research-article

A virtual machine re-packing approach to the horizontal vs. vertical elasticity trade-off for cloud autoscaling

Authors:

Francisco Hernandez-Rodriguez,

Erik ElmrothAuthors Info & Claims

CAC '13: Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference

Article No.: 6, Pages 1 - 10

https://doi.org/10.1145/2494621.2494628

Published: 09 August 2013 Publication History

Abstract

An automated solution to horizontal vs. vertical elasticity problem is central to make cloud autoscalers truly autonomous. Today's cloud autoscalers are typically varying the capacity allocated by increasing and decreasing the number of virtual machines (VMs) of a predefined size (horizontal elasticity), not taking into account that as load varies it may be advantageous not only to vary the number but also the size of VMs (vertical elasticity). We analyze the price/performance effects achieved by different strategies for selecting VM-sizes for handling increasing load and we propose a cost-benefit based approach to determine when to (partly) replace a current set of VMs with a different set. We evaluate our repacking approach in combination with different auto-scaling strategies. Our results show a range of 7% up to 60% cost saving in total resource utilization cost of our sample applications and workloads.

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Yu XWang RHao JWu QYi CWang PNiyato D(2024)Priority-Aware Deployment of Autoscaling Service Function Chains Based on Deep Reinforcement LearningIEEE Transactions on Cognitive Communications and Networking10.1109/TCCN.2024.335856510:3(1050-1062)Online publication date: Jun-2024
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Index Terms

A virtual machine re-packing approach to the horizontal vs. vertical elasticity trade-off for cloud autoscaling

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Published In

cover image ACM Other conferences

CAC '13: Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference

August 2013

247 pages

ISBN:9781450321723

DOI:10.1145/2494621

General Chair:
Salim Hariri
University of Arizona
,
Program Chair:
Alan Sill
Texas Tech University

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

University of Arizona: University of Arizona
OGF: Open Grid Forum
Florida Intl University: Florida International Univeristy

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SIGARCH: ACM Special Interest Group on Computer Architecture

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 August 2013

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Seventh Framework Programme
OPTIMIS and Swedish Research Council (VR)

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CAC '13

Sponsor:

University of Arizona
OGF
Florida Intl University

CAC '13: ACM Cloud and Autonomic Computing Conference

August 5 - 9, 2013

Florida, Miami, USA

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Yu XWang RHao JWu QYi CWang PNiyato D(2024)Priority-Aware Deployment of Autoscaling Service Function Chains Based on Deep Reinforcement LearningIEEE Transactions on Cognitive Communications and Networking10.1109/TCCN.2024.335856510:3(1050-1062)Online publication date: Jun-2024
https://doi.org/10.1109/TCCN.2024.3358565
Chhetri MForkan AVo QNepal SKowalczyk R(2023)Towards Proactive Risk-Aware Cloud Cost Optimization Leveraging Transient ResourcesIEEE Transactions on Services Computing10.1109/TSC.2023.325347316:4(3014-3026)Online publication date: 1-Jul-2023
https://doi.org/10.1109/TSC.2023.3253473
Chen ZLi HOta KDong M(2023)HyScaler: A Dynamic, Hybrid VNF Scaling System for Building Elastic Service Function Chains Across Multiple ServersIEEE Transactions on Network and Service Management10.1109/TNSM.2023.327755220:4(4803-4814)Online publication date: Dec-2023
https://doi.org/10.1109/TNSM.2023.3277552
Tuli SCasale GJennings N(2023)CILP: Co-Simulation-Based Imitation Learner for Dynamic Resource Provisioning in Cloud Computing EnvironmentsIEEE Transactions on Network and Service Management10.1109/TNSM.2023.326825020:4(4448-4460)Online publication date: Dec-2023
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Ali KJammal M(2023)ML-Based Dynamic Scaling and Traffic Forecasting for 5G O-RAN2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)10.1109/DASC/PiCom/CBDCom/Cy59711.2023.10361376(0444-0451)Online publication date: 14-Nov-2023
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Zhao YUta A(2022)Tiny Autoscalers for Tiny Workloads: Dynamic CPU Allocation for Serverless Functions2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)10.1109/CCGrid54584.2022.00026(170-179)Online publication date: May-2022
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Karmakar KBanerjee SDas RKhatua S(2022)Utilization aware and network I/O intensive virtual machine placement policies for cloud data centerJournal of Network and Computer Applications10.1016/j.jnca.2022.103442205:COnline publication date: 1-Sep-2022
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Tavares WAssis MBorin EBrandic ISakellariou RSpillner J(2021)Leveraging vCPU-utilization rates to select cost-efficient VMs for parallel workloadsProceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing10.1145/3468737.3494095(1-10)Online publication date: 6-Dec-2021
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