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<div class="">This is an announcement of Hai Nguyen's Candidacy Exam.</div>
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<div class="">Candidate: Hai Nguyen</div>
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<div class="">Date: Tuesday, May 02, 2023</div>
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<div class="">Time: 2:30 pm CST</div>
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<div class="">Remote Location: <a href="https://urldefense.com/v3/__https://uchicago.zoom.us/j/94903341306?pwd=UWViUFM5WnNzREY0WmtBMTlFcW1mQT09__;!!BpyFHLRN4TMTrA!8YNNtSW2zryOVCLpssArvincf1N6LSMJvViYXbE-WdNy90oisyeyz22MEMVQ_oGBC3M7lGMn9vwuMI2bWY8i9QyGxA$" target="_blank" class="">https://uchicago.zoom.us/j/94903341306?pwd=UWViUFM5WnNzREY0WmtBMTlFcW1mQT09</a></div>
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<div class="">Location: JCL 298</div>
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<div class="">Title: Rate-based Abstract Machine: An Efficient Computation Model for Guaranteeing Performance of Bursty, Real-time Applications</div>
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<div class="">Abstract: Today's Function-as-a-Service (FaaS) systems only provide statistical guarantees, so bursty, real-time applications cannot rely on them to deliver quality guarantee computation. We propose Rate-based Abstract Machine (RBAM) -- an extension
of the FaaS computation service that associates a guaranteed invocation rate to the FaaS function. This guarantee enables timely invocation allocation, enabling applications to meet real-time requirements.</div>
<div class="">We develop analytical models to study RBAM, proving its capability in bounding FaaS invocation allocation latency which is sufficient to guarantee applications' real-time deadlines. We also use the analytical model to prove the feasibility of
RBAM implementation and derive a naive RBAM allocation algorithm. We use this algorithm to create an RBAM realization, named Real-time Serverless (RTS).</div>
<div class="">To demonstrate RBAM applicability, we implement a distributed real-time video analytic</div>
<div class="">application with Real-time Serverless. RTS ensures both statistical and absolute application</div>
<div class="">performance guarantees. These new capabilities are also robust against a wide range of workload burstiness, opening new performance engineering space with flexible deployments. Further, we use RTS as a building block to create Storm-RTS, a stream
processing engine that utilizes the rate guarantee to implement application performance transparency and predictability. Such new features allow Storm-RTS to gain robust performance stability, high deployment flexibility, and can even optimize application
deployment for different objectives with simple declarative policies.</div>
<div class="">Given this foundation, we will provide an efficient RBAM implementation. We plan to propose several guaranteed invocation rate allocation algorithms that can deploy any rate-guarantee with low overhead. We will also develop analytical studies
and simulations to show their efficiency and then implement them inside RTS. We expect to make RTS implementation cost asymptotically small for a large number of RBAMs.</div>
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<div class="">Advisors: Andrew Chien</div>
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<div class="">Committee Members: Andrew Chien, Kyle Chard, Junchen Jiang, and Sanjay Krishnan</div>
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