Elisabetta Nitto Peter Matthews Dana Petcu - Model-driven development and operation of multi cloud applications: the

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The Author(s) 2017

Elisabetta Di Nitto , Peter Matthews , Dana Petcu and Arnor Solberg (eds.) Model-Driven Development and Operation of Multi-Cloud Applications SpringerBriefs in Applied Sciences and Technology 10.1007/978-3-319-46031-4_1

1. Introduction

Elisabetta Di Nitto 1

(1)

Politecnico di Milano - DEIB, Piazza L. da Vinci, 32, 20133 Milan, Italy

(2)

Institute e-Austria Timioara and West University of Timioara, B-dul Vasile Prvan 4, 300223 Timioara, Romania

Elisabetta Di Nitto (Corresponding author)

Email:

Dana Petcu (Corresponding author)

Email:

1.1 Context

Cloud computing is a major trend in the ICT industry. The wide spectrum of available Clouds, such as those offered by Microsoft, Google, Amazon, HP, AT&T, and IBM, just to mention big players, provides a vibrant technical environment, where even small and medium enterprises (SMEs) use cheap and flexible services creating innovative solutions and evolving their existing service offer. Despite this richness of environments, Cloud business models and technologies are characterized by critical issues, such as the heterogeneity between vendor technologies and the resulting lack of interoperability between Clouds. In this setting a number of challenges for systems developers and operators can be identified, especially for SMEs that have limited resources and do not have the strength to influence the market. In particular:

• Vendor Lock-in []. Cloud providers offer proprietary solutions that force Cloud customers to decide, at the early stages of software development the design and deployment models to adopt (e.g., public vs. hybrid Clouds) as well as the technology stack (e.g., Amazon Simple DB vs. Google Bigtable).
• Risk Management . There are several concerns when selecting a Cloud technology such as payment models, security, legal and contractual, quality and integration with the enterprise architecture and culture.
• Quality Assurance . Cloud performance can vary at any point in time. Elasticity may not ramp at desired speeds. Unavailability problems exist even when 99.9 % up-time is advertised (e.g., Amazon EC2 and Microsoft Office 365 outages in 2011).

The above issues can be addressed by enabling companies to develop their applications for multiple Cloud targets, by offering them proper tools to analyze the risks, performance and cost of various solutions and identify the ones that best suit the needs of the specific case, and by supporting a multi-Cloud deployment of applications to ensure a level of availability that is greater than the one offered by each specific Cloud. In this context, within the MODAClouds project, we focused on the following objectives:

• Deliver an advanced software engineering model-driven approach and an Integrated Development Environment (IDE) to support systems developers in building and deploying applications, together with related data, to multi-Clouds spanning across the full Cloud stack (Infrastructure as a Service, shortly IaaS, Platform as a Service, shortly PaaS, and Software as a Service, shortly SaaS).
• Define quality measures, monitoring mechanisms, prediction models, and adaptive policies to provide quality assurance in Clouds and multi-Clouds.
• Provide support to costs and risks analysis to increase trust in Clouds.
• Develop an integration framework between design tools and run-time.
• Create relevant and complex case studies for the entire risks assessment and software engineering methodologies.
• Analyze and validate project outcomes through case studies.
• Ensure distribution of project results via dissemination activities on relevant publication channels, training, and standardization.
• Provide community-based open source solutions supporting the full applications life-cycle.

In this chapter we provide a motivation for the adoption of a multi-Cloud approach and of a model-driven, quality aware development and operation paradigm (Sect. ).

1.2 Motivation

The main drivers for exploiting a multiple Cloud approach can be of various nature, from the need to avoid dependence from a single provider to the need to follow local constraints and laws, to the opportunity to replicate software in order to enhance availability. The main factors we have identified are summarized in Fig.. In the figure we distinguish between those drives that imply the simultaneous usage of services from multiple Clouds and those that are more concerned with the possibility of preparing a software system to be run on multiple Clouds but still using a single Cloud at a time during operation.

Fig. 1.1

Drivers for multi-Cloud adoption

To exemplify concrete needs in an industrial context, we refer to the case of a small company that we call MODAFIN, specialised in IT applications for financial services. Its main product line is a proprietary solution for stock market operations, cash administration, and lending management.

MODAFIN most profitable activities are software customization and life-cycle management for this product line.

Customisation involves development of custom modules to accommodate new functional requirements. Moreover, it includes application integration with existing databases and legacy business systems at the customer site.

Life-cycle management needs to assure high-availability for real-time computations during market hours, scalability and low operational costs for batch analytic workloads running after-hours. MODAFIN fulfills these quality requirements with a capacity management consultancy team following the application life-cycle.

The consultancy team has been working for a long time at the customers site, where the system is deployed in the operation environment. Thanks to the diffusion of the Cloud, however, new needs have arisen. At night, some customers want to run their batch analytic workloads at the cheapest operational costs of Amazon on-spot instances. During the day, they expect calculation engines to ramp-up computing power at an unprecedented pace when the stock market gets volatile. Moreover, some customer applications are collecting and processing stock market data directly on the Cloud using PaaS datastore services such as Google Bigtable or Amazon SimpleDB. At the same time, customers are cutting spending in consultancy services for life-cycle management as they are relying more and more on SaaS services.

To remain competitive, MODAFIN solution must evolve addressing all above requirements. To do so, the Company needs to apply advanced software engineering methodologies revising both the software development process and its life-cycle management services:

• It needs to develop a solution that can be executed on a broad spectrum of customers IaaS/PaaS, also supporting Cloud bursting, that is, the ability to move part of the system on a different Cloud to manage pick of traffic when needed.
• It must develop a flexible architecture for the system so that it could be adapted to new Cloud offers emerging in the next 510 years to adapt to changes of context and requirements.
• It needs libraries and connectors to integrate various data storage tools and services to address different needs in terms of performance, data locality, scalability and the like.

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