Catálogo de publicaciones - libros

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.

In the application economy, digital business initiatives are at the forefront of the growth strategy of many companies. Cloud based solutions offer a significant competitive advantage for both large companies and SMEs, leading to a rapid increase in the number of Cloud Service Providers (CSP). An important CSP driver is the improvement of consumers’ experience through digital platforms that allow users to access data and services from any location and through multiple channels with assured performance and availability.

The Cloud computing market encompasses an ever-growing number of providers offering a multitude of infrastructure-as-a-service (IaaS) and platform-as-a-service (PaaS) solutions. In order to exploit the peculiarities of each Cloud solution as well as to optimize performances, availability, and cost, an emergent need is to run and manage multi-Cloud applications [] (i.e., applications that can execute on multiple Cloud infrastructures and platforms).

Verifying that a software system shows certain non-functional properties is a primary concern for Cloud applications (In this chapter non-functional properties, QoS and non-functional requirements will be used interchangeably.). Given the heterogeneous technology offer and the related pricing models currently available in the Cloud market it is extremely complex to find the deployment that fits the application requirements, and provides the best Quality of Service (QoS) and cost trade-offs.

The Cloud brings velocity to the development and release process of applications, however software systems become complex, distributed on multiple clouds, dynamic and heterogeneous, leveraging both PaaS and IaaS resources. In this context, gathering feedback on the health and usage of services becomes really hard with traditional monitoring tools, since they were built for on-premise solutions offering uniform monitoring APIs and under the assumption that the application configuration evolves slowly over time.

Load balancing is an integral part of software systems that require to serve requests with multiple concurrent computing resources such as servers, clusters, network links, central processing units or disk drives.

Cloud offers the potential to support high scalability of applications. An increase in the application workload is typically handled by triggering the replication of its components so as to increase the application computational capability offered to users.

The main emphasis in this chapter is on the various supporting services needed to run the application. In the MODAClouds context, all services and resources involved in running and managing an application on a given Cloud provider comprise the runtime environment.

Nowadays, software systems are leveraging upon an aggregation of dedicated infrastructures and platforms, which leads to the design of large scale, distributed, and dynamic systems. The need to evolve and update such systems after delivery is often inevitable, for example, due to changes in the requirements, maintenance, or needs for advancing the quality of services such as scalability and performances.

DevOps [] is a recent trend in software engineering that bridges the gap between software development and operations, putting the developer in greater control of the operational environment in which the application runs. To support Quality-of-Service (QoS) analysis, the developer may rely on software performance models. However, to provide reliable estimates, the input parameters must be continuously updated and accurately estimated. Accurate estimation is challenging because some parameters are not explicitly tracked by log files requiring deep monitoring instrumentation that poses large overheads, unacceptable in production environments.