A Cloud Resources Portfolio Optimization Business Model -- From Theory to Practice
Valentin Haag, Maximilian Kiessler, Benedikt Pittl, Erich Schikuta
TL;DR
The paper tackles cost-efficient cross-provider cloud portfolios by formulating a portfolio optimization problem that jointly selects instances and allocations over time, with probabilistic capacity constraints and a total cost objective. It proposes two heuristic solvers, ERICH (greedy) and GEORG (genetic), and validates them on synthetic data, showing ERICH is faster and typically more stable while GEORG can achieve substantial cost reductions over generations. Building on this optimization foundation, the authors present a Cloud Portfolio Manager (CPM) built around the Business Model Canvas, detailing customer segments, value propositions, channels, revenue, and partnerships, and contrasting with existing cloud brokers and platforms. A prototype UI demonstrates portfolio/app management and allocation workflows, complemented by a user experience evaluation based on Nielsen heuristics to identify strengths (aesthetic design, control) and areas for improvement (error prevention, documentation). The work highlights the practical viability of cloud brokerage for optimized, cross-provider resource management and outlines a path toward a full-scale public platform with enhanced monitoring and dynamic resource negotiation capabilities.
Abstract
Cloud resources have become increasingly important, with many businesses using cloud solutions to supplement or outright replace their existing IT infrastructure. However, as there is a plethora of providers with varying products, services, and markets, it has become increasingly more challenging to keep track of the best solutions for each application. Cloud service intermediaries aim to alleviate this problem by offering services that help users meet their requirements. This paper aims to lay the groundwork for developing a cloud portfolio management platform and its business model, defined via a business model canvas. Furthermore, a prototype of a platform is developed offering a cloud portfolio optimization service, using two algorithms developed in previous research to create suitable and well-utilized allocations for a customer's applications.