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Title: Enabling Future-Proof Telemetry for Networked Systems
Abstract: Today's networked systems, such as data center, cellular, and sensor networks, face increasing demands on security, performance, and reliability. To fulfill these demands, we first need to obtain timely and accurate telemetry information about what is happening in the system. For instance, understanding the volume and the number of distinct network connections can help detect and mitigate network attacks. In storage systems, identifying hot items can help balance the server load. Unfortunately, existing telemetry tools cannot robustly handle multiple telemetry tasks with diverse workloads and resource constraints.
In this talk, I will present my research that focuses on building telemetry systems that are future-proof for current and new telemetry tasks, diverse workloads, and heterogeneous platforms. I will discuss the efficient algorithms and implementations that realize this future-proof vision in network monitoring for hardware and software platforms. I will describe how bridging theory and practice with sketching and sampling algorithms can significantly reduce memory footprints and speedup computations while providing robust results. Finally, I will end the talk with new directions in obtaining future-proof analytics for other types networked systems, such as low-power sensor and mobile devices, while enhancing energy efficiency and data privacy.
Bio:Alan (Zaoxing) Liu is a postdoctoral researcher at Carnegie Mellon University. His research interests are in networked and distributed systems with a recent focus on efficient systems and algorithmic design for telemetry, big-data analytics, and privacy. His research papers have been published in venues such as ACM SIGCOMM, USENIX FAST, and OSDI. He is a recipient of the best paper award at USENIX FAST '19 for his work on large-scale distributed load balancing. His work received multiple recognitions, including ACM STOC "Best -of-Theory" plenary talk and USENIX ATC "Best-of-Rest". Prior to CMU, he obtained his Ph.D. in Computer Science from Johns Hopkins University.
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