| Ioflow: A software-defined storage architecture E Thereska, H Ballani, G O'Shea, T Karagiannis, A Rowstron, T Talpey, ... Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems …, 2013 | 276 | 2013 |
| TetriSched: global rescheduling with adaptive plan-ahead in dynamic heterogeneous clusters A Tumanov, T Zhu, JW Park, MA Kozuch, M Harchol-Balter, GR Ganger Proceedings of the Eleventh European Conference on Computer Systems, 1-16, 2016 | 234 | 2016 |
| Prioritymeister: Tail latency qos for shared networked storage T Zhu, A Tumanov, MA Kozuch, M Harchol-Balter, GR Ganger Proceedings of the ACM Symposium on Cloud Computing, 1-14, 2014 | 130 | 2014 |
| {RobinHood}: Tail Latency Aware Caching--Dynamic Reallocation from {Cache-Rich} to {Cache-Poor} DS Berger, B Berg, T Zhu, S Sen, M Harchol-Balter 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2018 | 93 | 2018 |
| SNC-Meister: Admitting more tenants with tail latency SLOs T Zhu, DS Berger, M Harchol-Balter Proceedings of the Seventh ACM Symposium on Cloud Computing, 374-387, 2016 | 58 | 2016 |
| Saving cash by using less cache T Zhu, A Gandhi, M Harchol-Balter, MA Kozuch 4th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 12), 2012 | 51 | 2012 |
| WorkloadCompactor: Reducing datacenter cost while providing tail latency SLO guarantees T Zhu, MA Kozuch, M Harchol-Balter Proceedings of the 2017 Symposium on Cloud Computing, 598-610, 2017 | 50 | 2017 |
| Burscale: Using burstable instances for cost-effective autoscaling in the public cloud AF Baarzi, T Zhu, B Urgaonkar Proceedings of the ACM Symposium on Cloud Computing, 126-138, 2019 | 37 | 2019 |
| Softscale: stealing opportunistically for transient scaling A Gandhi, T Zhu, M Harchol-Balter, MA Kozuch Middleware 2012: ACM/IFIP/USENIX 13th International Middleware Conference …, 2012 | 30 | 2012 |
| tprof: Performance profiling via structural aggregation and automated analysis of distributed systems traces L Huang, T Zhu Proceedings of the ACM Symposium on Cloud Computing, 76-91, 2021 | 28 | 2021 |
| Peafowl: In-application cpu scheduling to reduce power consumption of in-memory key-value stores E Asyabi, A Bestavros, E Sharafzadeh, T Zhu Proceedings of the 11th ACM Symposium on Cloud Computing, 150-164, 2020 | 24 | 2020 |
| Metastable failures in the wild L Huang, M Magnusson, AB Muralikrishna, S Estyak, R Isaacs, A Aghayev, ... 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2022 | 22 | 2022 |
| The fast and the frugal: Tail latency aware provisioning for coping with load variations A Kumar, I Narayanan, T Zhu, A Sivasubramaniam Proceedings of The Web Conference 2020, 314-326, 2020 | 20 | 2020 |
| Metastable failures in distributed systems N Bronson, A Aghayev, A Charapko, T Zhu Proceedings of the Workshop on Hot Topics in Operating Systems, 221-227, 2021 | 16 | 2021 |
| Tetrisched: Space-time scheduling for heterogeneous datacenters A Tumanov, T Zhu, MA Kozuch, M Harchol-Balter, GR Ganger Tech. Rep., 2013 | 11 | 2013 |
| Overflowing emerging neural network inference tasks from the GPU to the CPU on heterogeneous servers A Kumar, A Sivasubramaniam, T Zhu Proceedings of the 15th ACM International Conference on Systems and Storage …, 2022 | 4 | 2022 |
| TraceSplitter: a new paradigm for downscaling traces. SM Sajal, R Hasan, T Zhu, B Urgaonkar, S Sen EuroSys, 606-619, 2021 | 3 | 2021 |
| SplitRPC: A {Control+ Data} Path Splitting RPC Stack for ML Inference Serving A Kumar, A Sivasubramaniam, T Zhu Proceedings of the ACM on Measurement and Analysis of Computing Systems 7 (2 …, 2023 | 2 | 2023 |
| Towards Cloud Efficiency with Large-scale Workload Characterization A Parayil, J Zhang, X Qin, Í Goiri, L Huang, T Zhu, C Bansal arXiv preprint arXiv:2405.07250, 2024 | 1 | 2024 |
| Kerveros: Efficient and Scalable Cloud Admission Control SM Sajal, L Marshall, B Li, S Zhou, A Pan, K Mellou, D Narayanan, T Zhu, ... 17th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2023 | 1 | 2023 |
