@inproceedings{34bad9839a3649db87c822b28b858438,
title = "TailX: Scheduling Heterogeneous Multiget Queries to Improve Tail Latencies in Key-Value Stores",
abstract = "Users of interactive services such as e-commerce platforms have high expectations for the performance and responsiveness of these services. Tail latency, denoting the worst service times, contributes greatly to user dissatisfaction and should be minimized. Maintaining low tail latency for interactive services is challenging because a request is not complete until all its operations are completed. The challenge is to identify bottleneck operations and schedule them on uncoordinated backend servers with minimal overhead, when the duration of these operations are heterogeneous and unpredictable. In this paper, we focus on improving the latency of multiget operations in cloud data stores. We present TailX, a task-aware multiget scheduling algorithm that improves tail latencies under heterogeneous workloads. TailX schedules operations according to an estimation of the size of the corresponding data, and allows itself to procrastinate some operations to give way to higher priority ones. We implement TailX in Cassandra, a widely used key-value store. The result is an improved overall performance of the cloud data stores for a wide variety of heterogeneous workloads. Specifically, our experiments under heterogeneous YCSB workloads show that TailX outperforms state-of- the-art solutions and reduces tail latencies by up to 70% and median latencies by up to 75%.",
author = "Vikas Jaiman and Mokhtar, {Sonia Ben} and Etienne Rivi{\`e}re",
note = "Funding Information: Experiments presented in this paper were carried out using the Grid{\textquoteright}5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations. This work was partially supported by the European Union{\textquoteright}s Horizon 2020 research and innovation programme under grant agreement No 692178 (EBSIS project), by CHIST-ERA under project DIONASYS, and by the Swiss National Science Foundation (SNSF) under grant 155249. Funding Information: Acknowledgments. Experiments presented in this paper were carried out using the Grid{\textquoteright}5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations. This work was partially supported by the European Union{\textquoteright}s Horizon 2020 research and innovation programme under grant agreement No 692178 (EBSIS project), by CHIST-ERA under project DIONASYS, and by the Swiss National Science Foundation (SNSF) under grant 155249. Publisher Copyright: {\textcopyright} IFIP International Federation for Information Processing 2020.",
year = "2020",
month = jun,
day = "8",
doi = "10.1007/978-3-030-50323-9_5",
language = "English",
isbn = "978-3-030-50322-2",
series = "Lecture Notes in Computer Science",
publisher = "Springer, Cham",
pages = "73--92",
editor = "A. Remke and V. Schiavoni",
booktitle = "Distributed Applications and Interoperable Systems. DAIS 2020",
address = "Switzerland",
}