Task Admission Control and Boundary Analysis of Cognitive Cloud Data Centers

TL;DR

This paper proposes a cognitive cloud data center model with real-time task admission control, deriving optimal policies and bounds, validated through experiments and machine learning techniques for economically optimal resource management.

Contribution

It introduces a novel cognitive cloud data center model with real-time admission control, deriving optimal policies and bounds, and demonstrates the use of machine learning for parameter estimation.

Findings

Optimal state-related control limit policy identified

Derived lower and upper bounds for the policy

Validated model through comprehensive experiments

Abstract

A novel cloud data center (DC) model is studied here with cognitive capabilities for real-time (or online) flow compared to the batch tasks. Here, a DC can determine the cost of using resources and an online user or the user with batch tasks may decide whether or not to pay for getting the services. The online service tasks have a higher priority in getting the service over batch tasks. Both types of tasks need a certain number of virtual machines (VM). By targeting on the maximization of total discounted reward, an optimal policy for admitting task tasks is finally verified to be a state-related control limit policy. Next, a lower and an upper bound for such an optimal policy are derived, respectively, for the estimation and utilization in reality. Finally, a comprehensive set of experiments on the various cases to validate this proposed model and the solution is conducted. As a demonstration, the machine learning method is adopted to show how to obtain the optimal values by using a feed-forward neural network model. The results achieved in this paper will be expectedly utilized in various cloud data centers with cognitive characteristics in an economically optimal strategy.