L. Bölöni and D. Turgut

Value of information based scheduling of cloud computing resources


Cite as:

L. Bölöni and D. Turgut. Value of information based scheduling of cloud computing resources. Future Generation Computer Systems, 71:212–220, June 2017.

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Abstract:

Traditionally, heavy computational tasks were performed on a dedicated infrastructure requiring a heavy initial investment, such as a supercomputer or a data center. Grid computing relaxed the assumptions of the fixed infrastructure, allowing the sharing of remote computational resources. Cloud computing brought these ideas into the commercial realm and allows users to request on demand an essentially unlimited amount of computing power. However, in contrast to previous assumptions, this computing power is metered and billed on an hour-by-hour basis. In this paper, we are considering applications where the output quality increases with the deployed computational power, a large class including applications ranging from weather prediction to financial modeling. We are proposing a computation scheduling that considers both the financial cost of the computation and the predicted financial benefit of the output, that is, its value of information (VoI). We model the proposed approach for an example of analyzing real-estate investment opportunities in a competitive environment. We show that by using the VoI-based scheduling algorithm, we can outperform minimalistic computing approaches, large but fixedly allocated data centers and cloud computing approaches that do not consider the VoI.

BibTeX:

@article{Boloni-2017-FGCS,
    author = "L. B{\"o}l{\"o}ni and D. Turgut",
    title = "Value of information based scheduling of cloud computing resources",
    journal = "Future Generation Computer Systems",
    volume = "71",
    pages = "212-220",
    year = "2017",
    month = "June",
    doi = "10.1016/j.future.2016.10.024",
    abstract = {
     Traditionally, heavy computational tasks were performed on a
     dedicated infrastructure requiring a heavy initial investment, such
     as a supercomputer or a data center. Grid computing relaxed the
     assumptions of the fixed infrastructure, allowing the sharing of
     remote computational resources. Cloud computing brought these ideas
     into the commercial realm and allows users to request on demand an
     essentially unlimited amount of computing power. However, in
     contrast to previous assumptions, this computing power is metered
     and billed on an hour-by-hour basis.
     In this paper, we are considering applications where the output
     quality increases with the deployed computational power, a large
     class including applications ranging from weather prediction to
     financial modeling. We are proposing a computation scheduling that
     considers both the financial cost of the computation and the
     predicted financial benefit of the output, that is, its value of
     information (VoI). We model the proposed approach for an example of
     analyzing real-estate investment opportunities in a competitive
     environment. We show that by using the VoI-based scheduling
     algorithm, we can outperform minimalistic computing approaches,
     large but fixedly allocated data centers and cloud computing
     approaches that do not consider the VoI.
    },
}

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