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Cloud computing make it possible to flexibly procure, scale, and release computational resources on demand in response to workload changes. Stakeholders in business and academia are increasingly exploring cloud deployment options for their critical applications. One open problem is that service level agreements (SLAs) in the cloud ecosystem are yet to mature to a state where critical applications can be reliably deployed in clouds. This article systematically surveys the landscape of SLA-based cloud research to understand the state of the art and identify open problems. The survey is particularly aimed at the resource allocation phase of the SLA life cycle while highlighting implications on other phases. Results indicate that (i) minimal number of SLA parameters are accounted for in most studies; (ii) heuristics, policies, and optimisation are the most commonly used techniques for resource allocation; and (iii) the monitor-analysis-plan-execute (MAPE) architecture style is predominant in autonomic cloud systems. The results contribute to the fundamentals of engineering cloud SLA and their autonomic management, motivating further research and industrial-oriented solutions.

Contemporary software systems are becoming increasingly large, heterogeneous, and decentralised. They operate in dynamic environments and their architectures exhibit complex trade-offs across dimensions of goals, time, and interaction, which emerges internally from the systems and externally from their environment. This gives rise to the vision of self-aware architecture, where design decisions and execution strategies for these concerns are dynamically analysed and seamlessly managed at run-time. Drawing on the concept of self-awareness from psychology, this paper extends the foundation of software architecture styles for self-adaptive systems to arrive at a new principled approach for architecting self-aware systems. We demonstrate the added value and applicability of the approach in the context of service provisioning to cloud-reliant service-based applications.

[Context and motivation] Self-adaptation empowers systems with the capability to meet stakeholders' requirements in a dynamic environment. Such systems autonomously monitor changes and events which drive adaptation decisions at runtime. Social Adaptation is a recent kind of requirements-driven adaptation which enables users to give a runtime feedback on the success and quality of a system's configurations in reaching their requirements. The system analyses users' feedback, infers their collective judgement and then uses it to shape its adaptation decisions. [Question/problem] However, there is still a lack of engineering mechanisms to guarantee a correct conduction of Social Adaptation. [Principal ideas/results] In this paper, we conduct a two-phase Expert Survey to identify core benefits, domain areas and challenges for Social Adaptation. [Contribution] Our findings provide practitioners and researchers in adaptive systems engineering with insights on this emerging role of users, or the crowd, and stimulate future research to solve the open problems in this area.

Many of the problems facing providers and users of Cloud-based systems, in terms of maximizing Quality-of-Service (QoS) satisfaction, can be studied using theories in microeconomics. Specifically, the concept of market-based control provides tools that can be used to design economically and computationally efficient Cloud software architectures. This chapter surveys both domains and presents some of the underlying problems and opportunities in the interesting crossbreed of economics and Cloud computing. The dynamic resource allocation problem is used as an example to demonstrate the added value of this approach. Observations from simulation studies reveal the usefulness of the posted offer market model as a viable mechanism for orchestrating the interaction of components in a Cloud software architecture. The chapter concludes with a reflection on open problems that need to be addressed to move the area forward.

In this chapter, the authors motivate the need for a systematic approach to cloud adoption from the risk perspective. The enormous potential of cloud computing for improved and cost-effective service delivery for commercial and academic purposes has generated unprecedented interest in its adoption. However, a potential cloud user faces numerous risks regarding service requirements, cost implications of failure, and uncertainty about cloud providers' ability to meet service level agreements. Hence, the authors consider two perspectives of a case study to identify risks associated with cloud adoption. They propose a risk management framework based on the principle of GORE (Goal-Oriented Requirements Engineering). In this approach, they liken risks to obstacles encountered while realising cloud user goals, therefore proposing cloud-specific obstacle resolution tactics for mitigating identified risks. The proposed framework shows benefits by providing a principled engineering approach to cloud adoption and empowering stakeholders with tactics for resolving risks when adopting the cloud.

Modern compute systems continue to evolve towards increasingly complex, heterogeneous and distributed architectures. At the same time, functionality and performance are no longer the only aspects when developing applications for such systems, and additional concerns such as flexibility, power efficiency, resource usage, reliability and cost are becoming increasingly important. This does not only raise the question of how to efficiently develop applications for such systems, but also how to cope with dynamic changes in the application behaviour or the system environment. The EPiCS Project aims to address these aspects through exploring self-awareness and self-expression. Self-awareness allows systems and applications to gather and maintain information about their current state and environment, and reason about their behaviour. Self-expression enables systems to adapt their behaviour autonomously to changing conditions. Innovations in EPiCS are based on systematic integration of research in concepts and foundations, customisable hardware/software platforms and operating systems, and self-aware networking and middleware infrastructure. The developed technologies are validated in three application domains: computational finance, distributed smart cameras and interactive mobile media systems.

The continuous evolution and unpredictability underlying service-based systems leads to difficulties in making exact QoS claims about the dependability of architectures interfacing with them. Hence, there is a growing need for new methods to evaluate the dependability of architectures interfacing with such environments. This paper presents a method for evaluating the security quality attribute of architectures in service-based systems. The proposed method combines some properties of the Architectural Tradeoff Analysis Method (ATAM) and security testing using Implied Scenario. In particular, the scenario elicitation process of ATAM is improved by utilising Implied Scenario technique to generate scenarios which may be undetected using plain ATAM. An industrial case study of a problem related to securing data at the Software-as-a-Service layer on Force.com Cloud platform is adopted to validate the new method. The results indicate that our method found four additional security scenarios beyond the plain ATAM, resulting in four new risks and two new tradeoff points.