Conference Proceedings
- Rubaba Hasan, Timothy Zhu and Bhuvan Urgaonkar, 2024, "AutoBurst: Autoscaling Burstable Instances for Cost-effective Latency SLOs", ACM, New York, NY, USA
- Vishwas Vasudeva Kakrannaya, Siddhartha Balakrishna Rai, Anand Sivasubramaniam and Timothy Zhu, 2024, "Fast and Accurate DNN Performance Estimation across Diverse Hardware Platforms", IEEE
- Sultan Mahmud Sajal, Timothy Zhu, Bhuvan Urgaonkar and Siddhartha Sen, 2024, "TraceUpscaler: Upscaling Traces to Evaluate Systems at High Load", ACM, New York, NY, USA
- Sultan Mahmud Sajal, Luke Marshall, Beibin Li, Shandan Zhou, Abhisek Pan, Konstantina Mellou, Deepak Narayanan, Timothy Zhu, David Dion, Thomas Moscibroda and Ishai Menache, 2023, "Kerveros: Efficient and Scalable Cloud Admission Control", USENIX, USA
- Adithya Kumar, Anand Sivasubramaniam and Timothy Zhu, 2023, "SplitRPC: A {Control + Data} Path Splitting RPC Stack for ML Inference Serving", ACM, New York, NY, USA
- Lexiang Huang, Matthew Magnusson, Abishek Bangalore Muralikrishna, Salman Estyak, Rebecca Isaacs, Abutalib Aghayev, Timothy Zhu and Aleksey Charapko, 2022, "Metastable Failures in the Wild", USENIX, USA
- Adithya Kumar, Anand Sivasubramaniam and Timothy Zhu, 2022, "Overflowing Emerging Neural Network Inference Tasks from the GPU to the CPU on Heterogeneous Servers", ACM, New York, NY, USA
- Lexiang Huang and Timothy Zhu, 2021, "tprof: Performance profiling via structural aggregation and automated analysis of distributed systems traces", ACM, New York, NY, USA
- Nathan Bronson, Abutalib Aghayev, Aleksey Charapko and Timothy Zhu, 2021, "Metastable Failures in Distributed Systems", ACM, New York, NY, USA, pp. 221–227
- Sultan Mahmud Sajal, Rubaba Hasan, Timothy Zhu, Bhuvan Urgaonkar and Siddhartha Sen, 2021, "TraceSplitter: A New Paradigm for Downscaling Traces", ACM, New York, NY, USA
- Esmail Asyabi, Azer Bestavros, Erfan Sharafzadeh and Timothy Zhu, 2020, "Peafowl: In-application CPU scheduling to reduce power consumption of in-memory key-value stores", ACM, New York, NY, USA
- Adithya Kumar, Iyswarya Narayanan, Timothy Zhu and Anand Sivasubramaniam, 2020, "The Fast and The Frugal: Tail Latency Aware Provisioning for Coping with Load Variations", ACM, New York, NY, USA
- Ataollah Fatahi Baarzi, Timothy Zhu and Bhuvan Urgaonkar, 2019, "BurScale: Using Burstable Instances for Improving the Cost-Efficacy of Autoscaling in the Public Cloud", ACM, New York, NY, USA, pp. 126–138
- Daniel S. Berger, Benjamin Berg, Timothy Zhu, Siddhartha Sen and Mor Harchol-Balter, 2018, "RobinHood: Tail Latency Aware Caching - Dynamic Reallocation from Cache-Rich to Cache-Poor", USENIX, USA, pp. 195--212
- Timothy Zhu, Michael A. Kozuch and Mor Harchol-Balter, 2017, "WorkloadCompactor: Reducing Datacenter Cost While Providing Tail Latency SLO Guarantees", ACM, New York, NY, USA, pp. 598–610
- Timothy Zhu, Daniel S. Berger and Mor Harchol-Balter, 2016, "SNC-Meister: Admitting More Tenants with Tail Latency SLOs", ACM, New York, NY, USA, pp. 374–387
- Alexey Tumanov, Timothy Zhu, Jun Woo Park, Michael A. Kozuch, Mor Harchol-Balter and Gregory R. Ganger, 2016, "TetriSched: Global Rescheduling with Adaptive Plan-ahead in Dynamic Heterogeneous Clusters", ACM, New York, NY, USA, pp. 35:1–35:16
- Timothy Zhu, Alexey Tumanov, Michael A. Kozuch, Mor Harchol-Balter and Gregory R. Ganger, 2014, "PriorityMeister: Tail Latency QoS for Shared Networked Storage", ACM, New York, NY, USA, pp. 29:1–29:14
- Eno Thereska, Hitesh Ballani, Greg O’Shea, Thomas Karagiannis, Antony Rowstron, Tom Talpey, Richard Black and Timothy Zhu, 2013, "IOFlow: A Software-defined Storage Architecture", ACM, New York, NY, USA, pp. 182–196
- Anshul Gandhi, Timothy Zhu, Mor Harchol-Balter and Michael A. Kozuch, 2012, "SOFTScale: Stealing Opportunistically for Transient Scaling", Springer-Verlag New York, Inc., New York, NY, USA, pp. 142–163
- Timothy Zhu, Anshul Gandhi, Mor Harchol-Balter and Michael A. Kozuch, 2012, "Saving Cash by Using Less Cache", USENIX Association, Berkeley, CA, USA, pp. 3–3
