CATCloud: closing semantic gaps of CPU interfaces for precise autoscaling in the cloud

Precise CPU allocation for a multi-programmed computer is crucial to application performance and resource efficiency, but is notoriously difficult under dynamic cloud workloads, where multiple users executing diverse applications often share the CPUs. We argue that the fundamental problem is rooted in the mismatch of the existing CPU allocation interface between the cloud and the OS---while the cloud represents CPU resources as a percentage quota of the host CPU (i.e., millicpu), the OS interprets CPU resources as time-shared quota slices allowed to run within a defined period. The cloud interface's disregard for periodicity stems from the fundamental difficulty of capturing fine-grained application runtime behavior in userspace. Consequently, existing solutions rely on coarse-grained, surrogate metrics such as CPU utilization, throttle, and queue lengths, leading to slow and imprecise allocation. We present CATCloud, an OS extension that closes the semantic gap of cloud CPU allocation. CATCloud views CPU resources as a shared bandwidth interface and implements a millisecond-scale CPU bandwidth autotuner for quota and periodicity. Implemented in the OS scheduler, CATCloud realizes observability of fine-grained run time and yield time behavior of target applications; which was previously opaque to the userspace autoscalers. By continuously capturing historical data, it accurately estimates the short-term CPU period and quota requirements. With an execution latency of only a few milliseconds, CATCloud can quickly and effectively react to bursty, dynamic workloads with simple statistical algorithms. We show that CATCloud significantly outperforms state-of-the-art techniques in terms of responsiveness, precision, and efficiency. Our evaluation on various cloud workloads shows that CATCloud can improve CPU efficiency by on an average of 27.8%, up to 81.3% and performance improvements on average of 27.91%, up to 152.5% with negligible memory and compute overheads, over existing autoscaling solutions

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Sampat, Pratik Rajesh

Contributors dc:contributor

• Ghose, Saugata
• Xu, Tianyin

• Operating System
• Cpu Allocation
• Autoscaling
• Cloud Computing
• Resource Management

Statement dc:rights

• Copyright 2024 Pratik Rajesh Sampat

Language dc:language

eng, en

Handle dc:identifier

https://hdl.handle.net/2142/124165

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/124165