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CloudRec: a framework for personalized service Recommendation in the Cloud

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Abstract

The elastic computing power and the pay-as-you-go model of the cloud offer an attractive platform to deploy software as a service applications. The large number of applications expected to heavily take advantage of the cloud will result in an explosive growth of various cloud services. As many cloud services may compete to offer similar functionalities, it is desirable to consider user preferences on the nonfunctional service properties (aka, quality of service, or QoS) when delivering cloud services to the end users. Unfortunately, current approaches primarily rely on the descriptions from the cloud service providers or expert-provided rankings, which are completely orthogonal to the open and distributed nature of the cloud. We present a novel framework (referred to as CloudRec) that exploits a user-centric strategy to achieve personalized QoS assessment of cloud services. CloudRec integrates a novel community-based QoS assessment model with an iterative algorithm to accurately discover a set of homogenous user and service communities from scarce and large-scale QoS data. The communities can serve as a bridge to relate users and services and hence provide an effective means to estimate the QoS of unknown cloud services. The effectiveness of the proposed framework is demonstrated through a rigorous theoretical analysis and an extensive empirical study on real QoS data.

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Notes

  1. We use community and cluster in an exchangeable manner in the rest of the paper.

  2. Strictly speaking, we ignore \(p(u_i)\) here as \(p(z_p^u|u_i)=U_{ip}a_p/p(u_i)\). For a given \(u_i, p(u_i)\) is a constant for all clusters. Thus, we can just choose another diagonal matrix \(A'_U=\text{ diag }(a_1/p(u_i),\ldots ,a_k/p(u_i))\) to absorb the constant.

  3. http://www.planet-lab.org/.

  4. The \(P\) values for the RMSE show a similar result so we skip them to avoid redundancy.

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  1. College of Computing and Information Sciences, Rochester Institute of Tech, Rochester, NY, USA

    Qi Yu

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Yu, Q. CloudRec: a framework for personalized service Recommendation in the Cloud. Knowl Inf Syst 43, 417–443 (2015). https://doi.org/10.1007/s10115-013-0723-x

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