During the last forty years, data management systems have grown in scale, complexity, and number of installations. At the same time, administration of these systems has become very expensive with the human factor dominating the total cost of ownership. Current trends like cloud computing make this situation even more problematic for service providers who have to configure and manage thousands of database nodes.
There has been a significant amount of research addressing this problem by providing autonomic or self-* features in database systems to support complex administrative tasks like physical database design, problem diagnosis, and performance tuning. However, novel challenges arise from trends like containerization and virtualization; emerging systems like Kafka, Presto, and Spark; auto-scaling and transient clusters on the cloud; and Software-as-a-Service (SaaS). A major challenge is the need to scale self-management capabilities to the level of hundreds to thousands of nodes while taking economic factors into account.
Autonomic, or self-managing, systems are a promising approach to achieve the goal of systems that are easier to use and maintain. A system is considered to be autonomic if it possesses the capabilities to be self-configuring, self-optimizing, self-healing and self-protecting. The aim of the SMDB workshop is to provide a forum for researchers from both industry and academia to present and discuss ideas related to self-management and self-organization in data management systems ranging from classical databases to data stream engines to large-scale cloud environments that utilize advanced in AI, machine learning and data mining and analysis.
A Framework for Self-Managing Database Systems
Jan Kossmann (Hasso Plattner Institute); Rainer Schlosser (Hasso Plattner Institute)
Towards Auto-Scaling Existing Transactional Databases with Strong Consistency
Michael Georgiou (Cyprus University of Technology); Aristodemos Paphitis (Cyprus University of Technology); Michael Sivirianos (Cyprus University of Technology); Herodotos Herodotou (Cyprus University of Technology)
Distribution-Driven, Embedded Synthetic Data Generation System and Tool for RDBMS
Joseph W. Hu (SAP); Ivan Bowman (SAP); Anisoara Nica (SAP SE, Waterloo, Canada); Anil Goel (SAP)
Jingren Zhou is Vice President at Alibaba Group. He is responsible for driving data intelligence infrastructure and several key data-driven businesses at Alibaba. Specifically, he leads work to develop cloud-scale distributed computing platform, data analytic products, and various business solutions. He also leads work to develop advanced techniques for personalized search, product recommendation, and advertisement at Alibaba's e-commerce platforms, including Taobao and Tmall. His research interests include cloud-computing, databases, and large scale machine learning systems. He received his PhD in Computer Science from Columbia University. He is a Fellow of IEEE.
David Lomet founded the Database Group at Microsoft Research Redmond in 1995 and managed it for 20 years. His research career began at IBM where, while on a 1975-76 sabbatical at the University of Newcastle-on-Tyne, he invented atomic actions (a form of transactions). He later worked at Wang Institute as a faculty member, and at Digital Equipment Corporation as a software architect and research staff member. He received a Ph.D. in computer science from the University of Pennsylvania. Lomet’s primary focus has been the engineering of database systems, with a focus on database system kernels. His work on concurrency control and recovery contributed to making DEC’s Rdb and Microsoft’s SQL Server database management systems leaders in cost/performance. His Deuteronomy research project’s latch-free Bw-tree index and log structure store are key elements in Microsoft’s Hekaton main memory database and Azure Cosmos DB cloud data service. Deuteronomy won the Microsoft Research Redmond “2017 Best Research Project” Award. Lomet is an author of over 120 papers, with two SIGMOD best paper awards, and over 60 patents. Lomet has won IEEE awards as well as the ACM SIGMOD Contributions Award, and at this conference the TCDE Service Award, for his 25 year tenure as EIC of the IEEE Data Engineering Bulletin. HE has also served as editor of ACM TODS, VLDB Journal and others, and has been a member of the VLDB Board. He has been a PC co-chair for ICDE and VLDB. He is a member of the IEEE Computer Society Board of Governors and society Secretary, and has been First Vice President and Treasurer. He is a fellow of IEEE, ACM, and AAAS, and a member of the National Academy of Engineering.