Price modeling of IaaS providers - An approach focused on enterprise application integration

One of the main advances in information technology today is cloud computing. It is a great alternative for users to reduce costs related to the need to acquire and maintain computational infrastructure to develop, implement and execute software applications. Cloud computing services are offered by providers and can be classified into three main modalities: Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS) and Infrastructureas-a-Service (IaaS). In IaaS, the user has a virtual machine at their disposal with the desired computational resources at a given cost. Generally, the providers offer infrastructure services divided into instances, with preestablished configurations. The main challenge faced by companies is to choose the instance that best fits their needs among the many options offered by providers. Frequently, these companies need a large computational infrastructure to manage and improve their business processes and, due to the high cost of maintaining local infrastructure, they have begun to migrate applications to the cloud in order to reduce these costs. In this paper, we introduce a proposal for price modeling of instances of virtual machines using linear regression. This approach analyzes a set of simplified hypotheses considering the following providers: Amazon EC2, Google Compute Engine and Microsoft Windows Azure.info:eu-repo/semantics/acceptedVersio

Decision Support Tools for Cloud Migration in the Enterprise

This paper describes two tools that aim to support decision making during the migration of IT systems to the cloud. The first is a modeling tool that produces cost estimates of using public IaaS clouds. The tool enables IT architects to model their applications, data and infrastructure requirements in addition to their computational resource usage patterns. The tool can be used to compare the cost of different cloud providers, deployment options and usage scenarios. The second tool is a spreadsheet that outlines the benefits and risks of using IaaS clouds from an enterprise perspective; this tool provides a starting point for risk assessment. Two case studies were used to evaluate the tools. The tools were useful as they informed decision makers about the costs, benefits and risks of using the cloud.Comment: To appear in IEEE CLOUD 201

A new MDA-SOA based framework for intercloud interoperability

Cloud computing has been one of the most important topics in Information Technology which aims to assure scalable and reliable on-demand services over the Internet. The expansion of the application scope of cloud services would require cooperation between clouds from different providers that have heterogeneous functionalities. This collaboration between different cloud vendors can provide better Quality of Services (QoS) at the lower price. However, current cloud systems have been developed without concerns of seamless cloud interconnection, and actually they do not support intercloud interoperability to enable collaboration between cloud service providers. Hence, the PhD work is motivated to address interoperability issue between cloud providers as a challenging research objective. This thesis proposes a new framework which supports inter-cloud interoperability in a heterogeneous computing resource cloud environment with the goal of dispatching the workload to the most effective clouds available at runtime. Analysing different methodologies that have been applied to resolve various problem scenarios related to interoperability lead us to exploit Model Driven Architecture (MDA) and Service Oriented Architecture (SOA) methods as appropriate approaches for our inter-cloud framework. Moreover, since distributing the operations in a cloud-based environment is a nondeterministic polynomial time (NP-complete) problem, a Genetic Algorithm (GA) based job scheduler proposed as a part of interoperability framework, offering workload migration with the best performance at the least cost. A new Agent Based Simulation (ABS) approach is proposed to model the inter-cloud environment with three types of agents: Cloud Subscriber agent, Cloud Provider agent, and Job agent. The ABS model is proposed to evaluate the proposed framework.Fundação para a Ciência e a Tecnologia (FCT) - (Referencia da bolsa: SFRH SFRH / BD / 33965 / 2009) and EC 7th Framework Programme under grant agreement n° FITMAN 604674 (http://www.fitman-fi.eu

Infrastructure-as-a-Service Usage Determinants in Enterprises

The thesis focuses on the research question, what the determinants of Infrastructure-as-a-Service usage of enterprises are. A wide range of IaaS determinants is collected for an IaaS adoption model of enterprises, which is evaluated in a Web survey. As the economical determinants are especially important, they are separately investigated using a cost-optimizing decision support model. This decision support model is then applied to a potential IaaS use case of a large automobile manufacturer

Right Scaling for Right Pricing: A Case Study on Total Cost of Ownership Measurement for Cloud Migration

Cloud computing promises traditional enterprises and independent software vendors a myriad of advantages over on-premise installations including cost, operational and organizational efficiencies. The decision to migrate software configured for on-premise delivery to the cloud requires careful technical consideration and planning. In this chapter, we discuss the impact of right-scaling on the cost modelling for migration decision making and price setting of software for commercial resale. An integrated process is presented for measuring total cost of ownership, taking in to account IaaS/PaaS resource consumption based on forecast SaaS usage levels. The process is illustrated with a real world case study