Scheduling in data management systems plays a very important role, particularly in periods of high demand or in the presence of diverging user preferences, which are often described as Service Level Agreements (SLAs). In this talk, I will describe our work in two different areas: (a) scheduling of web transactions in the presence of deadlines and (b) scheduling in data stream management systems.
With regards to web transaction scheduling, I will present ASETS, a parameter-free adaptive scheduling algorithm that automatically adapts to system load and to web transaction characteristics (i.e., interdependencies, deadlines, and priorities). ASETS prioritizes the execution of web transactions with the objective of minimizing weighted tardiness.
With regards to scheduling in data streams, I will summarize our early work, which can be used to optimize different Quality of Service metrics during the processing of multiple heterogeneous Continuous Queries (CQs). I will then describe our proposal for class-based scheduling of CQs and present an adaptive, synergistic load-manager and scheduler.
Dr. Alexandros Labrinidis received his Ph.D degree in Computer Science from the University of Maryland, College Park in 2002. He is currently an associate professor at the Department of Computer Science of the University of Pittsburgh and co-director of the Advanced Data Management Technologies Lab. He is also an adjunct associate professor at Carnegie Mellon University (CS Dept).
Dr. Labrinidis' research focuses on user-centric data management for network-centric applications, including web-databases, data stream management systems, sensor networks, and scientific data management. He has published over 60 papers at peer-reviewed journals, conferences, and workshops; he is the recipient of an NSF CAREER award in 2008. Dr. Labrinidis is currently the Secretary/Treasurer for ACM SIGMOD, and has served as the Editor of SIGMOD Record, and in numerous program committees of international conferences/workshops.
