More Web Proxy on the site http://driver.im/

research-article

Automated control in cloud computing: challenges and opportunities

Authors:

Jeffrey S. Chase,

Sujay S. ParekhAuthors Info & Claims

ACDC '09: Proceedings of the 1st workshop on Automated control for datacenters and clouds

Pages 13 - 18

https://doi.org/10.1145/1555271.1555275

Published: 19 June 2009 Publication History

Abstract

With advances in virtualization technology, virtual machine services offered by cloud utility providers are becoming increasingly powerful, anchoring the ecosystem of cloud services. Virtual computing services are attractive in part because they enable customers to acquire and release computing resources for guest applications adaptively in response to load surges and other dynamic behaviors. ``Elastic'' cloud computing APIs present a natural opportunity for feedback controllers to automate this adaptive resource provisioning, and many recent works have explored feedback control policies for a variety of network services under various assumptions.

This paper addresses the challenge of building an effective controller as a customer add-on outside of the cloud utility service itself. Such external controllers must function within the constraints of the utility service APIs. It is important to consider techniques for effective feedback control using cloud APIs, as well as how to design those APIs to enable more effective control. As one example, we explore proportional thresholding, a policy enhancement for feedback controllers that enables stable control across a wide range of guest cluster sizes using the coarse-grained control offered by popular virtual compute cloud services.

References

[1]

Amazon Elastic Compute Cloud (EC2). http://aws.amazon.com/ec2/.

[2]

Apache Tomcat. http://tomcat.apache.org/.

[3]

Aptana Cloud. http://aptana.com/cloud/.

[4]

Eucalyptus. http://eucalyptus.cs.ucsb.edu/.

[5]

Hyperic HQ Open Source Web Infrastructure Management Software. http://www.hyperic.com/.

[6]

Joyent. http://www.joyent.com/.

[7]

Open Resource Control Architecture (ORCA). http://www.nicl.cod.cs.duke.edu/orca/.

[8]

VMware: Virtualization via Hypervisor, Virtual Machine & Server Consolidation. http://www.vmware.com/.

[9]

P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt, and A. Warfield. Xen and the art of virtualization. In Proc. of SOSP, 2003.

Digital Library

[10]

J. L. Hellerstein, Y. Diao, S. Parekh, and D. M. Tilbury. Feedback Control of Computing Systems. John Wiley & Sons, 2004.

Digital Library

[11]

C. Karamanolis, M. Karlsson, and X. Zhu. Designing controllable computer systems. In Proc. of HOTOS, 2005.

Digital Library

[12]

M. Karlsson, C. Karamanolis, and X. Zhu. An adaptive optimal controller for non-intrusive performance differentiation in computing services. In Proc. of ICCA, 2005.

[13]

Y. Lu, T. Abdelzaher, and G. Tao. Direct adaptive control of a web cache system. In Proc. of American Control Conference, 2003.

[14]

P. Padala, K. G. Shin, X. Zhu, M. Uysal, Z. Wang, S. Singhal, A. Merchant, and K. Salem. Adaptive control of virtualized resources in utility computing environments. In Proc. of EuroSys, 2007.

Digital Library

[15]

S. Parekh, N. Gandhi, J. Hellerstein, D. Tilbury, T. Jayram, and J. Bigus. Using control theory to achieve service level objectives in performance management. In Proc. of IM, 2002.

[16]

G. Soundararajan, C. Amza, and A. Goel. Database replication policies for dynamic content applications. In Proc. of EuroSys, 2006.

Digital Library

[17]

B. Urgaonkar, P. Shenoy, A. Chandra, and P. Goyal. Dynamic provisioning of multi-tier internet applications. In Proc. of ICAC, 2005.

Digital Library

[18]

A. Yumerefendi, P. Shivam, D. Irwin, P. Gunda, L. Grit, A. Demberel, J. Chase, and S. Babu. Towards an autonomic computing testbed. In Proc. of HotAC, 2007.

Digital Library

Cited By

Shi YLiu W(2024)Transparency of Task Dependencies of Reinforcement Learning in Unmanned Systems2024 IEEE International Conference on Industrial Technology (ICIT)10.1109/ICIT58233.2024.10540806(1-8)Online publication date: 25-Mar-2024
https://doi.org/10.1109/ICIT58233.2024.10540806
Jananee MNimala K(2023)Allocation of cloud resources based on prediction and performing auto-scaling of workload2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)10.1109/ICECONF57129.2023.10083865(1-7)Online publication date: 5-Jan-2023
https://doi.org/10.1109/ICECONF57129.2023.10083865
Yadav MAkarte HYadav D(2022)Container Elasticity: Based on Response Time using DockerRecent Advances in Computer Science and Communications10.2174/266625581399920101219201015:5Online publication date: Jun-2022
https://doi.org/10.2174/2666255813999201012192010
Show More Cited By

Index Terms

Automated control in cloud computing: challenges and opportunities

Recommendations

State-of-the-art research study for green cloud computing

Although cloud computing has rapidly emerged as a widely accepted computing paradigm, the research on cloud computing is still at an early stage. Cloud computing suffers from different challenging issues related to security, software frameworks, quality ...
A Cloud Computing Security Framework Based on Cloud Security Trusted Authority
INFOS '16: Proceedings of the 10th International Conference on Informatics and Systems

Cloud computing is a promising technology that provides dynamic resource allocation, power saving, and low running cost. On the other hand, security risks of cloud computing represent a major concern that slows down its market growth. There are many ...
A Conceptual Platform of SLA in Cloud Computing
DASC '11: Proceedings of the 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing

Cloud computing is a promising technology, where the infrastructure, developing platform, software and storage are delivered as a service. With the development of cloud computing, more and more cloud service providers emerge. However, there are no ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ACDC '09: Proceedings of the 1st workshop on Automated control for datacenters and clouds

June 2009

64 pages

ISBN:9781605585857

DOI:10.1145/1555271

Program Chairs:
Jeff Chase
Duke University
,
Emre Kıcıman
Microsoft Research
,
Rich Wolski
UC Santa Barbara

Copyright © 2009 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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 June 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ICAC '09

Sponsor:

ICAC '09: 6th International Conference on Autonomic Computing

June 19, 2009

Barcelona, Spain

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

207
Total Citations
View Citations
4,661
Total Downloads

Downloads (Last 12 months)73
Downloads (Last 6 weeks)9

Reflects downloads up to 19 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Shi YLiu W(2024)Transparency of Task Dependencies of Reinforcement Learning in Unmanned Systems2024 IEEE International Conference on Industrial Technology (ICIT)10.1109/ICIT58233.2024.10540806(1-8)Online publication date: 25-Mar-2024
https://doi.org/10.1109/ICIT58233.2024.10540806
Jananee MNimala K(2023)Allocation of cloud resources based on prediction and performing auto-scaling of workload2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF)10.1109/ICECONF57129.2023.10083865(1-7)Online publication date: 5-Jan-2023
https://doi.org/10.1109/ICECONF57129.2023.10083865
Yadav MAkarte HYadav D(2022)Container Elasticity: Based on Response Time using DockerRecent Advances in Computer Science and Communications10.2174/266625581399920101219201015:5Online publication date: Jun-2022
https://doi.org/10.2174/2666255813999201012192010
Rodrigues VRighi RCosta CAntunes RBazo RReis ESeewald LJunior LEskofier B(2022)HealthStack: Providing an IoT Middleware for Malleable QoS Service Stacking for Hospital 4.0 Operating RoomsIEEE Internet of Things Journal10.1109/JIOT.2022.31606339:19(18406-18430)Online publication date: 1-Oct-2022
https://doi.org/10.1109/JIOT.2022.3160633
Malik MRecker S(2022)Analysis of Dynamic Resource Allocation in Digital Education Ecosystems2022 IEEE European Technology and Engineering Management Summit (E-TEMS)10.1109/E-TEMS53558.2022.9944430(136-141)Online publication date: 9-Mar-2022
https://doi.org/10.1109/E-TEMS53558.2022.9944430
Tsagkaropoulos AVerginadis YPapageorgiou NParaskevopoulos FApostolou DMentzas G(2021)Severity: a QoS-aware approach to cloud application elasticityJournal of Cloud Computing10.1186/s13677-021-00255-510:1Online publication date: 21-Aug-2021
https://doi.org/10.1186/s13677-021-00255-5
Yadav MPal NYadav D(2021)Workload Prediction over Cloud Server using Time Series Data2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/Confluence51648.2021.9377032(267-272)Online publication date: 28-Jan-2021
https://doi.org/10.1109/Confluence51648.2021.9377032
Hakimzadehy KNicholsonz PLugonesz DPayberahy A(2021)IMITA: Imitation Learning for Generalizing Cloud Orchestration2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)10.1109/CCGrid51090.2021.00033(237-246)Online publication date: May-2021
https://doi.org/10.1109/CCGrid51090.2021.00033
Yadav MRohit Yadav D(2021)Resource Provisioning Through Machine Learning in Cloud ServicesArabian Journal for Science and Engineering10.1007/s13369-021-05864-5Online publication date: 24-Jul-2021
https://doi.org/10.1007/s13369-021-05864-5
Yadav MYadav D(2021)Workload Prediction for Cloud Resource Provisioning using Time Series DataSoft Computing for Problem Solving10.1007/978-981-16-2712-5_37(447-459)Online publication date: 14-Oct-2021
https://doi.org/10.1007/978-981-16-2712-5_37
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents