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Application-Agnostic Batch Workload Management in Cloud Environments

Authors:

Seyyed Ahmad Javadi,

Shalini Bhaskara,

Rahul Doshi,

Prashanth Soundarapandian,

Muhammad Wajahat,

Anshul GandhiAuthors Info & Claims

SoCC '18: Proceedings of the ACM Symposium on Cloud Computing

Page 504

https://doi.org/10.1145/3267809.3275446

Published: 11 October 2018 Publication History

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Abstract

We present Scavenger, a reactive batch workload manager that opportunistically runs containerized batch jobs next to customer Virtual Machines (VMs) in a public cloud like setting to improve utilization. Scavenger dynamically regulates the resource usage of batch jobs, including CPU usage, memory capacity, and LLC capacity, to ensure that the customer VMs' resource demand is met at all times. We experimentally evaluate Scavenger and show that it considerably increases resource usage without compromising on the resource demand of customer VMs. Importantly, Scavenger does so without requiring any offline profiling or prior information about the customer workloads.

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SoCC '18: Proceedings of the ACM Symposium on Cloud Computing

October 2018

546 pages

ISBN:9781450360111

DOI:10.1145/3267809

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 11 October 2018

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SoCC '18: ACM Symposium on Cloud Computing

October 11 - 13, 2018

CA, Carlsbad, USA

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Batch scheduling of consolidated virtual machines based on their workload interference model

Understanding Performance Interference of I/O Workload in Virtualized Cloud Environments

Transparently bridging semantic gap in CPU management for virtualized environments

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