CloudGenius: Decision Support for Web Server Cloud Migration

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, which uses AHP to automate decision-making for selecting cloud services and VM images.
• It utilizes a multi-criteria evaluation to balance factors such as cost, performance, and QoS, streamlining complex cloud migrations.
• Experimental validation demonstrates that CloudGenius reduces manual workload and improves computational efficiency despite quadratic complexity.

CloudGenius: Decision Support for Web Server Cloud Migration

Abstract

This essay examines the "CloudGenius" framework, a methodological approach designed to support the migration of Web applications to cloud infrastructure services. The framework automates the decision-making process by utilizing a multi-criteria decision-making technique, namely the Analytic Hierarchy Process (AHP), to evaluate and select cloud services and virtual machine (VM) images. By doing so, CloudGenius simplifies the complexities associated with cloud migrations, thereby reducing the workload for Web engineers. The essay will detail the framework's structure, its component-based approach, and the experimental validation results illustrating its computational efficiency.

Introduction to Cloud Migration Challenges

The migration of Web applications to cloud services represents a formidable challenge due to its inherent complexity, encompassing technical, economic, and regulatory dimensions. Selecting an appropriate cloud service and VM image is crucial for ensuring the application meets its Quality of Service (QoS) targets. CloudGenius approaches this challenge through a structured framework that leverages AHP to streamline the selection process, addressing criteria such as cost, latency, and service quality.

CloudGenius Framework Overview

CloudGenius proposes a detailed five-step migration process:

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

The AHP-based selection mechanism evaluates cloud services and VM images against a user-defined hierarchy of criteria, allowing flexibility in aligning with organizational priorities.

Multi-Criteria Decision Making with AHP

The framework's strength lies in its use of AHP to facilitate multi-criteria decision making. CloudGenius positions both VM images and cloud services as decision alternatives, evaluating them against a structured criteria hierarchy that comprises attributes such as hourly cost, performance metrics, and popularity. This hierarchical approach allows CloudGenius to not only assess the suitability of individual components but also manage the trade-offs between various decision criteria.

Experimental Validation

The applicability of CloudGenius was demonstrated through a prototypical implementation named CumulusGenius, which emphasizes decision accuracy and process efficiency. Experiments conducted to determine the time complexity confirmed that while the approach is quadratic concerning the number of VM images and services, the decision support offered significantly reduces manual computational effort.

Implications and Future Work

The CloudGenius framework significantly eases the cloud migration process for single-tier Web applications, providing a systematic means to assess and choose cloud infrastructure services. Future work should focus on extending the framework to support multi-tier applications and incorporate qualitative criteria for more diverse decision contexts. Moreover, enhancements in user interface design and integration with existing cloud service databases could further streamline its practical deployment.

Conclusion

CloudGenius provides a structured decision-support approach that effectively addresses the intricate demands of migrating Web applications to cloud services. Through its methodical use of AHP and adaptable decision criteria, it reduces the complexity and effort involved in cloud migration, offering a valuable tool for organizations transitioning into cloud computing environments. The framework's robustness and flexibility position it as a pivotal tool in the evolving landscape of cloud adoption.