Un mod\`ele de trafic adapt\'e \`a la volatilit\'e de charge d'un service de vid\'eo \`a la demande: Identification, validation et application \`a la gestion dynamique de ressources

TL;DR

This paper introduces a probabilistic, epidemiology-inspired model for adaptive traffic management in video-on-demand services, enabling dynamic resource allocation and improved handling of workload spikes.

Contribution

It presents a novel analytical model and parameter identification method for modeling workload variability, validated with real data, and applies Large Deviation Principles to inform resource policies.

Findings

Model accurately fits real workload traces

Verifies a Large Deviation Principle for extreme events

Supports dynamic resource management policies

Abstract

Dynamic resource management has become an active area of research in the Cloud Computing paradigm. Cost of resources varies significantly depending on configuration for using them. Hence efficient management of resources is of prime interest to both Cloud Providers and Cloud Users. In this report we suggest a probabilistic resource provisioning approach that can be exploited as the input of a dynamic resource management scheme. Using a Video on Demand use case to justify our claims, we propose an analytical model inspired from standard models developed for epidemiology spreading, to represent sudden and intense workload variations. As an essential step we also derive a heuristic identification procedure to calibrate all the model parameters and evaluate the performance of our estimator on synthetic time series. We show how good can our model fit to real workload traces with respect to the stationary case in terms of steady-state probability and autocorrelation structure. We find that the resulting model verifies a Large Deviation Principle that statistically characterizes extreme rare events, such as the ones produced by "buzz effects" that may cause workload overflow in the VoD context. This analysis provides valuable insight on expectable abnormal behaviors of systems. We exploit the information obtained using the Large Deviation Principle for the proposed Video on Demand use-case for defining policies (Service Level Agreements). We believe these policies for elastic resource provisioning and usage may be of some interest to all stakeholders in the emerging context of cloud networking.