Brad Glasbergen, Fangyu Wu and Khuzaima Daudjee receive Best Demonstration Award at SIGMOD/PODS 2021

Monday, June 28, 2021

Cheriton School of Computer Science PhD student Brad Glasbergen, recent CS undergraduate Fangyu Wu and Professor Khuzaima Daudjee have received the Best Demonstration Award at the 2021 ACM SIGMOD/PODS Conference for Dendrite, a proof-of-concept system they developed that allows data systems to adapt easily to changes in workload and system behaviour.

This year’s ACM Special Interest Group on Management of Data conference was hosted virtually from June 20 to 25, 2021 in Xi’an, Shaanxi, China.

photo of Brad Glasbergen, Fangyu Wu, Khuzaima Daudjee

L to R: PhD student Brad Glasbergen, recent CS graduate Fangyu Wu, and Professor Khuzaima Daudjee

Brad Glasbergen studies distributed systems, with a primary focus on leveraging machine learning techniques to improve performance through understanding system behaviour for data storage and management.

Fangyu Wu (BCS ’20) is a recent graduate from the Cheriton School of Computer Science who works on performance analysis of complex data systems.

Cheriton School of Computer Science Professor Khuzaima Daudjee is interested in systems-oriented research, and works at the intersection of distributed systems and data management, particularly on building large-scale systems, storage and infrastructure in the cloud and on modern hardware. 

Data systems need to respond to fluctuations in client workloads, as variability places stress upon them, ties up system resources and causes performance to degrade. Typically, a system administrator resolves workload issues to deliver high performance by vigilantly configuring and monitoring the system. To help reduce the burden on system administrators, adaptive systems are emerging that extract important features and metrics from a system to determine how the system is performing and to adjust storage and processing techniques adaptively to optimize for a given workload.

However, many popular data systems do not support robust adaption capabilities. Furthermore, enhancing existing systems to support such adaption is challenging. It requires significant design and development effort, as each self-driving data system uses a custom monitoring and response approach.

Dendrite: Bolt-on adaptivity for data systems

Dendrite, the award-winning system developed by the Data Systems and Systems & Networks Groups’ researchers, addresses these challenges by integrating easily with any data system, eliminating the need for a large design and development effort. It also democratizes self-driving systems by providing a widely applicable framework to extract metrics, declaring adaption rules intuitively to determine if a reaction is warranted and, if so, to specify appropriate responses. 

“Dendrite automatically extracts metrics from application debug logs and the operating system, allowing system developers and administrators to specify adaption rules using these metrics,” said Professor Daudjee. “If a rule’s conditions are satisfied, Dendrite executes a user-defined function configured by a system administrator as a response. Dendrite is linked directly into debug logging libraries and does not rely on any system-specific functionality, so any system using such a library can automatically use Dendrite’s functions to deploy self-driving components.”

Watch the research team’s demonstration video presented at SIGMOD 2021

Two for two

This is the second time that Brad and his supervisor Professor Khuzaima Daudjee received a Best Demo Award at the ACM SIGMOD conference. Last year, PhD students Brad Glasbergen and Michael Abebe along with Professors Khuzaima Daudjee, Daniel Vogel and Jian Zhao received the Best Demo Award at ACM SIGMOD 2020 for Sentinel, a tool to understand data system behaviour and performance.


To learn more about this research, please see Brad Glasbergen, Fangyu Wu, Khuzaima Daudjee. Dendrite: Bolt-on Adaptivity for Data Systems. Proceedings of the 2021 International Conference on Management of Data (SIGMOD ’21), June 20–25, 2021, Virtual Event, China.

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