Abstract
One of the drawbacks of using predictive quality of service (QoS) in cloud service suggestions is that the values vary rapidly over time, which may result in end-users receiving inadequate services. As a result, the cloud-based recommendation system’s performance suffers. In this paper, an efficient service recommendation with a spatial–temporal aware QoS prediction mechanism in a cloud computing environment is proposed. The main contribution of this article is to use the geographical location of the services to help us choose the closest neighbor to show time QoS values sparingly, reducing the range of searches while increasing precision, and then using the Bayesian ridge regression technique to model QoS variations by making a zero-mean Laplace prior distribution assumption on the residuals of the QoS prediction, which corresponds to a Bayesian regression problem. The findings of the experiment show that the proposed approach may enhance the accuracy of time-aware cloud service recommendation by 10% over the previous approaches of temporal QoS prediction.
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Youssef, A., Pati, A., Parhi, M. (2023). An Efficient Service Recommendation with Spatial–Temporal Aware QoS Prediction Mechanism in Cloud Computing Environment. In: Pati, B., Panigrahi, C.R., Mohapatra, P., Li, KC. (eds) Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering. Lecture Notes in Networks and Systems, vol 428. Springer, Singapore. https://doi.org/10.1007/978-981-19-2225-1_12
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DOI: https://doi.org/10.1007/978-981-19-2225-1_12
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