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Scavenger: A Black-Box Batch Workload Resource Manager for Improving Utilization in Cloud Environments

Authors:

Seyyed Ahmad Javadi,

Amoghavarsha Suresh,

Muhammad Wajahat,

Anshul GandhiAuthors Info & Claims

SoCC '19: Proceedings of the ACM Symposium on Cloud Computing

Pages 272 - 285

https://doi.org/10.1145/3357223.3362734

Published: 20 November 2019 Publication History

Abstract

Resource under-utilization is common in cloud data centers. Prior works have proposed improving utilization by running provider workloads in the background, colocated with tenant workloads. However, an important challenge that has still not been addressed is considering the tenant workloads as a black-box. We present Scavenger, a batch workload manager that opportunistically runs containerized batch jobs next to black-box tenant VMs to improve utilization. Scavenger is designed to work without requiring any offline profiling or prior information about the tenant workload. To meet the tenant VMs' resource demand at all times, Scavenger dynamically regulates the resource usage of batch jobs, including processor usage, memory capacity, and network bandwidth. We experimentally evaluate Scavenger on two different testbeds using latency-sensitive tenant workloads colocated with Spark jobs in the background and show that Scavenger significantly increases resource usage without compromising the resource demands of tenant VMs.

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Scavenger: A Black-Box Batch Workload Resource Manager for Improving Utilization in Cloud Environments

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cover image ACM Conferences

SoCC '19: Proceedings of the ACM Symposium on Cloud Computing

November 2019

503 pages

ISBN:9781450369732

DOI:10.1145/3357223

Copyright © 2019 ACM.

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Published: 20 November 2019

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November 20 - 23, 2019

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SoCC '19 Paper Acceptance Rate 39 of 157 submissions, 25%;

Overall Acceptance Rate 169 of 722 submissions, 23%

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https://doi.org/10.3390/math12121845
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Yang DXiao ZZhang DZhang SCao JChen G(2024)PREACT: Predictive Resource Allocation for Bursty Workloads in a Co-located Data CenterProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673135(722-731)Online publication date: 12-Aug-2024
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