Demystifying the Clouds: Harnessing Resource Utilization Models for Cost Effective Infrastructure Alternatives

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 (VM) 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.

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“…Lloyd et al proposed a workload cost prediction methodology to support developers in determining cost effective deployment options for cloud‐based SOA applications. Despite sharing some of our goals, that work focuses only on analyzing performance and cost related metrics, whereas we propose a more general approach to assess and rank deployment options based on multiple quality attributes.…”

Sustainable and Resilient Network Infrastructure Design for Cloud Data Centers

“…up the highest throughput to the network which contributes 9.82% to the entire network, while Rks [15].server [1] contributed the smallest portion of the network throughput of only 0.248%. However, the node failure would further degrade the entire network performance due to the completed cloud tasks being reduced because of the decreasing total number of packets received.…”

“…However, the node failure would further degrade the entire network performance due to the completed cloud tasks being reduced because of the decreasing total number of packets received. The failure on Rks [15].server [1] made average network throughput decrease by 3.1% Table 4.12 -Representative node network throughput ratio and degradation after failure …”

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