CloudGenius: Decision Support for Web Server Cloud Migration (1203.3997v1)

Abstract: Cloud computing is the latest computing paradigm that delivers hardware and software resources as virtualized services in which users are free from the burden of worrying about the low-level system administration details. Migrating Web applications to Cloud services and integrating Cloud services into existing computing infrastructures is non-trivial. It leads to new challenges that often require innovation of paradigms and practices at all levels: technical, cultural, legal, regulatory, and social. The key problem in mapping Web applications to virtualized Cloud services is selecting the best and compatible mix of software images (e.g., Web server image) and infrastructure services to ensure that Quality of Service (QoS) targets of an application are achieved. The fact that, when selecting Cloud services, engineers must consider heterogeneous sets of criteria and complex dependencies between infrastructure services and software images, which are impossible to resolve manually, is a critical issue. To overcome these challenges, we present a framework (called CloudGenius) which automates the decision-making process based on a model and factors specifically for Web server migration to the Cloud. CloudGenius leverages a well known multi-criteria decision making technique, called Analytic Hierarchy Process, to automate the selection process based on a model, factors, and QoS parameters related to an application. An example application demonstrates the applicability of the theoretical CloudGenius approach. Moreover, we present an implementation of CloudGenius that has been validated through experiments.

• The paper introduces CloudGenius, a framework that automates decision-making in web server cloud migration using the Analytic Hierarchy Process (AHP).
• The framework integrates user-defined requirements and QoS parameters to systematically evaluate and choose compatible VM images and infrastructure services.
• Empirical results with prototype CumulusGenius demonstrate the framework's quadratic time complexity and practical viability in optimizing migration strategies.

CloudGenius: Framework for Automated Decision Support in Web Server Cloud Migration

This paper presents "CloudGenius," a framework designed to facilitate the migration of web servers to cloud environments by automating the selection process of cloud-based virtual machine (VM) images and infrastructure services. The framework addresses the complex decision-making challenges inherent in cloud migration, which are compounded by the heterogeneous sets of criteria and dependencies between infrastructure and software images. The primary goal of CloudGenius is to enable efficient and effective migration decisions through an automated model that minimizes the manual resolution of these intricate dependencies.

CloudGenius applies the Analytic Hierarchy Process (AHP), a well-known multi-criteria decision-making technique, to evaluate alternatives based on user-defined criteria and Quality of Service (QoS) parameters specific to the application. The model facilitates the capture of user preferences and requirements, translating them into actionable criteria, thus aiding in the determination of the optimal VM image and cloud infrastructure service pair.

Model Components and Migration Process

The framework comprises several key components:

• Formal Model: It includes sets of web application requirements, VM images, infrastructure services, and providers. The dependencies between images and services ensure compatibility and feasibility of migration solutions.
• Migration Process: Defined using Business Process Model and Notation (BPMN) 2.0, the process involves input from web engineers regarding requirements, criteria, and user preferences. CloudGenius leverages this input to perform requirement checks, evaluate potential solutions, and suggest optimal VM image and service combinations.

The migration involves several steps:

1. Cloud Infrastructure Service Selection
2. Cloud VM Image Selection
3. VM Image Customization
4. Migration Strategy Definition and Application

This process can be iteratively refined, allowing users to adapt their requirements or attempt lower-ranked alternatives if initial solutions prove unsatisfactory.

Key Results and Computational Complexity

The paper provides insights into the computational complexity of the CloudGenius selection algorithm, indicating that the approach exhibits quadratic time complexity with respect to the number of VM images and services evaluated. Empirical experiments with a prototype, "CumulusGenius," demonstrate the practical viability of this decision support framework, offering valuable metrics on time complexity across different stages of the selection process.

Implications and Future Work

The implications of implementing CloudGenius extend to optimizing cloud migration efforts for web applications, reducing time and resources spent on manual decision-making. This not only supports effective migration processes but also highlights the practical benefits of integrating AI-driven models in cloud service selections.

The paper acknowledges several areas for future enhancements, such as extending criteria catalogs, improving the quality and currency of database measurements, and bolstering user-friendliness of the implementation. Moreover, expanding the framework to accommodate multi-tier applications and inter-related components could address broader decision support challenges across different IT system migrations.

In summary, CloudGenius provides a structured approach to cloud migration decision-making, leveraging comprehensive data models and automated evaluation processes to streamline migration strategies notably. While the paper primarily focuses on web applications, the principles and methodologies proposed can serve as a foundation for more complex IT system migrations, offering a pathway for further refinement and adaptation in cloud computing paradigms.