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ECE PhD Dissertation Defense: Ran Liu
October 30, 2020 @ 11:00 am - 12:00 pm
PhD Dissertation Defense: Optimal Proactive Services with Uncertain Predictions
Location: Zoom Link
Abstract: With the evolution of technologies such as machine learning and data science, proactive services with the aid of predictive information have been recognized as a promising method to exploit network bandwidth, storage, and computation resources to achieve improved user experiences, especially delay performance.
Specifically, services can be processed proactively when the system is lightly loaded, with the results stored to meet user demand in the future.
Our primary goal in the thesis is to investigate the fundamental performance improvement that can be achieved from proactive services under uncertain predictions. We aim to analyze the queueing behavior of proactive systems under certain proactive strategies and characterize the improvement in terms of the limiting fraction of proactive work and the limiting average delay.
In the first work, we analytically investigate the problem of how to efficiently utilize uncertain predictive information to design proactive caching strategies with provably good access-delay characteristics.
First, we derive an upper bound for the average amount of proactive service per request that the system can support.
Then we analyze the behavior of a family of threshold-based proactive strategies with a Markov chain, which shows that the average amount of proactive service per request can be maximized by properly selecting the threshold.
Finally, we propose the UNIFORM strategy, which is the threshold-based strategy with the optimal threshold, and show that it outperforms the commonly used Earliest-Deadline-First (EDF) type proactive strategies in terms of delay.
We perform extensive numerical experiments to demonstrate the influence of thresholds on delay performance under the threshold-based strategies, and specifically, compare the EDF strategy and the UNIFORM strategy to verify our results.
In the second work, we study a more generalized proactive service problem with a more generalized service model and derive explicit solutions on the limiting average fraction of proactive work and the limiting average delay in closed-form expressions.
In this work, we analytically investigate how to optimally take advantage of under-utilized network resources for proactive services with the aid of uncertain predictive information.
Specifically, we first derive an upper bound on the fraction of services that can be completed proactively by a single-server system.
Then we analyze a family of fixed-probability (FIXP) proactive strategies in two proactive systems, namely the Genie-Aided system and the Realistic Proactive system.
We analyze the asymptotic behaviors of the FIXP strategies by modeling a Markov process and the corresponding embedded Markov Chain.
We obtain optimal FIXP strategies in both systems and prove that the optimal FIXP strategies maximize the limiting fraction of proactive service among all proactive strategies and minimize average delay among FIXP strategies.
We perform extensive numerical experiments to demonstrate the influence of the parameter of FIXP on the performance of the limiting fraction of proactive service and the limiting average delay in both proactive systems and verify our theoretical results in multiple scenarios.