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

Queue-priority optimized algorithm: a novel task scheduling for runtime systems of application integration platforms

The need for integration of applications and services in business processes from enterprises has increased with the advancement of cloud and mobile applications. Enterprises started dealing with high volumes of data from the cloud and from mobile applications, besides their own. This is the reason why integration tools must adapt themselves to handle with high volumes of data, and to exploit the scalability of cloud computational resources without increasing enterprise operations costs. Integration platforms are tools that integrate enterprises’ applications through integration processes, which are nothing but workflows composed of a set of atomic tasks connected through communication channels. Many integration platforms schedule tasks to be executed by computational resources through the First-in-first-out heuristic. This article proposes a Queue-priority algorithm that uses a novel heuristic and tackles high volumes of data in the task scheduling of integration processes. This heuristic is optimized by the Particle Swarm Optimization computational method. The results of our experiments were confirmed by statistical tests, and validated the proposal as a feasible alternative to improve integration platforms in the execution of integration processes under a high volume of data.info:eu-repo/semantics/acceptedVersio

Task scheduling characterisation in enterprise application integration

Cloud computing allows enterprises to incorporate applications and computational resources as services, and thus, enterprises can concentrate on their business processes, without concerning the development, configuration and maintenance of these applications and resources. Integration platforms are one of these services that allow enterprises to integrate applications in order to reduce the maintenance costs and operations of the integration of on-premises platforms. However, high performance on resources offered by the cloud, demands improvement in task scheduling of integration platforms. Our literature review has identified a lack of studies in the field of enterprise application integration, focusing on specificities and vulnerabilities of the task scheduling of integration processes. This is a pioneer work regarding the characterisation of the scheduling of tasks of integration processes. We propose a ranking according to their conceptual models and apply this ranking to five integration processes. Then, we have statistically analysed the influence of each component of their conceptual models on the performance of the execution of these integration processes. We characterise the task scheduling of integration processes and presented a mathematical equation for the makespan as a function of the components of this characterisation. This study can guide software engineers in the optimal task scheduling for integration processes, which can improve the performance runtime systems regarding using the computational resources and result in minimisation of costs of companies.info:eu-repo/semantics/acceptedVersio

Modelagem de preços de provedores de IaaS utilizando regressão múltipla

Uma alternativa para usuários reduzirem custos de aquisição e manutenção de infraestrutura computacional para desenvolver, implementar e executar suas aplicações é a computação em nuvem. Os serviços de computação em nuvem são oferecidos por provedores e podem ser classificados em três modalidades: Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS) e Infrastructure-as-a-Service (IaaS). Em IaaS, os provedores oferecem os serviços divididos em instâncias e o usuário tem à disposição uma máquina virtual com os recursos computacionais que desejar a um determinado valor. O principal desafio enfrentado pelas empresas é escolher, além do provedor, a instância que melhor se adapta as suas necessidades. Frequentemente, estas empresas precisam de uma grande infraestrutura computacional para gerir e aperfeiçoar seus processos de negócio e, diante do alto custo para manter uma infraestrutura local, têm migrado suas aplicações para a nuvem. Este trabalho busca fornecer subsídios capazes de auxiliar as empresas no processo de seleção do melhor provedor/instância para implantar e executar suas soluções de integração na nuvem. Para isso, um estudo preliminar para a elaboração de uma nova proposta de modelagem dos preços das instâncias de máquinas virtuais usando regressão linear é apresentado. Nesta abordagem são considerados os provedores Amazon EC2, Google Compute Engine e Microsoft Windows Azure.info:eu-repo/semantics/acceptedVersio

A cloud-based integration platform for enterprise application integration: a model-driven engineering approach

This article addresses major information systems integration problems, approaches, technologies, and tools within the context of Model-Driven Software Engineering. The Guaraná integration platform is introduced as an innovative platform amongst state-of-the-art technologies available for enterprises to design and implement integration solutions. In this article, we present its domain-specific modeling language and its industrial cloud-based web development platform, which supports the design and implementation of integration solutions. A real-world case study is described and analyzed; then, we delve into its design and implementation, to finally disclose ten measures that empirically help estimating the amount of effort involved in the development of integration solutions.info:eu-repo/semantics/acceptedVersio

On using Markov decision processes to model integration solutions for disparate resources in software ecosystems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The software ecosystem of an enterprise is usually composed of an heterogeneous set of applications, databases, documents, spreadsheets, and so on. Such resources are involved in the enterprise’s daily activities by supporting its business processes. As a consequence of market change and the enterprise evolution, new business processes emerge and the current ones have to be evolved to tackle the new requirements. It is not a surprise that different resources may be required to collaborate in a business process. However, most of these resources were devised without taking into account their integration with the others, i.e., they represent isolated islands of data and functionality. Thus, the goal of an integration solution is to enable the collaboration of different resources without changing them or increasing their coupling. The analysis of integration solutions to predict their behaviour and find possible performance bottlenecks is an important activity that contributes to increase the quality of the delivered solutions. Software engineers usually follow an approach that requires the construction of the integration solution, the execution of the actual integration solution, and the collection of data from this execution in order to analyse and predict their behaviour. This is a costly, risky, and time-consuming approach. This paper discusses the usage of Markov models for formal modelling of integration solutions aiming at enabling the simulation of the conceptual models of integration solutions still in the design phase. By using well-established simulation techniques and tools at an early development stage, this new approach contributes to reduce cost, risk, development time and improve software quality attributes such as robustness, scalability, and maintenance

Task Scheduling Optimization on Enterprise Application Integration Platforms Based on the Meta-heuristic Particle Swarm Optimization

Companies seek technological alternatives that provide competiti veness for their business processes. Among these alternatives, there are integration platforms that allow you to connect applications to your software ecosystems. These ecosystems are often composed of local applications and cloud computing services, such as SaaS and PaaS, and still, interact with social media. Integration platforms are specialized software that allows you to design, execute and monitor integration solutions, which connect functionality and data from different applications. Integration platforms typically provide a specific domain language, development toolkit, runtime engine, and monitoring tool. The efficiency of the engine in sche duling and performing integration tasks has a direct impact on the performance of a solution and this is one of the challenges faced by integration platforms. Our literature review has identified that integration engines adopt task scheduling algorithms based on the textit First-In-First-Out discipline, which may be inefficient. Therefore, it is appropriate to seek a task scheduling algorithm that optimizes engine performance, providing a positive impact on the performance of the integration solution in different scenarios. This article proposes an algorithm for task scheduling based on the meta-heuristic optimization technique, which assigns the tasks to the computational resources, considering the waiting time in the queue of ready tasks and the computational complexity of Each task in order to optimize the performance of the integration solution