Caliper

Author:

Kannan Ram Srivatsa1,Laurenzano Michael1,Ahn Jeongseob2,Mars Jason1,Tang Lingjia1

Affiliation:

1. University of Michigan, Ann Arbor, Michigan, USA

2. Ajou University, Yeongton-gu Suwon, South Korea

Abstract

We introduce Caliper , a technique for accurately estimating performance interference occurring in shared servers. Caliper overcomes the limitations of prior approaches by leveraging a micro-experiment-based technique. In contrast to state-of-the-art approaches that focus on periodically pausing co-running applications to estimate slowdown, Caliper utilizes a strategic phase-triggered technique to capture interference due to co-location. This enables Caliper to orchestrate an accurate and low-overhead interference estimation technique that can be readily deployed in existing production systems. We evaluate Caliper for a broad spectrum of workload scenarios, demonstrating its ability to seamlessly support up to 16 applications running simultaneously and outperform the state-of-the-art approaches.

Funder

National Science Foundation

National Research Foundation of Korea

Korea government

Publisher

Association for Computing Machinery (ACM)

Subject

Hardware and Architecture,Information Systems,Software

Reference71 articles.

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Co-Approximator: Enabling Performance Prediction in Colocated Applications.;ACM Transactions on Embedded Computing Systems;2024-07-25

2. FEDGE: An Interference-Aware QoS Prediction Framework for Black-Box Scenario in IaaS Clouds with Domain Generalization;2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2024-05-27

3. gPerfIsol: GNN-Based Rate-Limits Allocation for Performance Isolation in Multi-Tenant Cloud;2024 27th Conference on Innovation in Clouds, Internet and Networks (ICIN);2024-03-11

4. Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public Cloud;2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2023-05

5. Adrias: Interference-Aware Memory Orchestration for Disaggregated Cloud Infrastructures;2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA);2023-02

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