CloudGenius: A Hybrid Decision Support Method for Automating the Migration of Web Application Clusters to Public Clouds

Abstract

Abstract-With the increase in cloud service providers, and the increasing number of compute services offered, a migration of information systems to the cloud demands selecting the best mix of compute services and VM (Virtual Machine) images from an abundance of possibilities. Therefore, a migration process for web applications has to automate evaluation and, in doing so, ensure that Quality of Service (QoS) requirements are met, while satisfying conflicting selection criteria like throughput and cost. When selecting compute services for multiple connected software components, web application engineers must consider heterogeneous sets of criteria and complex dependencies across multiple layers, which is impossible to resolve manually. The previously proposed CloudGenius framework has proven its capability to support migrations of single-component web applications. In this paper, we expand on the additional complexity of facilitating migration support for multi-component web applications. In particular, we present an evolutionary migration process for web application clusters distributed over multiple locations, and clearly identify the most important criteria relevant to the selection problem. Moreover, we present a multicriteria-based selection algorithm based on Analytic Hierarchy Process (AHP). Because the solution space grows exponentially, we developed a Genetic Algorithm (GA)-based approach to cope with computational complexities in a growing cloud market. Furthermore, a use case example proofs CloudGenius' applicability. To conduct experiments, we implemented CumulusGenius, a prototype of the selection algorithm and the GA deployable on hadoop clusters. Experiments with CumulusGenius give insights on time complexities and the quality of the GA.

A decision process model to support migration to cloud computing

“…The suggestions made in Khajeh-Hosseini et al (2012), Menzel et al (2014), García-Galán et al (2015) and Santiago (2015) particularly focus on the selection of the providers for Infrastructure as a Service (IaaS). Khajeh-Hosseini et al (2012) developed a cloud adoption toolkit that aids decisions on the suitability of the technology, consumption of energy, cost, impacts of stakeholders and operational viability.…”

“…It incorporates two decision support tools: cost calculation and risk-benefit analysis. Menzel et al (2014) developed the CloudGenius framework that provides a multi-criteria approach in decision support for selecting providers for IaaS to migrate a web server to the cloud. It allows users to define multiple quantitative and qualitative requirements that are then matched against a knowledge-base of cloud service providers.…”

“…Some of the cloud DSSs utilise knowledge-bases that provide cloud offerings along with their pricing policies as discussed in Menzel et al (2014) and Andrikopoulos et al (2013a). However, to make the discussions required in order to migrate, a wider range of information such as cloud capabilities, services offered, pricing schemes, etc., should be considered.…”

An exploration of the determinants for decision to migrate existing resources to cloud computing using an integrated TOE-DOI model

“…[18][19][20][21][22][23]). Although significant contributions have been made by these works, there are still many aspects which require further support [24].…”

Development of a Multisensor Embedded Intelligent Home Environment Monitoring System Based on Digital Signal Processor and Wi-Fi

“…Many control systems for home monitor or control were proposed in recent years, which shows that the integration of wireless sensor network for home monitoring was workable [15][16][17][18][19][20][21][22][23][24][25]. Some other researches for smart city, surveillance, fire safety for home, and home monitoring application research topics are also discussed in recent years [26][27][28][29][30].…”