Biography

Since October 2011 Professor Dr. Ramin Yahyapour is managing director of GWDG, a joint compute and IT competence center of the university and the Max Planck Society. At the same time, he became full professor for Practical Computer Science at the Georg-August-Universität Göttingen. Before, he was Professor at the Technische Universität Dortmund, director of the IT & Media Center, and CIO of the university.

Professor Yahyapour holds a doctoral degree in Electrical Engineering. His research focus is on efficient resource management and its application on service-oriented infrastructures, clouds, and data management.

Professor Yahyapour was and is active in several national and international research projects. He was e.g. scientific coordinator of the FP7 integrated project SLA@SOI or executive member of the CoreGRID Network of Excellence. Professor Yahyapour serves regularly as reviewer for funding agencies and consultant for IT organizations. He is organizer and program committee member of several conferences and workshops as well as reviewer for multiple journals.

Research Interests

• resource management and scheduling
• service-oriented infrastructures and service computing
• cloud computing
• data management

Recent Publications

2024

• A novel combining method of dynamic and static web crawler with parallel computing (Qingyang Liu, Ramin Yahyapour, Hongjiu Liu, Yanrong Hu), 2024-01-01 DOI BibTeX
• An attention based approach for automated account linkage in federated identity management (Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour), 2024-01-01 DOI BibTeX
• Nonlinear parsimonious modeling based on Copula–LoGo (Qingyang Liu, Ramin Yahyapour), 2024-01-01 DOI BibTeX
• Optimization of containerized application deployment in virtualized environments: a novel mathematical framework for resource-efficient and energy-aware server infrastructure (Reza Rabieyan, Ramin Yahyapour, Patrick Jahnke), 2024-01-01 DOI BibTeX
• Optimizing Resource Consumption and Reducing Power Usage in Data Centers, A Novel Mathematical VM Replacement Model and Efficient Algorithm (Reza Rabieyan, Ramin Yahyapour, Patrick Jahnke), 2024-01-01 DOI BibTeX

2023

• A Survey on Identity and Access Management for Cross-Domain Dynamic Users: Issues, Solutions, and Challenges (Aytaj Badirova, Shirin Dabbaghi, Faraz Fatemi Moghaddam, Philipp Wieder, Ramin Yahyapour), 2023-01-01 BibTeX
• Forecast stock price based on GRA-LoGo model of information filtering networks (Qingyang Liu, Ramin Yahyapour), 2023-01-01 BibTeX

2022

• Online Orchestration of Collaborative Caching for Multi-Bitrate Videos in Edge Computing (Song Yang, Lei Jiao, Ramin Yahyapour, Jiannong Cao), 2022-01-01 BibTeX
• Using a Workflow Management Platform in Textual Data Management (Triet Ho Anh Doan, Sven Bingert, Ramin Yahyapour), 2022-01-01 DOI BibTeX

2021

• SmartSSO - A Deep Learning Platform for Automated Account Linkage in Federated Identity Management (Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Christof Pohl, Ramin Yahyapour), 2021-01-01 BibTeX

@article{2_143982,

	abstract = {"Abstract This study addresses the issue of power consumption in virtualized cloud data centers by proposing a virtual machine (VM) replacement model and a corresponding algorithm. The model incorporates multi-objective functions, aiming to optimize VM selection based on weights and minimize resource utilization disparities across hosts. Constraints are incorporated to ensure that CPU utilization remains close to the average CPU usage while mitigating overutilization in memory and network bandwidth usage. The proposed algorithm offers a fast and efficient solution with minimal VM replacements. The experimental simulation results demonstrate significant reductions in power consumption compared with a benchmark model. The proposed model and algorithm have been implemented and operated within a real-world cloud infrastructure, emphasizing their practicality."},
	author = {Reza Rabieyan and Ramin Yahyapour and Patrick Jahnke},
	doi = {10.1007/s10723-024-09772-4},
	grolink = {https://resolver.sub.uni-goettingen.de/purl?gro-2/143982},
	month = {01},
	title = {Optimizing Resource Consumption and Reducing Power Usage in Data Centers, A Novel Mathematical VM Replacement Model and Efficient Algorithm},
	type = {article},
	year = {2024},
}

@article{2_144725,

	abstract = {"Abstract This study addresses the critical need for effective resource management through software container migration in cloud data centers. It emphasizes its role in avoiding resource shortages and reducing energy consumption in cloud environments. This study introduces a novel multi-objective integer linear programming (ILP) approach for software container replacements, complemented by a specialized algorithm designed to migrate software containers between over- and underutilized hosts to enhance efficiency compared to traditional optimization methods. The simulation results demonstrate the algorithm's effectiveness, validating its potential for optimizing energy usage and resource allocation in cloud environments. Statistical analyses confirm the proposed model's and algorithm's superiority over benchmark approaches, highlighting their potential for enhancing resource management in cloud computing systems."},
	author = {Reza Rabieyan and Ramin Yahyapour and Patrick Jahnke},
	doi = {10.1007/s11227-024-06304-5},
	grolink = {https://resolver.sub.uni-goettingen.de/purl?gro-2/144725},
	month = {01},
	title = {Optimization of containerized application deployment in virtualized environments: a novel mathematical framework for resource-efficient and energy-aware server infrastructure},
	type = {article},
	year = {2024},
}

@article{2_144794,

	author = {Qingyang Liu and Ramin Yahyapour},
	doi = {10.1016/j.eswa.2024.124774},
	grolink = {https://resolver.sub.uni-goettingen.de/purl?gro-2/144794},
	month = {01},
	title = {Nonlinear parsimonious modeling based on Copula–LoGo},
	type = {article},
	year = {2024},
}

@article{2_139926,

	author = {Shirin Dabbaghi Varnosfaderani and Piotr Kasprzak and Aytaj Badirova and Ralph Krimmel and Christof Pohl and Ramin Yahyapour},
	doi = {10.1016/j.ins.2023.119920},
	grolink = {https://resolver.sub.uni-goettingen.de/purl?gro-2/139926},
	month = {01},
	title = {An attention based approach for automated account linkage in federated identity management},
	type = {article},
	year = {2024},
}

@article{2_142141,

	author = {Qingyang Liu and Ramin Yahyapour and Hongjiu Liu and Yanrong Hu},
	doi = {10.1007/s11042-023-17925-y},
	grolink = {https://resolver.sub.uni-goettingen.de/purl?gro-2/142141},
	month = {01},
	title = {A novel combining method of dynamic and static web crawler with parallel computing},
	type = {article},
	year = {2024},
}

@article{2_142092,

	abstract = {"The considerable fluctuation of the stock market caused by COVID-19 tends to have a tremendous and long-lasting adverse impact on the economy. In this work, we propose a novel methodology to investigate this impact on the Chinese medical stock market. We examine changes in the stock network structure using the Triangulated Maximally Filtered Graph (TMFG), which is computationally faster and more adaptable to enormous datasets. Additionally, we develop the LoGo model, which combines a local-global approach in its construction, to predict the stock prices of the Chinese medical stock market. In addition to traditional predictors, we incorporate daily new infected numbers as an additional predictor to reflect the impact of COVID-19. We select data from the 2019-2020 period and divide it into two datasets: one for the period during COVID-19 and another for the period before COVID-19. Firstly, we compute the grey correlation coefficients between stocks instead of standard correlation coefficients. We use these coefficients to build the TMFG, enabling us to identify which stocks played the leading roles. Subsequently, we choose six stocks to build the price prediction models. Compared with the LSTM and SVR models, the LoGo models demonstrates higher accuracy, achieving an average accuracy of 71.67 percent. Furthermore, the execution time of the Logo models is 200 times faster than that of the SVR models and 50 times faster than that of the LSTM models."},
	author = {Qingyang Liu and Ramin Yahyapour},
	grolink = {https://resolver.sub.uni-goettingen.de/purl?gro-2/142092},
	month = {01},
	title = {Forecast stock price based on GRA-LoGo model of information filtering networks},
	type = {article},
	year = {2023},
}

@article{2_131999,

	author = {Aytaj Badirova and Shirin Dabbaghi and Faraz Fatemi Moghaddam and Philipp Wieder and Ramin Yahyapour},
	grolink = {https://resolver.sub.uni-goettingen.de/purl?gro-2/131999},
	month = {01},
	title = {A Survey on Identity and Access Management for Cross-Domain Dynamic Users: Issues, Solutions, and Challenges},
	type = {article},
	year = {2023},
}

@article{2_136421,

	abstract = {"Abstract The paper gives a brief introduction about the workflow management platform, Flowable, and how it is used for textual-data management. It is relatively new with its first release on 13 October, 2016. Despite the short time on the market, it seems to be quickly well-noticed with 4.6 thousand stars on GitHub at the moment. The focus of our project is to build a platform for text analysis on a large scale by including many different text resources. Currently, we have successfully connected to four different text resources and obtained more than one million works. Some resources are dynamic, which means that they might add more data or modify their current data. Therefore, it is necessary to keep data, both the metadata and the raw data, from our side up to date with the resources. In addition, to comply with FAIR principles, each work is assigned a persistent identifier (PID) and indexed for searching purposes. In the last step, we perform some standard analyses on the data to enhance our search engine and to generate a knowledge graph. End-users can utilize our platform to search on our data or get access to the knowledge graph. Furthermore, they can submit their code for their analyses to the system. The code will be executed on a High-Performance Cluster (HPC) and users can receive the results later on. In this case, Flowable can take advantage of PIDs for digital objects identification and management to facilitate the communication with the HPC system. As one may already notice, the whole process can be expressed as a workflow. A workflow, including error handling and notification, has been created and deployed. Workflow execution can be triggered manually or after predefined time intervals. According to our evaluation, the Flowable platform proves to be powerful and flexible. Further usage of the platform is already planned or implemented for many of our projects."},
	author = {Triet Ho Anh Doan and Sven Bingert and Ramin Yahyapour},
	doi = {10.1162/dint_a_00139},
	grolink = {https://resolver.sub.uni-goettingen.de/purl?gro-2/136421},
	month = {01},
	title = {Using a Workflow Management Platform in Textual Data Management},
	type = {article},
	year = {2022},
}

@article{2_114612,

	abstract = {"In the traditional video streaming service provisioning paradigm, users typically request video contents through nearby Content Delivery Network (CDN) server(s). However, because of the uncertain wide area networks delays, the (remote) users usually suffer from long video streaming delay, which affects the quality of experience. Multi-Access Edge Computing (MEC) offers caching infrastructures in closer proximity to end users than conventional Content Delivery Networks (CDNs). Yet, for video caching, MEC's potential has not been fully unleashed as it overlooks the opportunities of collaborative caching and multi-bitrate video transcoding. In this paper, we model and formulate an Integer Linear Program (ILP) to capture the long-term cost minimization problem for caching videos at MEC, allowing joint exploitation of MEC with CDN and real-time video transcoding to satisfy arbitrary user demands. While this problem is intractable and couples the caching decisions for adjacent time slots, we design a polynomial-time online orchestration framework which first relaxes and carefully decomposes the problem into a series of subproblems solvable in each individual time slot and then converts the fractional solutions into integers without violating constraints. We have formally proved a parameterized-constant competitive ratio as the performance guarantee for our approach, and also conducted extensive evaluations to confirm its superior practical performance. Simulation results demonstrate that our proposed algorithm outperforms the state-of-the-art algorithms, with 13.6% improvement on average in terms of total cost."},
	author = {Song Yang and Lei Jiao and Ramin Yahyapour and Jiannong Cao},
	grolink = {https://resolver.sub.uni-goettingen.de/purl?gro-2/114612},
	month = {01},
	title = {Online Orchestration of Collaborative Caching for Multi-Bitrate Videos in Edge Computing},
	type = {article},
	year = {2022},
}

@inproceedings{2_135507,

	author = {Shirin Dabbaghi Varnosfaderani and Piotr Kasprzak and Christof Pohl and Ramin Yahyapour},
	grolink = {https://resolver.sub.uni-goettingen.de/purl?gro-2/135507},
	month = {01},
	title = {SmartSSO - A Deep Learning Platform for Automated Account Linkage in Federated Identity Management},
	type = {inproceedings},
	year = {2021},
}