Architectural Partitioning and Deployment Modeling on Hybrid Clouds

TL;DR

This paper presents a heuristic approach for architecturally partitioning workloads and modeling hybrid cloud deployments, along with effort estimation, validated through multiple industry case studies.

Contribution

It introduces a novel heuristic solution for hybrid cloud workload partitioning and a deployment effort estimation model validated with real-world case studies.

Findings

Partitioning workloads improves hybrid cloud efficiency.

The deployment effort varies significantly across industry verticals.

The proposed model accurately predicts deployment effort in diverse scenarios.

Abstract

The hybrid cloud idea is increasingly gaining momentum because it brings distinct advantages as a hosting platform for complex software systems. However, there are several challenges that need to be surmounted before hybrid hosting can become pervasive and penetrative. One main problem is to architecturally partition workloads across permutations of feasible cloud and non-cloud deployment choices to yield the best-fit hosting combination. Another is to predict the effort estimate to deliver such an advantageous hybrid deployment. In this paper, we describe a heuristic solution to address the said obstacles and converge on the ideal hybrid cloud deployment architecture, based on properties and characteristics of workloads that are sought to be hosted. We next propose a model to represent such a hybrid cloud deployment, and demonstrate a method to estimate the effort required to implement and sustain that deployment. We also validate our model through dozens of case studies spanning several industry verticals and record results pertaining to how the industrial grouping of a software system can impact the aforementioned hybrid deployment model.