Problem-Oriented Cloud Computing Environment International Laboratory

South Ural State University
Office 492, University Building №3A
87, Prospect Lenina
Tel.: + 7 351 267 90 94
E-mail: gleb.radchenko[at]susu[dot]ru

Andrei Tchernykh Ph.D., Professor CICESE Research Center, Mexico


Head of Laboratory

Andrei Tchernykh

Ph.D., Professor
CICESE Research Center, Mexico


Gleb Radchenko Ph.D., Associate Professor South Ural State University

Deputy Head of Laboratory

Gleb Radchenko

Ph.D., Associate Professor
South Ural State University


Delivering computing services over the cloud has become an integral part of modern businesses and everyday activities. With the enormous capabilities of cloud computing services come various challenges we face, ranging from security issues and control to more efficient performance and user adaptation. Since its opening in 2016, the International Laboratory for Problem-Oriented Cloud Computing Environment has been conducting groundbreaking research into distributed computing and cloud computing environments to respond to the challenges of “Industry 4.0” and Big Data.


The Laboratory uses first-class facilities provided by the University, including the Tornado-SUSU and SKIF-Aurora supercomputers. These powerful facilities are ranked among the world’s best supercomputers.


The University’s supercomputers provide ample opportunity to research cloud computing environment, digital twins, information security, and smart city technologies.

1. Research on resources scheduling in cloud and fog computing

Modern CAE tasks are characterised by high requirements for the provided computing resources,  as well as a complex computational structure of the task, which can be described as a workflow. Also, for engineering analysis, multivariate calculations are common, when the computing task is launched multiple times (hundreds and thousands of times) with different variations of the input parameters. Applications of this kind constitute a large percentage of the load of modern supercomputers and distributed computing systems.

The research team develops a set of methods and algorithms for scheduling of workflow applications when performing multivariate calculations in the field of CAE in cloud computing systems. The created methods and algorithms should take into account the specifics of the classes of CAE problems, support multivariate calculations and provide the possibility of dynamically adjusting the design of the engineering simulation task when deviating the real parameters of the task execution (execution time, output volume, scalability) from the predicted ones.

2. Research on cloud-based digital twins support

In a “Smart manufacturing” enterprise, industrial processes go hand in hand with their virtual models –“digital twins”, representing the processes, systems, and equipment. These models provide a simulation of real-world processes and equipment allowing to increase the efficiency and reliability of manufacturing processes by (1) prediction and prevention of emergency situations; (2) support and improvement of technological processes; (3) production management automation; and (4) economic optimisation. The digital twin can use various kinds of computational methods that have different requirements for the basic computational resources.

The goal of this research is to develop models, methods and algorithms for management of the resources of hybrid computing systems that implement the concept of Fog Computing, designed to support the "Digital Twins" of technological processes in industrial enterprises, as well as the implementation of a prototype of a Fog Computing platform based on such models, methods and algorithms. The platform should support the provision of computational resources for the simulation of industrial processes, considering information about the problem domain that provides forecasting of computational characteristics and time of execution of tasks of Digital Twins, for the implementation of planning and resource scheduling methods.

3. Research on information security in distributed computing systems, cloud computing, and automation engineering

Information management in many areas, such as a smart cities, smart manufacturing, smart medicine, etc., will be closely related to fog, edge, and cloud computing for the Internet of Things (IoT) and the Industrial Internet of Things. Despite many advantages of the Internet of Things, this concept entails considerable risks for confidentiality, integrity and accessibility of data related to the loss of information, denial of access to it for a long time, information leakage, collusion of providers, etc. One of the challenges is to develop reliable fog, edge and cloud systems that will reduce uncertainty in case of technical failures, data security breaches, collusion, etc.

The primary purpose of this project is to develop and evaluate methods and algorithms to maintain the security of expectations and Internet requirements for the Internet of Things in the face of unknown risks that are difficult or impossible to predict and that can not be managed proactively.

4. Application of cloud computing systems for smart cities

Under this project, the research team studies the use of cloud computing for Big Data processing in road traffic management. One of the research applications is the processing of experimental case studies on the quality of public transport based on historical traffic patterns. Another practical research application is the assessment of passenger mobility based on the analysis of travel cards’ usage history.


  1. Andrei Tchernykh, Jorge M. Cortés-Mendoza, Alexander Feoktistov, Igor Bychkov, Loic Didelot, Pascal Bouvry, Gleb Radchenko, Kirill Borodulin. Configurable Cost-Quality Optimization of Cloud-based VoIP. Journal of Parallel and Distributed Computing, Special issue on "Advances in Parallel and Distributed Computing and Optimization", Elsevier, 2018.
  2. Andrei Tchernykh, Vanessa Miranda-López, Mikhail Babenko, Fermin Armenta-Cano, Gleb Radchenko, Alexander Yu. Drozdov, Arutyun Avetisyan. Performance Evaluation of Secret Sharing Schemes with Data Recovery in Secured and Reliable Heterogeneous Multi-Cloud Storage. Cluster Computing. Springer, 2018.
  3. Renzo Massobrio, Sergio Nesmachnow, Andrei Tchernykh, Arutyun Avetisyan, Gleb Radchenko. Towards a Cloud Computing Paradigm for Big Data Analysis in Smart Cities. Programming and Computer Software, 2018, Vol. 44, No. 3, pp. 181–189. Springer, 2018.
  4. David Peña, Andrei Tchernykh, Sergio Nesmachnow, Renzo Massobrio, Alexander Feoktistov, Igor Bychkov, Gleb Radchenko, Alexander Yu. Drozdov, Sergey N. Garichev. Operating Cost and Quality of Service Optimization for Multi-Vehicle-Type Timetabling for Urban Bus Systems. Journal of Parallel and Distributed Computing, Special issue on "Advances in Parallel and Distributed Computing and Optimization", Elsevier, 2018.
  5. Andrei Tchernykh, Mikhail Babenko, Nikolay Chervyakov, Vanessa Miranda-López, Viktor Kuchukov, Jorge M. Cortés-Mendoza, Maxim Deryabin, Nikolay Kucherov, Gleb Radchenko, Arutyun Avetisyan. AC-RRNS: Anti-Collusion Secured Data Sharing Scheme for Cloud Storage. International Journal of Approximate Reasoning. Special Issue on Uncertainty in Cloud Computing: Concepts, Challenges and Current Solutions. Elsevier.​
  6. Renzo Massobrio, Sergio Nesmachnow, Andrei Tchernykh, Arutyun Avetisyan, Gleb Radchenko. Towards a Cloud Computing Paradigm for Big Data Analysis in Smart Cities. Proceedings of the Institute for System Programming of the Russian Academy of Sciences Digest, Vol. 28, 6. 2016, p. 121-140.
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