Web requests are growing exponentially since the 90s due to the rapid development of the Internet. This process was further accelerated by the introduction of cloud services. It has been observed statistically that memory or web requests generally follow power-law distribution, Breslau et al. INFOCOM'99. That is, the $i^{\text{th}}$ most popular web page is requested with a probability proportional to $1 / i^α$ ($α> 0$ is a constant). Furthermore, this study, which was performed more than 20 years ago, indicated Zipf-like behavior, i.e., that $α\le 1$. Surprisingly, the memory access traces coming from petabyte-size modern cloud systems not only show that $α$ can be bigger than one but also illustrate a shifted power-law distribution -- called Pareto type II or Lomax. These previously not reported phenomenon calls for statistical explanation. Our first contribution is a new statistical {\it multi-core power-law} model indicating that double-power law can be attributed to the presence of multiple cores running many virtual machines in parallel on such systems. We verify experimentally the applicability of this model using the Kolmogorov-Smirnov test (K-S test). The second contribution of this paper is a theoretical analysis indicating why LRU and LFU-based algorithms perform well in practice on data satisfying power-law or multi-core assumptions. We provide an explanation by studying the online paging problem in the stochastic input model, i.e., the input is a random sequence with each request independently drawn from a page set according to a distribution $π$. We derive formulas (as a function of the page probabilities in $π$) to upper bound their ratio-of-expectations, which help in establishing O(1) performance ratio given the random sequence following power-law and multi-core power-law distributions.