CS Talk: Alexandros Labrinidis

Handling Big Streaming Data with DILoS

Dr. Alexandros Labrinidis



For the past few years, our group has been working on problems related to Big Data through several projects. After briefly discussing these projects, the rest of this talk will present DILoS, which focuses on load management for ``Big Streaming Data.'' Today, the ubiquity of sensing devices as well as of mobile and web applications continuously generates a huge amount of data in the form of streams, which need to be continuously processed and analyzed, to meet the near-real-time requirements of monitoring applications. Such processing happens inside Data stream management systems (DSMSs), which efficiently support continuous queries (CQs). CQs inherently have different levels of criticality and hence different levels of expected quality of service (QoS) and quality of data (QoD). In order to provide different quality guarantees, i.e., service level agreements (SLAs), to different client stream applications, we developed DILoS, a novel framework that exploits the synergy between scheduling and load shedding in DSMS. In overload situations, DILoS enforces worst-case response times for all CQs while providing prioritized QoD, i.e., minimize data loss for query classes according to their priorities. We further propose ALoMa, a new adaptive load manager scheme that enables the realization of the DILoS framework. ALoMa is a general, practical DSMS load shedder that outperforms the state-of-the-art in deciding when the DSMS is overloaded and how much load needs to be shed. We implemented DILoS in our real DSMS prototype system (AQSIOS) and evaluated its performance for a variety of real and synthetic workloads. Our experiments show that our framework (1) allows the scheduler and load shedder to consistently honor CQs' priorities and (2) maximizes the utilization of the system processing capacity to reduce load shedding. DILoS was developed in collaboration with Thao N.Pham (as part of her PhD thesis) and Panos K. Chrysanthis. This work has been funded in part by two NSF Awards and a gift from EMC/Greenplum.



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 Laboratory (ADMT Lab). He is also an adjunct associate professor at Carnegie Mellon University (CS Dept). Dr. Labrinidis' research focuses on user-centric data management for scalable network-centric applications, including web-databases, data stream management systems, sensor networks, and scientific data management (with an emphasis on big data). He has published over 70 papers at peer-reviewed journals, conferences, and workshops; he is the recipient of an NSF CAREER award in 2008. Dr. Labrinidis served as the Secretary/Treasurer for ACM SIGMOD and as the Editor of SIGMOD Record. He is currently on the editorial board of the Parallel and Distributed Databases Journal. He has also served on numerous program committees of international conferences/workshops; in 2014, he was the PC Track-Chair for Streams and Sensor Networks for the ICDE conference. Personal home page: http://labrinidis.cs.pitt.edu


Wednesday, October 29, 2014 at 3:30pm to 4:30pm

St. Mary's Hall, 326
3700 Reservoir Road, N.W., Washington

Event Type

Academic Events


Georgetown College, Computer Science

Open to the public and the press?


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