Abstract
The number of cloud service users has increased worldwide, and cloud service providers have been deploying and operating data centers to serve the globally distributed cloud users. The resource capacity of a data center is limited, so distributing the load to global data centers will be effective in providing stable services. Another issue in cloud computing is the need for providers to guarantee the service level agreements (SLAs) established with consumers. Whereas various load balancing algorithms have been developed, it is necessary to avoid SLA violations (e.g., service response time) when a cloud provider allocates the load to data centers geographically distributed across the world. Considering load balancing and guaranteed SLA, therefore, this paper proposes an SLA-based cloud computing framework to facilitate resource allocation that takes into account the workload and geographical location of distributed data centers. The contributions of this paper include: (1) the design of a cloud computing framework that includes an automated SLA negotiation mechanism and a workload- and location-aware resource allocation scheme (WLARA), and (2) the implementation of an agent-based cloud testbed of the proposed framework. Using the testbed, experiments were conducted to compare the proposed schemes with related approaches. Empirical results show that the proposed WLARA performs better than other related approaches (e.g., round robin, greedy, and manual allocation) in terms of SLA violations and the provider’s profits. We also show that using the automated SLA negotiation mechanism supports providers in earning higher profits.
Similar content being viewed by others
References
Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25(6):599–616
Amazon EC2 (2012) http://aws.amazon.com/ec2. Accessed 1 July 2012
Armbrust M, Fox A, Griffith R, Joseph A, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zahara M (2010) A view of cloud computing. Commun ACM 53(4):50–58
Minarolli D, Freisleben B (2011) Utility-based resource allocation for virtual machines in cloud computing. In: 2011 IEEE symposium on computers and communications (ISCC), pp 410–417. doi:10.1109/ISCC.2011.5983872
Walsh WE, Tesauro G, Kephart JO, Das R (2004) Utility functions in autonomic systems. In: First international conference on autonomic computing (ICAC’04), pp 70–77
Menasce DA, Bennani MN (2006) Autonomic virtualized environments. In: International conference on autonomic and autonomous systems (ICAS’06), pp 28–38
Chase JS, Anderson DC, Thakar PN, Vahdat AM, Doyle RP (2001) Managing energy and server resources in hosting centers. In: Eighteenth ACM symposium on operating systems principles (SOSP’01), pp 103–116
Van Nguyen H, Dang Tran F, Menaud J-M (2009) Autonomic virtual resource management for service hosting platforms. In: 2009 ICSE workshop on software engineering challenges of cloud computing (CLOUD’09), pp 1–8
Bennani MN, Menasce DA (2005) Resource allocation for autonomic data centers using analytic performance models. In: Second international conference on autonomic computing ICAC 2005, pp 229–240. doi:10.1109/ICAC.2005.50
Shi W, Hong B (2011) Towards profitable virtual machine placement in the data center. In: 2011 fourth IEEE international conference on utility and cloud computing (UCC), pp 138–145. doi:10.1109/UCC.2011.28
Breitgand D, Epstein A (2011) Sla-aware placement of multivirtual machine elastic services in compute clouds. In: 12th IFIP/IEEE international symposium on integrated network management (IM11), Dublin, Ireland
Chaisiri S, Lee B-S, Niyato D (2009) Optimal virtual machine placement across multiple cloud providers. In: Services computing conference, APSCC 2009. IEEE Asia-Pacific, pp 103–110. doi:10.1109/APSCC.2009.5394134
Frincu ME, Craciun C (2011) Multi-objective meta-heuristics for scheduling applications with high availability requirements and cost constraints in multi-cloud environments. In: 2011 fourth IEEE international conference on utility and cloud computing (UCC), pp 267–274. doi:10.1109/UCC.2011.43
Tordsson J, Montero RS, Moreno-Vozmediano R, Llorente IM (2012) Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers. Future Gener Comput Syst 28(2):358–367. doi:10.1016/j.future.2011.07.003
Sotomayor B, Montero RS, Llorente IM, Foster I (2009) Virtual infrastructure management in private and hybrid clouds. IEEE Internet Comput 13(5):14–22
Buyya R, Beloglazov A, Abawajy J (2010) Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. In: The 2010 international conference on parallel and distributed processing techniques and applications (PDPTA2010), pp 6–20
Le K, Zhang J, Meng J, Bianchini R, Nguyen TD, Jaluria Y (2011) Reducing electricity cost through virtual machine placement in high performance computing clouds. In: 2011 super computing (SC11), Washington, USA
Sim KM (2010) Grid resource negotiation: survey and new directions. IEEE Trans Syst Man Cybern, Part C, Appl Rev 40(3):245–257
Paschke A, Dietrich J, Kuhla K (2005) A logic based SLA management framework. In: Semantic web and policy workshop (SWPW), 4th semantic web conference (ISWC 2005), Galway, Ireland
Netto MA, Bubendorfer K, Buyya R (2007) SLA-based advance reservations with flexible and adaptive time QoS parameters. In: 5th international conference on service-oriented computing, Vienna, Austria, Sep 2007
Brandic I, Music D, Dustdar S (2009) Service mediation and negotiation bootstrapping as first achievements towards self-adaptable grid and cloud services. In: Grids meet autonomic computing workshop (GMAC 2009), In conjunction with the 6th international conference on autonomic computing and communications, Spain, June 2009
Foster I, Kesselman C, Lee C, Lindell B, Nahrstedt K, Roy A (1999) A distributed resource management architecture that supports advance reservations and co-allocation. In: 7th international workshop on quality of service (IWQoS’99), London, UK. IEEE Comput Soc, Los Alamitos
Son S, Jung G, Jun SC (2012) A SLA-based cloud computing framework: workload and location aware resource allocation to distributed data centers in a cloud. In: The 2012 international conference on parallel and distributed processing techniques and applications (PDPTA2012), Las Vegas, USA (to be appearing)
Sim KM (2005) Equilibria, prudent compromises, and the “Waiting” game. IEEE Trans Syst Man Cybern, Part B, Cybern 35(4):712–724
Rubinstein A (1982) Perfect equilibrium in a bargaining model. Econometrica 50(1):97–109
Son S, Sim KM (2012) A price and time slot negotiation mechanism for cloud service reservations. IEEE Trans Syst Man Cybern, Part B, Cybern 42(3):713–728. doi:10.1109/TSMCB.2011.2174355
Xen Hypervisor (2012) http://www.xen.org. Accessed 10 Dec 2012
JADE (2012) http://jade.tilab.com. Accessed 1 July 2012
FIPA (2012) http://www.fipa.org. Accessed 1 July 2012
Verizon IP Latency Statistics (2012) http://verizonbusiness.com/about/network/latency. Accessed 1 July 2012
Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2010-0026438) and by PLSI supercomputing resources of Korea Institute of Science and Technology Information.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Son, S., Jung, G. & Jun, S.C. An SLA-based cloud computing that facilitates resource allocation in the distributed data centers of a cloud provider. J Supercomput 64, 606–637 (2013). https://doi.org/10.1007/s11227-012-0861-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-012-0861-z