Award Abstract # 1546083
BIGDATA: Collaborative Research: F: Holistic Optimization of Data-Driven Applications

NSF Org: IIS
Division of Information & Intelligent Systems
Recipient: UNIVERSITY OF WASHINGTON
Initial Amendment Date: September 14, 2015
Latest Amendment Date: September 14, 2015
Award Number: 1546083
Award Instrument: Standard Grant
Program Manager: Maria Zemankova
IIS
 Division of Information & Intelligent Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2015
End Date: May 31, 2020 (Estimated)
Total Intended Award Amount: $600,000.00
Total Awarded Amount to Date: $600,000.00
Funds Obligated to Date: FY 2015 = $399,568.00
History of Investigator:
  • Alvin Cheung (Principal Investigator)
    akcheung@cs.berkeley.edu
Recipient Sponsored Research Office: University of Washington
4333 BROOKLYN AVE NE
SEATTLE
WA  US  98195-1016
(206)543-4043
Sponsor Congressional District: 07
Primary Place of Performance: University of Washington
Seattle
WA  US  98195-2350
Primary Place of Performance
Congressional District:
07
Unique Entity Identifier (UEI): HD1WMN6945W6
Parent UEI:
NSF Program(s): Big Data Science &Engineering
Primary Program Source: 01001516DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7433, 8083
Program Element Code(s): 808300
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

We interact with online shopping and banking websites on a daily basis. Many of these websites are powered by data-driven applications. Such application often consists of two parts: an application hosted on an application server, and a database management system (DBMS) hosted on a separate server from the application server that maintains persistent data. Unfortunately, many data-driven applications suffer from performance problems, such as taking a long time to load a page or inability to scale up to serve large number of clients simultaneously. The state of the art in discovering and fixing performance problems in data-driven applications is to examine the two parts of the application separately, and doing so misses many opportunities in discovering and fixing such problems. Unlike prior approaches, in this project we will treat the DBMS and the application in tandem. In particular, we will devise new techniques and tools to help identify performance problems, understand the cause of such problems, and fix them automatically. This project will open up new opportunities in cross-layer program compilation and optimization, with the practical goal of improving the performance of data-driven applications that will have a significant impact in many aspects of our daily lives. The findings from this project will be incorporated into undergraduate and graduate software engineering, introduction to data management, and compiler classes to be offered at the University of Chicago and the University of Washington. The outreach activities of this project will include engaging and advising students through special programs geared toward under-represented groups such as the Distributed Research Experiences for Undergraduates (DREU) organized by CRA-W (Computing Research Association -- Women) and Diversity Workshops organized by CRA-W.

Specifically, the proposed research consists of three thrusts: (1) a new cross-layer program analysis framework that produces an end-to-end profile of data-driven applications by understanding the application code, the queries that the application sends to the DBMS, and how the DBMS processes such queries; (2) a program analysis and testing framework that identify performance problems in data-driven applications by leveraging the end-to-end profile created from (1); and (3) new means to optimize data-driven applications by transforming both the application code and the queries that are issued. These three thrusts will work together to improve the performance of data-driven applications and help programmers detect performance problems during development. Software developed by this project, benchmarks used for evaluation, and performance comparison with existing techniques will be released to public domain through the project website. Further information will be available at the project website (https://people.eecs.berkeley.edu/~akcheung/coopt.html).

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Junwen Yang, Cong Yan, Chengcheng Wan, Shan Lu, Alvin Cheung. "View-Centric Performance Optimization for Database-Backed Web Applications." ICSE2019 , 2019
Yang, Junwen "PowerStation: Automatically detecting and fixing inefficiencies of database-backed web applications in IDE?" 26th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) , 2018 Citation Details
Yang, Junwen and Subramaniam, Pranav and Lu, Shan and Yan, Cong and Cheung, Alvin "How not to structure your database-backed web applications: a study of performance bugs in the wild" ICSE '18 Proceedings of the 40th International Conference on Software Engineering , 2018 10.1145/3180155.3180194 Citation Details

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