Zhenyu Song will present his General Exam "Robustly Improving Byte Miss Ratio with Workload-Learning Caching" on Tuesday, May 21, 2019 at 2pm in CS 402. The members of his committee are as follows: Kai Li (adviser), Wyatt Lloyd, and Ryan Adams Everyone is invited to attend his talk, and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. Abstract : Content Distribution Networks (CDNs) made up of widely-distributed caches deliver the majority of bytes sent across the Internet. Their efficiency and cost-effectiveness directly correspond to the byte miss ratio achieved by their caches. Improving the byte miss ratio across the many caches in a CDN is hampered by the diversity of workloads resulting in no one current algorithm being best in all cases. We introduce the workload-learning caching (WLC) algorithm that robustly improves byte miss ratio. WLC is robust because its model and feature set allow it to make decisions using a superset of the information used in current algorithms. WLC improves byte miss ratio by learning from a workload and adapts to recent request patterns dynamically. Simulation results using 3 CDN traces and 3 other traces with a wide range of cache sizes show that WLC robustly improves byte miss ratio over the state of the art algorithms. We have implemented WLC in a CDN caching proxy. Our experiments show that it provides lower byte miss ratios while achieving throughput and latency similar to the baseline. It requires moderate moderate amount of CPU power and relatively small memory overhead in today's CDN server without GPU or accelerators. Book : Distributed Systems: Principles and Paradigms . Andrew S. Tanenbaum and Maaten Van Steen. Papers: 1. Belady, Laszlo A. "A study of replacement algorithms for a virtual-storage computer." IBM Systems journal 5.2 (1966): 78-101. 2. Breslau, Lee, et al. "Web caching and Zipf-like distributions: Evidence and implications." INFOCOM'99. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE. Vol. 1. IEEE, 1999. 3. Cao, Pei, and Sandy Irani. "Cost-Aware WWW Proxy Caching Algorithms."Usenix symposium on internet technologies and systems. Vol. 12. No. 97. 1997. 4. Megiddo, Nimrod, and Dharmendra S. Modha. "ARC: A Self-Tuning, Low Overhead Replacement Cache." FAST. Vol. 3. No. 2003. 2003. 5. Beckmann, Nathan, Haoxian Chen, and Asaf Cidon. "LHD: Improving Cache Hit Rate by Maximizing Hit Density." 15th USENIX Symposium on Networked Systems Design and Implementation (NSDI 18). 2018 6. Vietri, Giuseppe, et al. "Driving cache replacement with ml-based lecar." 10th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage 18). 2018. 7. Nygren, Erik, Ramesh K. Sitaraman, and Jennifer Sun. "The akamai network: a platform for high-performance internet applications." ACM SIGOPS Operating Systems Review 44.3 (2010): 2-19. 8. Tang, Linpeng, et al. "Popularity prediction of facebook videos for higher quality streaming." 2017 USENIX Annual Technical Conference (USENIX ATC 17). 2017. 9. Hashemi, Milad, et al. "Learning Memory Access Patterns." International Conference on Machine Learning. 2018. 10. Friedman, Jerome H. "Greedy function approximation: a gradient boosting machine." Annals of statistics (2001): 1189-1232.