Events & News
CS Colloquium
| Category | Lecture |
| Date | Tuesday, February 2, 2016 |
| Time | 11:00 am |
| Concludes | 12:15 pm |
| Location | Gould-Simpson 906 |
| Details | Please join us for coffee and light refreshments at 10:45am, Gould-Simpson, 9th floor atrium. Faculty Host: Katherine Isaacs |
| Speaker | Liting Hu |
| Title | Ph.D. Candidate |
| Affiliation | College of Computing at Georgia Tech |
ELF: Efficient Lightweight Fast Stream Processing at Scale
Large Internet companies like Facebook, Amazon, and Twitter are increasingly recognizing the value of stream data processing, using tools like Flume, Muppet, or Storm to continuously collect and process incoming data in real time to help govern company activities. Applications include monitoring marketing streams for business-critical decisions, identifying spam campaigns from social network streams, datacenter’s intrusion detection and troubleshooting, and others. In contrast to batch jobs pipelined to run sequentially, streaming jobs are more likely to run concurrently with dynamic flexible requirements. Stream processing platforms, therefore, must not only offer throughput and low latency, but also scale with the ever-larger diverse concurrent jobs.
This talk presents a new stream processing model, termed ELF, which addresses these new challenges. ELF proposes a novel decentralized “many masters/many workers” architecture implemented over a set of agents distributed across the datacenter systems. We will discuss an ELF prototype system implemented and evaluated for a large-scale configuration built on previous work with distributed hash tables. It demonstrates scalability, high per-node throughput, sub-second job latency, and sub-second ability to adjust the actions of jobs being run. We will also discuss state-of-the-art alternatives and motivating business case details.
Biography
Liting Hu is a Ph.D candidate in computer science at Georgia Institute of Technology. Her primary research interests are in big data analytics and its intersection with distributed systems. Liting has also done research in datacenter’s resource management and system virtualization technology. She is co-advised by Dr.Karsten Schwan and Dr.Matthew Wolf. She spent summers interning at IBM T.J. Watson Research Center, Intel Science and Technology Center for Cloud Computing, Microsoft Research Asia, VMware, and has been working closely with them. She received her B.Tech from Huazhong University of Science and Technology under the supervision of Dr.Hai Jin in 2007 and received an award for Best Computer Science Undergraduate Honors Thesis.
Webpage: https://sites.google.com/site/litinghugatech/