URSA: Precise Capacity Planning and Fair Scheduling based on Low-level Statistics for Public Clouds

Zhang, Wei; Zheng, Ningxin; Chen, Quan; Yang, Yong; Song, Zhuo; Ma, Tao; Leng, Jingwen; Guo, Minyi

doi:10.1145/3404397.3404451

Cited by 13 publications

(2 citation statements)

References 19 publications

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“…First, the inconsistency between the shared page table and the translation lookaside buffer (TLB) of the child process can lead to the data leakage problem. Second, we cannot accurately estimate the working set size, which indicates the memory usage of each process and is important for cloud resource management [57]. This is because the usage is calculated based on the states in the page table [21], while the table is shared by multiple processes.…”

Section: Impact Of Fork Operations On Latencymentioning

confidence: 99%

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Async-fork: Mitigating Query Latency Spikes Incurred by the Fork-based Snapshot Mechanism from the OS Level

Pang¹,

Deng²,

Bai³

et al. 2023

Preprint

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In-memory key-value stores (IMKVSes) serve many online applications because of their efficiency. To support data backup, popular industrial IMKVSes periodically take a point-in-time snapshot of the in-memory data with the system call fork. However, this mechanism can result in latency spikes for queries arriving during the snapshot period because fork leads the engine into the kernel mode in which the engine is out-of-service for queries. In contrast to existing research focusing on optimizing snapshot algorithms, we optimize the fork operation to address the latency spikes problem from the operating system (OS) level, while keeping the data persistent mechanism in IMKVSes unchanged. Specifically, we first conduct an in-depth study to reveal the impact of the fork operation as well as the optimization techniques on query latency. Based on findings in the study, we propose Async-fork to offload the work of copying the page table from the engine (the parent process) to the child process as copying the page table dominates the execution time of fork. To keep data consistent between the parent and the child, we design the proactive synchronization strategy. Async-fork is implemented in the Linux kernel and deployed into the online Redis database in public clouds. Our experiment results show that compared with the default fork method in OS, Async-fork reduces the tail latency of queries arriving during the snapshot period by 81.76% on an 8GB instance and 99.84% on a 64GB instance.

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Section: Impact Of Fork Operations On Latencymentioning

confidence: 99%

“…Consequently, the Linux kernel cannot give an accurate estimation on WSS. In practice, it is very important for public clouds to improve resource utilization because tenants generally would like to apply for more resources than their actual demands (e.g., 68.4% of memory space is wasted in our clouds [57]).…”

Section: A Issues Incurred By Shared Page Table Designmentioning

confidence: 99%