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OptiSpot: minimizing application deployment cost using spot cloud resources.

ABSTRACT: The spot instance model is a virtual machine pricing scheme in which some resources of cloud providers are offered to the highest bidder. This leads to the formation of a spot price, whose fluctuations can determine customers to be overbid by other users and lose the virtual machine they rented. In this paper we propose OptiSpot, a heuristic to automate application deployment decisions on cloud providers that offer the spot pricing model. In particular, with our approach it is possible to determine: (i) which and how many resources to rent in order to run a cloud application, (ii) how to map the application components to the rented resources, and (iii) what spot price bids to use to minimize the total cost while maintaining an acceptable level of performance. To drive the decision making, our algorithm combines a multi-class queueing network model of the application with a Markov model that describes the stochastic evolution of the spot price and its influence on virtual machine reliability. We show, using a model developed for a real enterprise application and historical traces of the Amazon EC2 spot instance prices, that our heuristic finds low cost solutions that indeed guarantee the required levels of performance. The performance of our heuristic method is compared to that of nonlinear programming and shown to markedly accelerate the finding of low-cost optimal solutions.

SUBMITTER: Dubois DJ

PROVIDER: S-EPMC6959408 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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OptiSpot: minimizing application deployment cost using spot cloud resources.

Dubois Daniel J DJ Casale Giuliano G

Cluster computing 20160423 2

The spot instance model is a virtual machine pricing scheme in which some resources of cloud providers are offered to the highest bidder. This leads to the formation of a spot price, whose fluctuations can determine customers to be overbid by other users and lose the virtual machine they rented. In this paper we propose OptiSpot, a heuristic to automate application deployment decisions on cloud providers that offer the spot pricing model. In particular, with our approach it is possible to determ ...[more]

PMID: 32009837

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Project description:BackgroundThe widespread popularity of genomic applications is threatened by the "bioinformatics bottleneck" resulting from uncertainty about the cost and infrastructure needed to meet increasing demands for next-generation sequence analysis. Cloud computing services have been discussed as potential new bioinformatics support systems but have not been evaluated thoroughly.ResultsWe present benchmark costs and runtimes for common microbial genomics applications, including 16S rRNA analysis, microbial whole-genome shotgun (WGS) sequence assembly and annotation, WGS metagenomics and large-scale BLAST. Sequence dataset types and sizes were selected to correspond to outputs typically generated by small- to midsize facilities equipped with 454 and Illumina platforms, except for WGS metagenomics where sampling of Illumina data was used. Automated analysis pipelines, as implemented in the CloVR virtual machine, were used in order to guarantee transparency, reproducibility and portability across different operating systems, including the commercial Amazon Elastic Compute Cloud (EC2), which was used to attach real dollar costs to each analysis type. We found considerable differences in computational requirements, runtimes and costs associated with different microbial genomics applications. While all 16S analyses completed on a single-CPU desktop in under three hours, microbial genome and metagenome analyses utilized multi-CPU support of up to 120 CPUs on Amazon EC2, where each analysis completed in under 24 hours for less than $60. Representative datasets were used to estimate maximum data throughput on different cluster sizes and to compare costs between EC2 and comparable local grid servers.ConclusionsAlthough bioinformatics requirements for microbial genomics depend on dataset characteristics and the analysis protocols applied, our results suggests that smaller sequencing facilities (up to three Roche/454 or one Illumina GAIIx sequencer) invested in 16S rRNA amplicon sequencing, microbial single-genome and metagenomics WGS projects can achieve cost-efficient bioinformatics support using CloVR in combination with Amazon EC2 as an alternative to local computing centers.

Project description:PurposeDorsal wrist ganglions are treated commonly with aspiration, or open or arthroscopic excision in operating room (OR) or procedure room (PR) settings. As it remains unclear which treatment strategy is most cost-effective in yielding cyst resolution, our purpose was to perform a formal cost-minimization analysis from the societal perspective in this context.MethodsA microsimulation decision analytic model evaluating 5 treatment strategies for dorsal wrist ganglions was developed, ending in either resolution or a single failed open revision surgical excision. Strategies included immediate open excision in the OR, immediate open excision in the PR, immediate arthroscopic excision in the OR, or 1 or 2 aspirations before each of the surgical options. Recurrence and complications rates were pooled from the literature for each treatment type. One-way sensitivity and threshold analyses were performed.ResultsThe most cost-minimal strategy was 2 aspiration attempts before open surgical excision in the PR setting ($1,603 ± 1,595 per resolved case), followed by 2 aspirations before open excision in the OR ($1,969 ± 2,165 per resolved case). Immediate arthroscopic excision was the costliest strategy ($6,539 ± 264 per resolved case). Single aspiration preoperatively was more cost-minimal than any form of immediate surgery ($2,918 ± 306 and $4,188 ± 306 per resolved case performed in the PR and OR, respectively).ConclusionsFrom the societal perspective, performing 2 aspirations before surgical excision in the PR setting was the most cost-minimal treatment strategy, although in reference to surgeons who do not perform this procedure in the PR setting, open excision in the OR was nearly as cost-effective. As patient preferences may preclude routinely performing 2 aspirations, performing at least 1 aspiration before surgical excision improves the cost-effectiveness of dorsal wrist ganglions treatment.Type of study/level of evidenceEconomic Decision Analysis II.

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OptiSpot: minimizing application deployment cost using spot cloud resources.

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OptiSpot: minimizing application deployment cost using spot cloud resources.

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