ICCS-DE 2020

Andrei Tchernykh, CICESE Research Center, Ensenada, Mexico

http://usuario.cicese.mx/~chernykh/

SPEECH: "Uncertainty and optimization in cloud computing"

About the Speaker

Andrei Tchernykh is holding a full professor position in computer science at CICESE Research Center, Ensenada, Baja California, Mexico He is Chair of the Parallel Computing Laboratory at CICESE, Mexico and “Laboratory of Problem-Oriented Cloud Computing” at South Ural State University, Russia. He is researcher of ISP RAS - Institute for System Programming of the Russian Academy of Sciences and MIPT - Moscow Institute of Physics and Technology, Moscow, Russia.

He obtained his Ph.D. in 1986 from the Institute of Precision Mechanics and Computer Engineering of the Russian Academy of Sciences, where he took part in the supercomputer ELBRUS design and development. He gained industrial experience as supercomputer design team leader in Advance Technical Products Corp, and Supercomputer Design Department of Electro-Mechanical Enterprise, Russian leaders in HPC design and development.

Tchernykh leads a number of research projects and grants in different countries funded by CONACYT, NSF, ANII, Ochoa, INRIA, FNR, UC MEXUS, DAAD, LAFMI, UJF, INPG, REDII, FUMEC, etc. He has published over 200 papers in some of the most distinguished scientific journals and international conferences and served as a TPC member, and general co-chair of more than 250 professional peer-reviewed conferences. He has graduated 36 Ph.D. and M.S. students, and served as the External Examiner of Academic Council for Ph.D. programs in India, Malaysia, Germany, Luxembourg. México, and France.

He is awarded Global Scholars Fellow at Tsinghua University (China), German Academic Exchange Service fellowship at University of Göttingen, Dortmund University, Technische Universität Clausthal, and Severo Ochoa fellowship at Barcelona Supercomputing Center (Spain). He was an Invited Visiting Researcher at Centre de recherché INRIA Lille - Nord Europe (France), Université Grenoble Alpes (France), Luxembourg University (Luxembourg), Moscow Institute of Physics and Technology (Russia), Institut National Polytechnique de Grenoble (France), University of California–Irvine (USA), University of Southern California (USA), Université Joseph Fourier (France), UdelaR (Uruguay), etc.

He is an editorial board member of several journals, such as International Journal of Metaheuristics, Supercomputing Frontiers and Innovations, Computational Mathematics and Software Engineering, Proceedings of ISP RAS, etc. He also has served as a guest editor for several special issues including International Journal of Approximate Reasoning (Elsevier) Special Issue on Uncertainty in Cloud Computing: Concepts, Challenges, and Current Solutions.

Prof. Andrei Tchernykh is engaged in extensive research on grid and cloud research addressing resource optimization, both, theoretical and experimental, cybersecurity, uncertainty, scheduling, multi-objective optimization, heuristics and meta-heuristics, adaptive resource allocation, energy-aware algorithms and Internet of Things.

Abstact

In this talk, we discuss the opportunities and challenges of mitigating uncertainty in cloud computing. We analyze the structure of uncertainties arising from performance and bandwidth changing, virtualization, and elasticity, among others. We also describe uncertainty associated with workload properties, dynamism of the execution context, and uncertainty associated with such important aspects as privacy, security, and availability.

We address scheduling problems for different scenarios of HPC, Grid and Cloud Infrastructures. We provide some theoretical and experimental results and discuss static, dynamic and adaptive approaches. We discuss challenges of resource optimization in the presence of uncertainty, ranged from handling resource heterogeneity, dynamic behavior of the execution context as well as uncertainties associated with cloud storages and Internet of Things.

Dynamicity of characteristics and elasticity of IoT add a considerable uncertainty on various levels of the computation, communication, and storage. One of the challenges is to mitigate the uncertainty of the occurrence of technical failures, data security breaches, collusion, etc.

We also discuss methods and algorithms for supporting security expectations and requirements of IoT under unknown risks that are difficult or impossible to anticipate and cannot be managed proactively. They are based on mechanisms of adjustable and adaptive security, reliability, and redundancy to cope with different user-provider preferences, workloads, system state, errors, fog-edge-cloud properties, etc.

Igor Sheremet, Russian Foundation for Basic Research

SPEECH: "Resource-based games"

About the Speaker

Born March 23, 1956

Doctor of Engineering Sciences (1994), Professor (1998), Corresponding Member of Russian Academy of Sciences (2016)
Deputy Director for Science, Russian Foundation for Basic Research
Co-Chair of Task Group “Advanced Mathematical Tools for Data-Driven Applied Systems Analysis” of Committee on Data (CODATA) of International Science Council (ISC)
Chair of Joint Working Group of CODATA and International Institute of Applied Systems Analysis (IIASA) on Big Data and Systems Analysis
Vice-Chair of RAS Committee on Systems Analysis
Vice-Chair of RAS Science Board on Robotics and Mechatronics
Member of RAS Science Board on Quantum Technologies
Member of IIASA Science Advisory Committee, 2014-2020

Main contributions

In 80-90th developed “Set-of-Strings” mathematical framework for Data and Knowledge Engineering, providing knowledge-based implementation of Big Data, Internet of Things, Security Information and Event Management (SIEM), and various associated up-to-date technologies.

Since 2000th is developing theory of recursive multisets and its applications to systems analysis, operations research, economical combinatorics, and associated areas. The main goal of these developments is establishing mathematical background for optimal (rational) planning and scheduling in the created global digital economy, expected to be smart by spending minimal amounts of resources for production and relocation of goods and delivery of services. Such smartness must be maintained in all possible situations, including that, which would be consequences of destructive impacts (natural hazards, technogenic catastrophes, terror, mutual sanctions, etc.) on the technological and resource bases of economical systems.

Abstact

Article is dedicated to the multigrammatical modelling of games. Basic notions and definitions, concerning multisets and multiset grammars, are considered. So called resource-based games (RBG) are introduced, and representation of their simplest class – antagonistic RBG – by filtering multiset grammars is considered in details. More sophisticated cooperative and coalitional RBG are proposed. Directions of further development of RBG by application of multigrammatical framework are discussed.

Igor Bychkov, Matrosov Institute for System Dynamics and Control Theory of the Siberian Branch of the Russian Academy of Sciences, Irkutsk, Russia

http://idstu.irk.ru/en/Bychkov_IV/

SPEECH 1: "Swarm optimization approach to non-stationary physical field survey problem using a group of autonomous underwater vehicles"

SPEECH 2: "Situational awareness for distributed mobile robot teams under limited communication"

About the Speaker

Igor Bychkov is the Deputy Chairman of SB RAS for scientific work, Director of the Irkutsk branch of the SB RAS.

He graduated from the Mathematics Department of Irkutsk State University in 1983. Igor Bychkov worked at the Irkutsk Computing Center of the Siberian Branch of the Academy of Sciences of the USSR since 1981. In the future, this Сenter has renamed the Matrosov Institute for System Dynamics and Control Theory of the Siberian Branch of the Russian Academy of Sciences (ISDCT SB RAS).

Igor Bychkov defended his thesis in 1991. Next, he received a Dr Degree in 2003. Igor Bychkov has been a Director of ISDCT SB RAS since 2007. He was elected a corresponding member of the Russian Academy of Sciences in the Department of Nanotechnology and Information Technology of the Russian Academy of Sciences (the speciality Computer Science) in 2008. Now, he is Academician of RAS, professor.

Igor Bychkov is a member of a number scientific and expert councils, editorial boards of scientific journals. He is an expert for the Russian Foundation for Basic Research, Russian Scientific Foundation, Russian Academy of Sciences. He leads a number of national and international research projects. His main interests include artificial intelligence, geoinformation systems, WEB-technologies, systems of intelligent data analysis, mathematical modeling, and cloud computing.

Igor Bychkov is a member of the Scientific Council of the Russian Academy of Sciences “High-Performance Computing Systems, Scientific Telecommunications and Information Infrastructure”, the Council of the SB RAS Program “Information and Telecommunication Resources of the SB RAS”, and the chairman of the Scientific Coordination Council for Informatization under the Presidium of the Irkutsk Scientific Center of SB RAS.

He was Vice-mayor of Irkutsk in 2006-2007.

Igor Bychkov is the author and co-author of more than 150 publications, including 8 collective monographs. Under his leadership 6 candidate dissertations were completed and defended.

Abstract: "Swarm optimization approach to non-stationary physical field survey problem using a group of autonomous underwater vehicles"

The paper considers the problem of searching for the source of a non-stationary physical field. We assume that the use of swarm algorithms may be applicable in this case. A hybrid of the Whale Optimization Algorithm and Grey Wolf Optimizer is proposed in this paper. The algorithm has several advantages over its origins: a more precise solution of the optimization problem for low-dimensional functions and a higher convergence rate of the first iterations. Two modifications were made to adapt the algorithm to the requirements of the problem. The proposed algorithm is used as a basis for a control strategy for a group of autonomous underwater vehicles. As a result, in the vast number of cases, the group can find the source within the given number of search iterations.

Abstract: "Situational awareness for distributed mobile robot teams under limited communication"

A high level of team situational awareness is essential during complex, large-scale missions of autonomous mobile robots. When a situation appears that needs inter-agent interaction for cooperative decision-making, the basic understanding of the current conditions ought to be identical within the group. To achieve this requirement, all emergent information of acute importance must be promptly shared among team members. It is a non-trivial problem for large-sized and distributed robotic teams, especially under hard communication constraints. The problem considered in this paper is to find an efficient emergency broadcasting strategy for search and survey operations of the robotic groups providing the fastest way for any agent to aware the remaining team in case of any unexpected changes. A number of simple ruled-based heuristics is proposed to treat the problem. The comparison between the suggested approaches is made regarding both the quality of the obtained solutions and the working speed.

Mikhail Babenko, North-Caucasus Federal University, Stavropol, Russia

SPEECH: "Computationally secure threshold secret sharing scheme with minimal redundancy"

About the Speaker

Mikhail Babenko graduated from Stavropol State University (SSU) in 2007 with degree in mathematics. Received Ph.D. degree in mathematics from SSU in 2011. He works as assistant professor in Department of Applied Mathematics and Mathematical Modeling since 2012. He is an author of over 63 publications and 5 patents. His research interests include cloud computing, high-performance computing, residue number systems, neural networks, cryptography. National, international projects and industrial collaborations: 11 national projects and 3 project under Federal target program Russia Federation.

Abstact

A well-developed and technologically advanced telecommunication infrastructure stipulate a rapid growth of electronic data exchange. Nowadays, it is common for the public and private institutions as well as the industrial companies to outsource massive electronic databases to storage centers. The cloud computing technology allows the users to work with such centers without even knowing their internal structure. However, storing all the data in one center creates a single point of failure and raises privacy and availability concerns, especially in the sense of disaster preparedness and recovery. Secret sharing is a cryptographic technology, which allows us to address both privacy and availability issues simultaneously.

The presentation introduces novel mathematical models and techniques that allow us to enrich the spectrum of services that secret sharing can provide to cloud computing technology users. They deepen our understanding of which mathematical tools are required to bring the existing theoretical constructions closer to the current industrial needs.

We propose a computationally, secure secret sharing scheme based on the minimally redundant modular code. It reduces the computational complexity of data encoding and decoding and reduces data redundancy. We will show that it is computationally secure and provides data redundancy equivalent to the redundancy of the Rabin system. We have demonstrated that the minimally redundant modular code does not satisfy the criterion of compactness of a sequence. Still, it can be used as an asymptotically ideal secret sharing scheme.