Instance-Frequency-Weighted Regularized, Nonnegative and Adaptive Latent Factorization of Tensors for Dynamic QoS Analysis

Abstract

Temporally dynamic QoS data are commonly encountered in large-scale cloud services environments. They can be quantized into a high-dimensional and incomplete (HDI) tensor defined on the user×service×time. Despite its HDI nature, it contains various temporal patterns highly helpful in representing involved users and services. A latent factorization of tensors (LFT) model is able to discover such patterns from an HDI tensor, while its model generality cannot be ensured due to the complex structure and incomplete data of an HDI tensor. To address this issues, this paper proposes an Instance-frequency-weighted regularized, Nonnegative and Adaptive LFT (INAL) model with three-fold ideas: a) adopting the principle of data density-oriented modeling to reduce the computation and storage complexity; b) refining the regularization effects on each latent factor with its relevant instance-frequency for illustrating the imbalanced distribution of known data in an HDI tensor; and c) making its hyper-parameter self-adaptive via incorporating the principle of a particle swarm optimization (PSO) algorithm into the training process, thereby achieving a highly adaptive and practical model. Empirical studies on two dynamic QoS datasets from real applications demonstrate that compared with state-of-the-art models, the proposed model achieves significant gain in prediction accuracy for unobserved dynamic QoS data and achieves highly competitive computational efficiency.