the energy accountability of running software

Software running on data centres and personal computers is responsible for a large amount of energy consumption and consequent emissions worldwide. At the same time, it is very difficult to perform an accurate measure of energy consumption for individual software programs and their individual components, this is also due to the current software engineering practices of layering and virtualisation. For this research project you are asked to look into energy models for software, energy accountability for software, comparing the running of the same algorithm on different platforms for measuring the energy consumption, etc.

Literature

[1] S. Miller and C. Lerche (2022) Sustainability with Rust. AWS Open Source Blog. Online: https://aws.amazon.com/de/blogs/opensource/sustainability-with-rust/. 

[2] Green Software Lab (2018) Energy Efficiency in Programming Languages (source code of 10 benchmarks implemented in 28 programming languages). GitHub. Online: https://github.com/greensoftwarelab/Energy-Languages. 

Supervisor: Prof. Dr. Marco Aiello


 

Cellular Automata for Modelling Resource Allocation

Cellular automata are simple models that can represent complex even chaotic systems. The goal of this project is to provide simple models for resource allocation that help identifying efficient allocation rules. The project entails both a modelling and simulation component to it.

Supervisor: Prof. Dr. Marco Aiello


 

A Complex Network Analysis of Models to be Checked

Model Checking is the field concerned with the automatic process of checking whether a given model (or family of models) meets certain specifications. Model checkers work in various ways usually exploring large models explicitly represented as states or symbolically represented. Your task is to evaluate the impact of using complex network analysis techniques to improve and extend model checking approaches.

Supervisor: Prof. Dr. Marco Aiello


 

AI Planning for Poker Player

The game of poker is interesting in that it combines elements of stochasticity, reasoning, risk awareness, opponent modeling, and decision making with hidden information. The goal of this project is to study components that are necessary to build a successful AI poker player. Most notably:

1) HTN Planning techniques that include risk-awareness

2) Accurate opponent modeling based on game hand history

3) Dynamic evaluation of the current state of the game based on hand history, stack values, and position

4) Adaptation of strategies to type of game played (number of tables, speed, limits, etc.)

The case study will be online No-Limit Hold 'em Multi-Table Tournaments.

Keywords: AI Poker Player, AI Planning, Risk Awareness

Literature:

[1] Jonathan Rubin, Ian Watson (2011) Computer poker: A review, Artificial Intelligence, 172(5-6): 958-987

[2] Darse Billings, Aaron Davidson, Jonathan Schaeffer, Duane Szafron (2002) The challenge of poker, Artificial Intelligence, 134(1-2):201-240

[3] Dan Harrington, Bill Robertie (2014) Harrington on Modern Tournament Poker: How to Play No-Limit Hold 'em Multi-Table Tournaments, Perfect Paperback 

Supervisors: Prof. Dr. Marco Aiello / Dr. Ilche Georgievski / Ebaa Alnazer, M.Sc.

Demand-Side Management via AI Planning

Commercial buildings are characterised by high operational costs and high CO2 emission levels due to the high demand for energy [1]. The management of energy demand, also known as demand-side management, allows buildings to make informed decisions about their energy consumption, but it also helps energy providers to shape load demand profiles [2]. However, current building management systems are capable of controlling heating, ventilation and air conditioning, lighting and other aspects using specific strategies limited by basic scheduling functions [3]. On the other hand, with the proliferation of the Internet of Things (IoT) deployed in building, building management systems can be transformed into systems employing intelligent demand-side management. In other words, buildings equipped with various sensors and actuators have the potential to become intelligent and provide occupants with a more comfortable experience, reduced operational costs, and improved energy efficiency.

This calls for developing approaches that can plan the energy demand of buildings intelligently. In this context, Artificial Intelligence (AI) planning offers powerful techniques for automated and dynamic selection and organisation of actions that, when executed, achieve a given objective [4]. AI planning could be helpful for buildings to intelligently plan their demand by computing effective plans or schedules of device actions.

The aim of this project is to propose and develop an approach for demand-side management via AI planning. This involves defining the problem of demand-side management as a domain-independent planning problem, modelling the planning problem using a standard syntax, such as PDDL [5], solving the planning problem using existing AI planners, and evaluating the efficiency and effectiveness of the proposed approach.

Keywords: AI planning, demand-side management, Internet of Things, office buildings

Literature

[1] D. D'Agostino, B. Cuniberti, and P. Bertoldi, Energy consumption and efficiency technology measures in European non-residential buildings," Energy and Buildings, vol. 153, pp. 2-86,2017.

[2] P. Palensky and D. Dietrich, Demand side management: Demand response, intelligent energy systems, and smart loads," IEEE Transactions on Industrial Informatics, vol. 7, no. 3, pp. 381-388, 2011.

[3] I. Georgievski, T. A. Nguyen, F. Nizamic, B. Setz, A. Lazovik, and M. Aiello, Planning meets activity recognition: Service coordination for intelligent buildings," Pervasive and Mobile Computing, vol. 38, no. 1, pp. 110-139, 2017.

[4] M. Ghallab, D. S. Nau, and P. Traverso, Automated planning: Theory & practice. Morgan Kaufmann Publishers Inc., 2004.

[5] M. Fox and D. Long, PDDL2.1: An extension to PDDL for expressing temporal planning domains," Journal of Artificial Intelligence Research, vol. 20, no. 1, pp. 61-124, 2003.

Supervisor: Dr. Ilche Georgievski


 

HTN Planning for Automated Deployment and Dynamic Reconfiguration of Cloud Applications

Modern software applications are increasingly deployed and distributed on infrastructures in the Cloud. Before the deployment process happens, these applications are being manually - or with some predefined scripts - composed from various smaller interdependent components. With the increase in demand for, and complexity of applications, the composition process becomes an arduous task often associated with errors and a suboptimal use of computer resources. Dynamic reconfiguration, which is the process of reconfiguring an application while it is running, only aggravates the problem even further [1]. Such processes can be alleviated by automatically and dynamically composing and reconfiguring Cloud applications , especially when clear semantics exists that describe the deployment and reconfiguration problems, such as the Aeolus formal model [2].

Artificial Intelligence (AI) planning provides techniques for searching and selecting actions that, when executed, achieve a given goal [3]. In the context of the Cloud, AI planning can provides powerful methods for searching in large and complex Cloud infrastructures to find "good" compositions and reconfigurations of Cloud applications. Additionally, AI planning can be used to handle the Cloud uncertainty (e.g., failures of hardware resources), find deployments optimal with respect to the use of computer resources, etc. Among AI planning techniques, Hierarchical Task Network (HTN) planning appears suitable for solving deployment problems [4].

The aim of this project is to study and solve both deployment and reconfiguration problems using HTN planning. The project should build upon [4]. The project tasks include:

  • Refine the definition of the Aelous-based deployment problem as an HTN planning problem (to improve performance);
  • Define the Aelous-based reconfiguration problem as an HTN planning problem;
  • Model such HTN planning problems using HPDL and/or SHOP2;
  • Evaluate the approach using one or more existing AI planners;
  • Compare the performance of existing AI planners among themselves and with Metis [5].

Keywords: Automated deployment, dynamic reconfiguration, cloud applications, HTN planning

Literature

[1] N. Arshad, D. Heimbigner, and A. Wolf, "Deployment and dynamic reconfiguration planning for distributed software systems," in International Conference on Tools with Artificial Intelligence, ser. ICTAI'03, 2003, pp. 39-46.

[2] R. Di Cosmo, J. Mauro, S. Zacchiroli, and G. Zavattaro, "Aeolus: A component model for the cloud," Information and Computation, vol. 239, pp. 100-121, 2014.

[3] M. Ghallab, D. S. Nau, and P. Traverso, Automated planning: Theory & practice. Morgan Kaufmann Publishers Inc., 2004.

[4] I. Georgievski, F. Nizamic, A. Lazovik, and M. Aiello, "Cloud ready applications composed via htn planning," in IEEE International Conference on Service Oriented Computing and Applications, ser. SOCA'17, 2017, pp. 23-33.

[5] T. A. Lascu, J. Mauro, and G. Zavattaro, "A planning tool supporting the deployment of cloud applications," in International Conference on Tools with Artificial Intelligence, ser. ICTAI'13, 2013, pp. 213-220.

Supervisor: Dr. Ilche Georgievski


 

AI Planning Under Uncertainty for Sustainable Buildings

With the increment of CO2 emission, the world energy consumption, and the diversity in energy sources (finite and renewable), the goals of building management systems are widened to include cutting the energy consumption; hence preserving finite resources, using non-carbon sources when possible to lower the CO2 footprint, and lowering costs for consumers and businesses while preserving users comfort [2]. This is especially true for buildings that are connected to a smart grid, which makes it possible to include renewable sources and provides dynamic pricing and energy offers coming from competing providers [4]. The advent of the Internet of Things (IoT) offers significant opportunities to improve the limited control capabilities offered by current building management systems [5]. In other words, equipping commercial buildings with different sensors and actuators allows management systems to make informed and "smart" decisions to control the environment and adjust it towards satisfying predefined objectives, such as energy saving. This "smartness" implies automating the decision-making process. Artificial Intelligence (AI) planning fits perfectly for this purpose since it offers automation techniques to intelligently search and compose a sequence of actions, i.e., plans when executed, can achieve a predefined set of goals [6].

Planning in such domains is challenging due to several types of uncertainty, e.g., weather forecasting, demand forecasting, and market prices forecasting [3, 2]. Consider the following example. A smart building is connected to a smart grid and has the goal of maintaining economical and environmental sustainability. The building can get its electricity from two different sources; either from stored electricity generated locally by renewable resources or from electric utilities with varying prices and energy offers. The choice of whether to consume the locally generated electricity or the purchasing it from outside sources should be made under uncertainty about the future market prices and the future weather forecast. In many approaches, AI planning problems are modelled with uncertainties about the action outcomes in mind; hence the resulting plans in such approaches are of a probabilistic nature [7, 1].

The aim of this project is to develop an AI planning-based approach in such domains. This involves defining the entities that are part of these domains, defining the types of uncertainty that can arise in these domains, modelling the AI planning problem by using a standard syntax that can model some sort of uncertainty, e.g., PPDDL [8], solving the planning problem using existing AI planners that can deal with uncertainties, and evaluating the efficiency and effectiveness of the proposed approach.

Keywords: sustainable buildings, Internet of Things, AI planning

Literature

[1] Blythe, J.: An overview of planning under uncertainty. Artificial Intelligence Today, pp. 85-110 (1999).

[2] Fiorini, L., Aiello, M.: Energy management for user’s thermal and power needs: A survey. Energy Reports 5, 1048-1076 (2019).

[3] Fiorini, L., Aiello, M.: Predictive multi-objective scheduling with dynamic prices and marginal CO2-emission intensities. In: Proceedings of the Eleventh ACM International Conference on Future Energy Systems. pp. 196-207 (2020).

[4] Georgievski, I.: Coordinating services embedded everywhere via hierarchical planning. Ph.D. thesis, University of Groningen (2015).

[5] Georgievski, I., Nguyen, T.A., Nizamic, F., Setz, B., Lazovik, A., Aiello, M.: Planning meets activity recognition: Service coordination for intelligent buildings. Pervasive and Mobile Computing 38, 110-139 (2017).

[6] Ghallab, M., Nau, D., Traverso, P.: Automated Planning: theory and practice. Elsevier (2004).

[7] Koenig, S., Simmons, R.G.: Risk-sensitive planning with probabilistic decision graphs. In: Principles of Knowledge Representation and Reasoning. pp. 363-373. Elsevier (1994).

[8] Younes, H.L., Littman, M.L.: PPDDL1. 0: An extension to PDDL for expressing planning domains with probabilistic effects. Techn. Rep. CMU-CS-04-162 2, 99 (2004).

Supervisor: M.Sc. Ebaa Alnazer

 

Cluster Scheduling in Data Centers

Cluster schedulers are a key component of managing the workload and the computational resources of data centers. Schedulers make scheduling decisions by gathering the information about the load and the queues on compute nodes as well as the available resources by communicating with the resource managers. Over the past years, a number of schedulers such as Borg [1], Mesos [2], Apache Yarn [3], and Slurm [4] have been developed. These schedulers are different in many aspects and regarding various key features like cluster type, language support, job migration, and resource allocation policies. The possible goals of this project include scientific evaluation of various resource allocation policies used in current schedulers and exploring the compatibility of different schedulers for GPU enhanced clusters. Interested students can also propose their topics within the scope of the project.

Keywords: Cluster scheduling, Data centers

Literature

[1] Verma, A., Pedrosa, L., Korupolu, M.: Large-scale cluster management at Google with Borg. In: Proceedings of the Tenth European Conference on Computer Systems. (2015).

[2] Hindman. B., Konwinski, A., et al.: Mesos: A platform for fine-grained resource sharing in the data center. In: NSDI. (2011).

[3] Vavilapalli. V., Murthy, A., et al.: Apache hadoop yarn: Yet another resource negotiator. In: Proceedings of the 4th annual Symposium on Cloud Computing. (2013).

[4] Yoo. AB., Jette, MA., et al.: Slurm: Simple linux utility for resource management. Workshop on job scheduling strategies for parallel processing. Springer, Berlin, Heidelberg. (2003)

Supervisor: Dr. Kawsar Haghshenas

Kontakt

Dieses Bild zeigt Marco Aiello

Marco Aiello

Prof. Dr.

Abteilungsleiter

Dieses Bild zeigt Elisabeth Ibach

Elisabeth Ibach

 

Sekretariat

Dieses Bild zeigt Ilche Georgievski

Ilche Georgievski

Dr.

Leitung Arbeitsbereich Smart Energy Systems

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