13th International Symposium on Intelligent Distributed Computing
7-10 October 2019, Saint-Petersburg, Russia
Scheduling Complex Jobs in the Cloud – Challenges and Future Directions
Speaker: Helen Karatza
Professor Emeritus, Department of Informatics, Aristotle University of Thessaloniki, Greece
Cloud computing offers virtually unlimited computing resources on a pay-per-use approach to end users for running complex computationally intensive jobs without the problem of acquiring and maintaining expensive computers.
However, there are important issues that must be addressed in order to exploit cloud computing full potential. This is mainly due to the scale of the cloud and the increasing number of cloud users and applications deployed in it. Critical issues are effective resource allocation, job scheduling, cost, quality of service, energy conservation. The scheduling algorithms must provide good performance to leasing cost ratio.
Generally, job scheduling in large scale distributed computing systems is a challenging problem. Particularly important in cloud computing is the effective scheduling of complex real-time applications, taking into account not only the job response time but also the cost of the energy consumption for the cloud providers. Therefore, energy efficient scheduling strategies are required allowing for guarantees that the deadlines of complex jobs will be met.
In this talk we will present recent research covering various concepts on complex jobs scheduling in the cloud and we will provide research directions and challenges in the cloud computing area.
Helen Karatza is a Professor Emeritus in the Department of Informatics at the Aristotle University of Thessaloniki, Greece, where she teaches courses in the postgraduate and undergraduate level, and supervises doctoral and postdoctoral research. Dr. Karatza's research interests include Computer Systems Modeling and Simulation, Performance Evaluation, Grid and Cloud Computing, Energy Efficiency in Large Scale Distributed Systems, Resource Allocation and Scheduling and Real-time Distributed Systems.
Dr. Karatza has authored or co-authored over 220 technical papers and book chapters including five papers that earned best paper awards at international conferences. She is senior member of IEEE, ACM and SCS, and she served as an elected member of the Board of Directors at Large of the Society for Modeling and Simulation International. She served as Chair and Keynote Speaker in International Conferences.
Dr. Karatza is the Editor-in-Chief of the Elsevier Journal “Simulation Modeling Practice and Theory” and Senior Associate Editor of the “Journal of Systems and Software” of Elsevier. She was Editor-in-Chief of “Simulation Transactions of The Society for Modeling and Simulation International” and Associate Editor of “ACM Transactions on Modeling and Computer Simulation”. She served as Guest Editor of Special Issues in International Journals.
More info about her activities/publications can be found in http://agent.csd.auth.gr/~karatza/
Distributed Group Control of Autonomous Agents: Collective Robotics Use Case
Speaker: Vladimir Gorodetsky
Prof. of Computer Science, InfoWings, Russia
Despite the conceptual diversity of networked applications determining leading trends in the area of modern intelligent information technologies, the majority of them has many commons generalized within such frameworks as Internet of Things and/or Cyber-physical systems. Indeed, these frameworks were specifically developed to model and control of a wide class of modern applications composed of large number of intensively interacting heterogeneous (e.g. physical, virtual and social) autonomous objects with embedded computing and communication capabilities united in a network. The autonomous components of such applications operate in shared knowledge and data space and use intensive interactions to coordinate their individual behaviors and to conflict free control of shared resources and services. In the talk, this class of coordination and control is referred to as group control. Typical examples of group control applications are collective robotics, space-based distributed surveillance systems composed of small satellites, teams of unmanned aerial vehicles solving various distributed surveillance tasks for a number of customers, and many others.
The talk proposes a classification of group control tasks, analyses the peculiarities and common properties of various related applications and shows that the traditionally used purely knowledge-based (KB) paradigm of Artificial Intelligence (AI) does not quite fit to model the applications in question and needs an extension with some behavior-based (BB) concepts and models.
The first focus of the talk is on the BB group control problem statement, analysis of some BB-related basis concepts, e.g. behavior pattern, group behavior scenario, situation, situation assessment and situation awareness, among others. These concepts and corresponding models are ignored in ontologies of the KB paradigm of artificial intelligence. The second focus is on generic formal model of group control specifying the group behavior of autonomous agents in terms of a network of interacting state machines with inner states implementing predefined group control protocols (distributed algorithms), in typical use cases.
The introduced BB-concepts and proposed distributed algorithms of group control formalized as a network of autonomous agents (state machines) are illustrated by a case study implementing a group of interacting robots performing jointly autonomous assembly production without intervention of a human.
In conclusion, a sketch of a roadmap of future research and development in the area of group control is outlined.
Prof. of Computer Science. Received MS degree in mechanics from The Military Air Force Engineering Academy in St. Petersburg (1960) and MS degree in mathematics from Mathematical and Mechanical Department of The St. Petersburg State University (1970). Received his Ph.D. degree (1967) and Doctor of Technical Sciences degree (1973) in Optimal Control. Professor of Computer Science of the St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (1988-2018). He has authored or co-authored over 150 technical papers related to the areas of Optimal Control System Theory, Applied Statistics, Planning, Pattern Recognition and Artificial Intelligence, Multi-agent systems, Knowledge Discovery and Data Fusion, P2P Agent-based Service Oriented Technology and Applications, Computer Network Security, Distributed Data Mining and Knowledge Discovery, Semantic Technologies, Text Mining and Classification. He taught more than 10 graduate and undergraduate courses in the Military Airspace Engineering Academy and in Herzen State Pedagogical University in the areas of Applied Mathematics, Probability Theory, Mathematical Statistics, Optimization and Decision Making Methods, Mathematical Programming, Programming Languages and Software Engineering, Databases, Applied Algebra and Discrete Mathematics, Formal Grammar and Logic, Internet Technologies. He is member of IEEE Computer Society, International Society of Information Fusion (ISIF), International Foundation of Autonomous Agents and Multi-agent Systems (IF AAMAS), Russian and European Associations for Artificial Intelligence, Member of Editorial Board of the International Jourmal “Data Science and Analytics” (Springer). Current research interests: multi-agent systems, networks of autonomous agents and self-organization, semantic computing, ontologies, NLP, recommender systems, big data, group control, collective robotics, B2B production systems, space-based surveillance systems.
St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS)
Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University)
Prof. Igor Kotenko
Russia, 199178, St. Petersburg., No. 39, 14-th Linia, VI
Phone / Fax: +7-812-328-71-81 / +7-812-328-44-50