Scalable Optimal Deployment in the Cloud of Component-based Applications using Optimization Modulo Theory, Mathematical Programming and Symmetry Breaking

TL;DR

This paper presents a scalable method for deploying component-based applications in the cloud by reducing the search space using symmetry breaking and graph analysis, enabling efficient optimization with OMT.

Contribution

It introduces a novel combination of symmetry breaking and graph-based reduction techniques to improve the scalability of cloud deployment optimization.

Findings

Symmetry breaking strategies significantly reduce computational complexity.

Combining variable reduction with symmetry breaking enables scalable deployment solutions.

Experimental results show improved performance on real-world problems.

Abstract

The problem of Cloud resource provisioning for component-based applications consists in the allocation of virtual machines (VMs) offers from various Cloud Providers to a set of applications such that the constraints induced by the interactions between components and by the components hardware/software requirements are satisfied and the performance objectives are optimized (e.g. costs are minimized). It can be formulated as a constraint optimization problem, hence, in principle the optimization can be carried out automatically. In the case the set of VM offers is large (several hundreds), the computational requirement is huge, making the automatic optimization practically impossible with the current general optimization modulo theory (OMT) and mathematical programming (MP) tools. We overcame the difficulty by methodologically analyzing the particularities of the problem with the aim of identifying search space reduction methods. These are methods exploiting:(1) the symmetries of the general Cloud deployment problem, (2) the graph representation associated to the structural constraints specific to each particular application, and (3) their combination. An extensive experimental analysis has been conducted on four classes of real-world problems, using six symmetry breaking strategies and two types of optimization solvers. As a result, the combination of a variable reduction strategy with a column-wise symmetry breaker leads to a scalable deployment solution, when OMT is used to solve the resulting optimization problem.