We will have the presence of one to three experts on the domain of software engineering and IoT, to enlighten our understanding of best practices and to discuss future challenges at SERP4IoT 2020.
Marcos Dias de Assuncao was, until recently, an Inria Starting Researcher at Avalon, LIP, ENS Lyon. Prior to working for Inria, he was a research scientist at IBM Research in Sao Paulo. He obtained his PhD in Computer Science at the University of Melbourne in Australia (2009).
Marcos has over 19 years of experience in research and development in distributed systems and networks, has published over 60 papers,
deposited more than 30 patents applications, and contributed to the design and development of several software systems. His current topics of interest comprising deep reinforcement learning to address resource management problems in edge and cloud computing and fault tolerance for distributed data stream processing applications.
Distributed Stream Processing (DSP) applications are increasingly used in new pervasive services that process enormous amounts of data in a seamless and near real-time fashion. Edge computing has emerged as a means to minimise the time to handle events by enabling processing (i.e., operators) to be offloaded from the Cloud to the edges of the Internet, where the data is often generated. Deciding where to execute such operations (i.e., edge or cloud) during application deployment or at runtime is not a trivial problem. Edge computing resources are often more limited than their cloud counterparts, and they may be powered by batteries or renewable and intermittent energy sources. In this talk I will introduce the problem of placing and reconfiguring DSP applications onto cloud-edge infrastructure, key steps towards achieving elastic applications. Then, I will provide some details on how Reinforcement
Learning (RL) and Monte-Carlo Tree Search (MCTS) can be used to reassign
operators during application runtime. I will describe an optimisation to an MCTS algorithm that achieves latency similar to other approaches, but
with fewer operator migrations and faster execution time.
Due to medical reasons, Prof. Litoiu won't be able to company us in this virtual edition of SERP4IoT 2020. We, the organizing committe, wish him a speed recovery.
Marin Litoiu is a Full Professor in the Department of Electrical Engineering and Computer Science and in the School of Information Technology, York University. He leads the Adaptive Software Research Lab and focuses on making large software systems more versatile, resilient, energy-efficient, self-healing and self-optimizing. His research won many awards including the IBM Canada CAS Research Project of the Year Award, the IBM CAS Faculty Fellow of the Year Award for his “impact on IBM people, processes and technology,” three Best Paper Awards and two Most Influential Paper Awards. Prior to joining York University, Dr. Litoiu was a Research Staff member with the Centre for Advanced Studies in the IBM Toronto Lab where he led the research programs in software engineering and autonomic computing. He received the Canada NSERC Synergy Award for Innovation in recognition for these collaborative university/industry activities. He was also recipient of the IBM Outstanding Technical Contribution Award for his research vision on Cloud Computing. Dr. Litoiu is one of the founders of the SEAMS Symposium series—ACM/IEEE Software Engineering for Adaptive and Self-Managing Systems and the Chair of the SEAMS Steering Committee. He has been the General Chair of SEAMS in 2013 and 2019, and also serves on the steering committees of CASCON. Dr. Litoiu is also member of IEEE CS Conference Advisory Committee and he is the Scientific Director of "Dependable Internet of Things Applications (DITA)," an NSERC CREATE program. Link to presenter web site