Publication

Cost-Optimal Outsourcing of Applications into the Clouds

Immanuel Trummer
2010
Conference Papers

Abstract

Commercial services for provisioning software components and virtual infrastructure in the cloud are emerging. For customers, this creates a multitude of possibilities for outsourcing part of the IT-stack to third parties in order to run their applications. These possibilities are associated with different running costs, so cloud customers have to determine the optimal solution. In this paper, we present and experimentally evaluate an algorithm that solves the corresponding optimization problem. We assume that applications are described as templates, fixing the deployment structure and constraining the properties of the used soft- and hardware components. Different parts of the application may be outsourced to different providers and several levels of outsourcing can be considered. However, dependencies between different parts of the application have to be respected. Our algorithm decomposes the application graph in a first step in order to discover all suitable cloud provisioning services from a registry. It determines the optimal solution by representing the problem as constraint optimization problem that can be solved by an existing solver implementation.

Official source

https://infoscience.epfl.ch/entities/publication/312c7c06-b330-4c49-b4b9-ea205e556a03

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Immanuel Trummer
2010
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/312c7c06-b330-4c49-b4b9-ea205e556a03

About this result

Related concepts (32)

Optimization problem

In mathematics, computer science and economics, an optimization problem is the problem of finding the best solution from all feasible solutions. Optimization problems can be divided into two categories, depending on whether the variables are continuous or discrete: An optimization problem with discrete variables is known as a discrete optimization, in which an object such as an integer, permutation or graph must be found from a countable set.

Constrained optimization

In mathematical optimization, constrained optimization (in some contexts called constraint optimization) is the process of optimizing an objective function with respect to some variables in the presence of constraints on those variables. The objective function is either a cost function or energy function, which is to be minimized, or a reward function or utility function, which is to be maximized.

Convex optimization

Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets (or, equivalently, maximizing concave functions over convex sets). Many classes of convex optimization problems admit polynomial-time algorithms, whereas mathematical optimization is in general NP-hard.

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