Transient Provisioning and Performance Evaluation for Cloud Computing Platforms: A Capacity Value Approach

TL;DR

This paper presents a matrix analytic approach to model and optimize transient resource provisioning in cloud platforms, accounting for bursty, predictable, and batch demand variations to minimize revenue loss.

Contribution

It introduces a novel framework for transient provisioning that incorporates diverse demand characteristics and provides simple, numerically efficient expressions for expected revenue loss.

Findings

Accounting for demand variability reduces revenue loss significantly.

The framework models a wide range of system behaviors effectively.

Simple expressions enable straightforward numerical evaluation.

Abstract

User demand on the computational resources of cloud computing platforms varies over time. These variations in demand can be predictable or unpredictable, resulting in `bursty' fluctuations in demand. Furthermore, demand can arrive in batches, and users whose demands are not met can be impatient. We demonstrate how to compute the expected revenue loss over a finite time horizon in the presence of all these model characteristics through the use of matrix analytic methods. We then illustrate how to use this knowledge to make frequent short term provisioning decisions --- transient provisioning. It is seen that taking each of the characteristics of fluctuating user demand (predictable, unpredictable, batchy) into account can result in a substantial reduction of losses. Moreover, our transient provisioning framework allows for a wide variety of system behaviors to be modeled and gives simple expressions for expected revenue loss which are straightforward to evaluate numerically.