XCloud: Extensible Performance Management  for Cloud Databases

Project Description

Cloud computing, one of the most promising concepts in information technology, is drastically transforming our perception of how web-based data-centric applications are deployed. By providing a suite of virtualized resources, it reduces data processing services to commodities that could be acquired and paid-for on-demand yielding improved cost efficiencies and at the same time delivering elastic scalability. Despite the relatively fast growth and increased adoption of cloud data services, challenges related to application management still exist. Provisioning cloud resources to meet application-specific Quality-of-Service (QoS) goals, assigning incoming query processing workloads to the reserved resources to optimize resource utilization, monitoring performance factors to ensure acceptable QoS levels, are some of the critical tasks that are still addressed through custom tools at the application level which drastically increase the application development and maintenance overhead. In this project, we argue that the management of data processing applications should itself be offered to developers as a cloud-based automated service.

This project introduces performance extensibility as the key feature of next generation cloud data processing services that could help address the aforementioned challenges. Performance extensibility refers to the ability to express customized performance models and constraints and use them to automatically extend the functionality of core application tasks, such as performance optimization and resource and workload management, towards meeting the application's performance objectives. This project presents a comprehensive research agenda to support performance extensibility for cloud databases by (a) enabling performance criteria and performance Service-Level-Agreements (SLAs) to be defined at the application level and (b) developing a suite of application management services, including workload, resource and SLA management, that can seamlessly customize their functionality towards satisfying application-specific performance objectives.



Olga Papaemmanouil (Brandeis University)


Chi Zhang (PhD student)

Ryan Marcus (graduation in 2019, First position: MIT postdoc )
 Kyriaki Dimitriadou (graduation in 2017, First employment Amazon)

Dimokritos Stamatakis  (Ph.D. student, Brandeis University)

Sofiya Semenova (Undergraduate, moved on as PhD student at University of Boulder, 2017)
Ari Karchmer (Undergraduate, moved on as PhD student at Boston University, 2018)
Jeffrey Couture (Undergraduate)
Shuai Yu (Master Student, First employment Google Inc, 2018)
David Barsky (Undergraduate, 2017)



The code for these projects can be found here:

NashDB: http://git.io/nashdb

WiseDB: http://git.io/wisedb 


This project is sponsored by the NSF Career Award  IIS-1253196.