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Cost-Aware Cloud Bursting for Enterprise Applications

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Sambit SahuAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 13, Issue 3

Article No.: 10, Pages 1 - 24

https://doi.org/10.1145/2602571

Published: 01 May 2014 Publication History

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Abstract

The high cost of provisioning resources to meet peak application demands has led to the widespread adoption of pay-as-you-go cloud computing services to handle workload fluctuations. Some enterprises with existing IT infrastructure employ a hybrid cloud model where the enterprise uses its own private resources for the majority of its computing, but then “bursts” into the cloud when local resources are insufficient. However, current commercial tools rely heavily on the system administrator’s knowledge to answer key questions such as when a cloud burst is needed and which applications must be moved to the cloud. In this article, we describe Seagull, a system designed to facilitate cloud bursting by determining which applications should be transitioned into the cloud and automating the movement process at the proper time. Seagull optimizes the bursting of applications using an optimization algorithm as well as a more efficient but approximate greedy heuristic. Seagull also optimizes the overhead of deploying applications into the cloud using an intelligent precopying mechanism that proactively replicates virtualized applications, lowering the bursting time from hours to minutes. Our evaluation shows over 100% improvement compared to naïve solutions but produces more expensive solutions compared to ILP. However, the scalability of our greedy algorithm is dramatically better as the number of VMs increase. Our evaluation illustrates scenarios where our prototype can reduce cloud costs by more than 45% when bursting to the cloud, and that the incremental cost added by precopying applications is offset by a burst time reduction of nearly 95%.

References

[1]

M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. H. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia. 2009. Above the couds: A Berkeley view of cloud computing. Technical Report UCB/EECS-2009-28. EECS Department, University of California, Berkeley. http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.html.

Google Scholar

[2]

AWSECO 2013. AWS economics center. http://aws.amazon.com/economics/.

Google Scholar

[3]

H. Ballani, P. Costa, T. Karagiannis, and A. Rowstron. 2011. Towards predictable datacenter networks. In Proceedings of the ACM SIGCOMM Conference (SIGCOMM’11). ACM, 242--253.

Digital Library

Google Scholar

[4]

T. Bicer, D. Chiu, and G. Agrawal. 2011. A framework for data-intensive computing with cloud bursting. In Proceedings of the IEEE International Conference on Cluster Computing (CLUSTER). 169--177.

Digital Library

Google Scholar

[5]

R. Bradford, E. Kotsovinos, A. Feldmann, and H. Schiöberg. 2007. Live wide-area migration of virtual machines including local persistent state. In Proceedings of VEE. ACM, 169--179.

Digital Library

Google Scholar

[6]

R. Buyya, R. Ranjan, and R. N. Calheiros. 2010. InterCloud: Utility-oriented federation of cloud computing environments for scaling of application services. In Proceedings of the International Conference on Algorithms and Architectures for Parallel Processing.

Digital Library

Google Scholar

[7]

E. Cecchet, V. Udayabhanu, T. Wood, and P. Shenoy. 2011. BenchLab: An open testbed for realistic benchmarking of web applications. In Proceedings of the 2nd USENIX Conference on Web Application Development (WebApps).

Digital Library

Google Scholar

[8]

C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, and A. Warfield. 2005. Live migration of virtual machines. In Proceedings of NSDI.

Digital Library

Google Scholar

[9]

Cloudbursting 2008. Cloudbursting - Hybrid Application Hosting. http://aws.typepad.com/aws/2008/08/cloudbursting-.html. (Last accessed 8/08).

Google Scholar

[10]

E. G. Coffman, Jr., M. R. Garey, and D. S. Johnson. 1997. Approximation algorithms for bin packing: A survey. In Approximation Algorithms for NP-Hard Problems.

Digital Library

Google Scholar

[11]

K. A. Dowsland and W. B. Dowsland. 1992. Packing problems. Europ. J. Oper. Res. 56, 1, 2--14.

Crossref

Google Scholar

[12]

A. Gulati, G. Shanmuganathan, I. Ahmad, C. Waldspurger, and M. Uysal. 2011. Pesto: Online storage performance management in virtualized datacenters. In Proceedings of SOCC (SOCC’11). ACM, Article 19.

Digital Library

Google Scholar

[13]

C. Guo, G. Lu, H. J. Wang, S. Yang, C. Kong, P. Sun, W. Wu, and Y. Zhang. 2010. SecondNet: A data center network virtualization architecture with bandwidth guarantees. In Proceedings of the 6th International Conference (Co-NEXT’10). ACM, Article 15.

Digital Library

Google Scholar

[14]

J. Hellerstein, F. Zhang, and P. Shahabuddin. 1999. An approach to predictive detection for service management. In Proceedings of the IEEE International Conference on Systems and Network Management.

Google Scholar

[15]

S. Kailasam, N. Gnanasambandam, J. Dharanipragada, and N. Sharma. 2010. Optimizing service level agreements for autonomic cloud bursting schedulers. In Proceedings of the 39th International Conference on Parallel Processing Workshops (ICPPW ’10). IEEE Computer Society, 285--294.

Digital Library

Google Scholar

[16]

H. Kim, M. Parashar, D. J. Foran, and L. Yang. 2009. Investigating the use of autonomic cloudbursts for high-throughput medical image registration. In Proceedings of GRID. IEEE, 34--41.

Digital Library

Google Scholar

[17]

G. Lee, N. Tolia, P. Ranganathan, and R. H. Katz. 2010. Topology-aware resource allocation for data-intensive workloads. In Proceedings of the 1st ACM Asia-Pacific Workshop on Systems (APSys’10). ACM, 1--6.

Digital Library

Google Scholar

[18]

A. Mashtizadeh, E. Celebi, T. Garfinkel, and M. Cai. 2011. The design and evolution of live storage migration in VMware ESX. In Proceedings of USENIX ATC. 14--14. http://dl.acm.org/citation.cfm?id=2002181.2002195.

Digital Library

Google Scholar

[19]

G. Mateescu, W. Gentzsch, and C. J. Ribbens. 2011. Hybrid computing-where HPC meets grid and cloud computing. Future Gener. Comput. Syst. 27, 5, 440--453.

Digital Library

Google Scholar

[20]

M. Mishra and A. Sahoo. 2011. On theory of VM placement: Anomalies in existing methodologies and their mitigation using a novel vector based approach. In Proceedings of the IEEE International Conference on Cloud Computing (CLOUD), 275--282.

Digital Library

Google Scholar

[21]

K. Nagin, D. Hadas, Z. Dubitzky, A. Glikson, I. Loy, B. Rochwerger, and L. Schour. 2011. Inter-cloud mobility of virtual machines. In Proceedings of the Annual International Conference on Systems and Storage (SYSTOR’11). ACM, Article 3.

Digital Library

Google Scholar

[22]

M. Nelson, B.-H. Lim, and G. Hutchins. 2005. Fast transparent migration for virtual machines. In Proceedings of ATEC ’05: USENIX ATC. USENIX Association, Berkeley, CA, 25.

Digital Library

Google Scholar

[23]

ObjectWeb. The ObjectWeb TPC-W implementation. Website. http://jmob.objectweb.org/tpcw.html.

Google Scholar

[24]

OpenNebula 2012. The Open Source Toolkit for Data Center Virtualization. (2012). http://www.opennebula.org/.

Google Scholar

[25]

Openstack 2012. openstack: Cloud Software. http://www.openstack.org/.

Google Scholar

[26]

A. Rai, R. Bhagwan, and S. Guha. 2012. Generalized resource allocation for the cloud. In Proceedings of the 3rd ACM Symposium on Cloud Computing (SoCC’12). ACM, Article 15, 12 pages.

Digital Library

Google Scholar

[27]

S. Ranjan, J. Rolia, H. Fu, and E. Knightly. 2002. QoS-driven server migration for Internet data centers. In Proceedings of IWQoS. 3--12.

Google Scholar

[28]

B. Rochwerger, D. Breitgand, A. Epstein, et al. 2011. Reservoir - When one cloud is not enough. Computer 44, 44--51.

Digital Library

Google Scholar

[29]

U. Sharma, P. Shenoy, S. Sahu, and A. Shaikh. 2011. Kingfisher: Cost-aware elasticity in the cloud. In Proceedings of INFOCOM 2011. 206--210.

Crossref

Google Scholar

[30]

Z. Shen, S. Subbiah, X. Gu, and J. Wilkes. 2011. CloudScale: Elastic resource scaling for multi-tenant cloud systems. In Proceedings of (SOCC’11). ACM, Article 5, 14 pages.

Digital Library

Google Scholar

[31]

P. Shivam, A. Iamnitchi, A. R. Yumerefendi, and J. S. Chase. 2005. Model-driven placement of compute tasks and data in a networked utility. In Proceedings of ICAC.

Digital Library

Google Scholar

[32]

W. Sobel, S. Subramanyam, A. Sucharitakul, J. Nguyen, H. Wong, S. Patil, A. Fox, and D. Patterson. 2008. Cloudstone: Multi-platform, multi-language benchmark and measurement tools for web 2.0. In Proceedings of Cloud Computing and its Applications.

Google Scholar

[33]

B. Sotomayor, R. S. Montero, I. M. Llorente, and I. Foster. 2009. Virtual infrastructure management in private and hybrid clouds. Internet Comput. 13, 5, 14--22.

Digital Library

Google Scholar

[34]

Terremark 2012. Study: USA.gov Achieves cloud bursting efficiency using terremark enterprise cloud. http://terremark.com.

Google Scholar

[35]

B. Urgaonkar, G. Pacifici, P. Shenoy, M. Spreitzer, and A. Tantawi. 2005. An analytical model for multi-tier internet services and its applications. In Proceedings of the ACM Sigmetrics Conference.

Digital Library

Google Scholar

[36]

B. Urgaonkar, P. Shenoy, A. Chandra, P. Goyal, and T. Wood. 2008. Agile dynamic provisioning of multi-tier Internet applications. ACM Trans. Auton. Adapt. Syst. 3, Article 1.

Digital Library

Google Scholar

[37]

VDataCenter 2012. VMware: Public & hybrid cloud computing. http://www.vmware.com/solutions/cloud-computing/public-cloud/products.html.

Google Scholar

[38]

VMotion 2009. Virtual machine mobility with VMware VMotion and Cisco data center Interconnect technologies. http://www.cisco.com/en/US/solutions/collateral/ns340/ns517/ns224/ns836/white_paper_c11-557822.pdf.

Google Scholar

[39]

VMware DRS. Resource management with VMware DRS. http://www.vmware.com/pdf/vmware_drs_wp.pdf.

Google Scholar

[40]

D. Williams, H. Jamjoom, and H. Weatherspoon. 2012. The Xen-Blanket: Virtualize once, run everywhere. In Proceedings of the 7th ACM European Conference on Computer Systems (EuroSys’12). 113--126.

Digital Library

Google Scholar

[41]

T. Wood, K. K. Ramakrishnan, P. Shenoy, and J. Van der Merwe. 2011. CloudNet: Dynamic pooling of cloud resources by live WAN migration of virtual machines. In Proceedings of VEE. 121--132.

Digital Library

Google Scholar

[42]

T. Wood, P. Shenoy, A. Venkataramani, and M. Yousif. 2009. Sandpiper: Black-box and gray-box resource management for virtual machines. Comput. Netw. The Int. J. Comput. Telecom. Netw. 53, 17. http://portal.acm.org/citation.cfm?id=1663647.1663710.

Digital Library

Google Scholar

[43]

Z. Xiao, W. Song, and Q. Chen. 2013. Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans. Parall. Distrib. Syst. 24, 6, 1107--1117.

Digital Library

Google Scholar

[44]

Y. Zhang, V. Paxson, and S. Shenkar. 2000. The stationarity of Internet path properties: Routing, loss, and throughput. Technical Report. AT&T Center for Internet Research at ICSI, http://www.aciri.org/.

Google Scholar

[45]

J. Zheng, T. E. Ng, and K. Sripanidkulchai. 2011. Workload-aware live storage migration for clouds. In Proceeding of VEE (VEE’11). ACM, 133--144.

Digital Library

Google Scholar

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Atif YMathew S(2025)IoT inter-cloud interoperability for sustainable digital transformationAccelerating Digital Transformation with the Cloud and the Internet of Things (IoT)10.1016/B978-0-44-322217-7.00012-8(177-198)Online publication date: 2025
https://doi.org/10.1016/B978-0-44-322217-7.00012-8
Ahmad FRangappa MKatiyar NStaniszewski MVarró DLiu AMuccini H(2023)Hybrid Cloudification of Legacy Software for Efficient Simulation of Gas Turbine DesignsProceedings of the 45th International Conference on Software Engineering: Software Engineering in Practice10.1109/ICSE-SEIP58684.2023.00041(384-395)Online publication date: 17-May-2023
https://dl.acm.org/doi/10.1109/ICSE-SEIP58684.2023.00041
Baloni DPant SKumar MRawat GRawat SKanti P(2022)Cloud Bursting: Intelligent Technique in Cloud Computing2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)10.1109/SMART55829.2022.10047386(3-6)Online publication date: 16-Dec-2022
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Index Terms

Cost-Aware Cloud Bursting for Enterprise Applications
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles

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Reviews

Reviewer: Jill Gemmill

Seagull, a system to optimally manage cloud bursting, is described in this paper; optimization is with respect to the number and size of virtual machine (VM) images and total data transfer. A performance evaluation of Seagull is also provided. The authors address an important research topic in cloud computing, exploring how to manage applications with varying resource requirements in an automated manner that also minimizes costs in terms of both dollars and application performance. Seagull is designed for use by enterprise data centers that require bursting into cloud resources for occasional excess capacity demands. The paper offers a valuable contribution to a difficult and reality-based problem. The authors use some insight in designing their approach: (1) When an application that is large (lots of data, many VMs) requires more resources than are available locally, it may be better to move some other, smaller apps into the cloud, freeing up local resources. (2) Since the biggest delay in restarting an app that has been moved comes from having to move the data (app data and virtual machine images), occasional precopying of the smaller apps with a high likelihood of being moved into the cloud will significantly reduce the delay caused by the need to copy. Seagull addresses the classic bin-packing problem, which is known to be NP-hard. An optimal integer linear program (ILP) formulation is introduced and an algorithm for precopying is presented; in addition, an algorithm using a greedy heuristic is provided for approximating an optimal answer for large-scale problems. The heuristic uses sorting to improve results. The authors also present a prototyped Seagull using a Xen-based local data center and Amazon EC2 for cloud bursting. Finally, a detailed experimental evaluation for Seagull is presented, using three examples of different web applications. The applications each have a MySQL database backend; one is a two-tier Java application implemented as an Apache Tomcat servlet, one uses a PHP application, and one uses an Ajax application and supports a memcached tier that can be horizontally scaled. The authors testbedprototype choices (for example, choice of apps, parameterssizes selected) are well reasoned and explained. The design is flexible in that different algorithmsweighting representing cost and excess demand can be substituted. The experimental protocol is thorough and well reasoned. Figure 12 shows Seagulls scalability to 800 hosts with proportional increases in numbers of the three applications. Real data centers may be running hundreds (or thousands) of applications, as well as thousands of hosts. It would be interesting to think through how Seagulls performance would be impacted by this much longer list of applications to consider as part of the computation time. Overall, this was an excellent and very thorough paper. Online Computing Reviews Service

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cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 13, Issue 3

May 2014

97 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/2630790

Editor:
Munindar P. Singh
Department of Computer Science, North Carolina State University

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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 the author(s) 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].

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Publication History

Published: 01 May 2014

Accepted: 01 October 2013

Revised: 01 September 2013

Received: 01 January 2013

Published in TOIT Volume 13, Issue 3

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