Faculty Seminar: A Study of Long-Tail Latency in n-Tier Systems:
RPC vs. Asynchronous Invocations

Dr. Qingyang Wang, Assistant Professor, LSU Computer Science

March 17, 2017 at 3:00 pm, 117  EE Conference Room



Long-tail latency or wide response time fluctuation of web-facing applications continues to be a serious
problem. An Amazon study showed that every 100ms increase in the page load time decreases sales by
1%. In this talk, we will describe an experimental study of an important class of long tail latency problems
that are specific to distributed systems: Cross-Tier Queue Overflow (CTQO) due to a combination of
millibottlenecks (with sub-second duration) and tightly-coupled servers in n-tier systems (e.g., Apache,
Tomcat, and MySQL) using RPC-style request-response communications. CTQO is a significant and broad
problem, since the initiating millibottleneck can originate from any system resource (e.g., CPU, memory,
and network). We will discuss several practical solutions to this problem. For example, we show that CTQO
can be reduced or avoided by replacing thread-based servers with asynchronous servers which support
asynchronous inter-tier communication. Our studies show that in the era of cloud computing where
resource sharing is a common practice and millibottlenecks are unavoidable, we need to rethink the
traditional thread-based architecture of n-tier systems in order to achieve both high performance and high
resource efficiency in cloud.


Dr. Qingyang Wang is an Assistant Professor in the Department of EECS at Louisiana State University-
Baton Rouge. His research is in distributed systems and cloud computing with a current focus on
performance and scalability analysis of large-scale web applications (e.g., He has led
research projects at LSU on cloud performance measurements, scalable web application design, and
automated system management in clouds. He has collaborated extensively with scientists and researchers
from industry companies, including IBM, HP, Amazon, Fujitsu (Japan), and Wipro (India). Dr. Wang received
his Ph.D. degree from Georgia Institute of Technology in 2014. He is a recipient of the Best Student Paper
award in IEEE Cloud 2011 and nominated for the Best Paper Award in IEEE Cloud 2016.