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The growth in the number of cloud computing users has led to the availability of a variety of cloud based services provided by different vendors. This has made the task of selecting a suitable set of services quite difficult. There has been a lot of research towards the development of suitable decision support system (DSS) to assist users in making an optimal selection of cloud services. However, existing decision support systems cannot address two crucial issues: firstly, the involvement of both business and technical perspectives in decision making simultaneously and, secondly, the multiple-clouds services based selection using a single DSS. In this paper, we tackle these issues in the light of solving the problem of cloud service discovery. In particular, we present the following novel contributions: Firstly, we present a critical analysis of the state-of-the-art in decision support systems. Based on our analysis, we identify critical shortcomings in the existent tools and develop the set of requirements which should be met by a potential DSS. Secondly, we present a new holistic framework for the development of DSS which allows a pragmatic description of user requirements. Additionally, the data gathering and analysis is studied as an integral part of the proposed DSS and therefore, we present concrete algorithms to assess the data for an optimal service discovery. Thirdly, we assess our framework for applicability to cloud service selection using an industrial case study. We also demonstrate the implementation and performance of our proposed framework using a prototype which serves as a proof of concept. Overall, this paper provides a novel and holistic framework for development of a multiple cloud service discovery based decision support system. 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing 978-1-4799-8006-2/15 $31.00

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Industry in all sectors is experiencing a profound digital transformation that puts software at the core of their businesses. In order to react to continuously changing user requirements and dynamic markets, companies need to build robust workflows that allow them to increase their agility in order to remain competitive. This increasingly rapid transformation, especially in domains like IoT or Cloud computing, poses significant challenges to guarantee high quality software, since dynamism and agile short-term planning reduce the ability to detect and manage risks. In this paper, we describe the main challenges related to managing risk in agile software development, building on the experience of more than 20 agile coaches operating continuously for 15 years with hundreds of teams in industries in all sectors. We also propose a framework to manage risks that considers those challenges and supports collaboration, agility, and continuous development. An implementation of that framework is then described in a tool that handles risks and mitigation actions associated with the development of multi-cloud applications. The methodology and the tool have been validated by a team of evaluators that were asked to consider its use in developing an urban smart mobility service and an airline flight scheduling system.

Compliance with the new European General Data Protection Regulation (Regulation (EU) 2016/679) and security assurance are currently two major challenges of Cloud-based systems. GDPR compliance implies both privacy and security mechanisms definition, enforcement and control, including evidence collection. This paper presents a novel DevOps framework aimed at supporting Cloud consumers in designing, deploying and operating (multi)Cloud systems that include the necessary privacy and security controls for ensuring transparency to end-users, third parties in service provision (if any) and law enforcement authorities. The framework relies on the risk-driven specification at design time of privacy and security level objectives in the system Service Level Agreement (SLA) and in their continuous monitoring and enforcement at runtime.