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Issue No. 04 - Oct.-Dec. (2017 vol. 5)
ISSN: 2168-7161
pp: 667-680
Wes J. Lloyd , Departments of Computer Science and Civil Engineering, 1372 Campus Delivery, Colorado State University, Fort Collins, CO
Shrideep Pallickara , Department of Computer Science, 1873 Campus Delivery, Colorado St. Univ., Ft. Collins, CO
Olaf David , Departments of Computer Science and Civil Engineering, 1372 Campus Delivery, Colorado State University, Fort Collins, CO
Mazdak Arabi , Departments of Computer Science and Civil Engineering, 1372 Campus Delivery, Colorado State University, Fort Collins, CO
Tyler Wible , Departments of Computer Science and Civil Engineering, 1372 Campus Delivery, Colorado State University, Fort Collins, CO
Jeffrey Ditty , Departments of Computer Science and Civil Engineering, 1372 Campus Delivery, Colorado State University, Fort Collins, CO
Ken Rojas , US Department of Agriculture Natural Resources Conservation Service, 2150 Centre Ave, Information Technology Center, Building A, Suite 150, Fort Collins, CO
ABSTRACT
Deployment of service oriented applications (SOAs) to public infrastructure-as-a-service (IaaS) clouds presents challenges to system analysts. Public clouds offer an increasing array of virtual machine types with qualitatively defined CPU, disk, and network I/O capabilities. Determining cost effective application deployments requires selecting both the quantity and type of virtual machine (VM) resources for hosting SOA workloads of interest. Hosting decisions must utilize sufficient infrastructure to meet service level objectives and cope with service demand. To support these decisions, analysts must: (1) understand how their SOA behaves in the cloud; (2) quantify representative workload(s) for execution; and (3) support service level objectives regardless of the performance limits of the hosting infrastructure. In this paper we introduce a workload cost prediction methodology which harnesses operating system time accounting principles to support equivalent SOA workload performance using alternate virtual machine types. We demonstrate how the use of resource utilization checkpointing supports capturing the total resource utilization profile for SOA workloads executed across a pool of VMs. Given these workload profiles, we develop and evaluate our cost prediction methodology using six SOAs. We demonstrate how our methodology can support finding alternate infrastructures that afford lower hosting costs while offering equal or better performance using any VM type on Amazon's public elastic compute cloud.
INDEX TERMS
Resource management, Cloud computing, Semiconductor optical amplifiers, Service-oriented architecture, Hardware, Predictive models, Computational modeling
CITATION

W. J. Lloyd et al., "Demystifying the Clouds: Harnessing Resource Utilization Models for Cost Effective Infrastructure Alternatives," in IEEE Transactions on Cloud Computing, vol. 5, no. 4, pp. 667-680, 2017.
doi:10.1109/TCC.2015.2430339
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