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.

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.

A dvanced computing systems generally contain many heterogeneous subsystems, each with a local perspective and goal set, which interconnect in changing network topologies. The subsystems must interact with each other and with humans in ways that are difficult to understand and predict while robustly maintaining performance, reliability, and security even with unforeseen dynamics, such as system failures or changing goals.To meet these stringent requirements, computational systemsranging from robot swarms and personal music devices to Web services and sensor networks-must achieve sophisticated autonomous behavior by adapting themselves at runtime and through learning processes that enable ongoing self-change. Managing tradeoffs among conflicting local and global goals at runtime requires considerable awareness of both the system's current state and its environment. Yet researchers have only recently begun to understand the implications of selfawareness principles and how to translate them into system engineering. Consequently, there is no general methodology for architecting self-aware systems or for comparing their self-awareness capabilities.To address this need, we examined how human selfawareness can serve as a source of inspiration for a new notion of computational self-awareness and associated self-expression, and we developed a general framework for describing a computing system's self-awareness properties. As part of this work, we created a reference architecture, which we used to derive architectural patterns for RESEARCH FEATURE

Abstract-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.

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Abstract. [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.

Service providers often use service level agreements (SLAs) to assure potential users of their services about the QoS to expect when they subscribe. In the cloud computing model, providers are required to continuously meet their SLA claims in the face of unanticipated failure of cloud resources. The dynamics of the cloud environment as attributed to its unpredictable mode of use and elasticity of its resources make human-driven solutions inefficient or sometimes infeasible. On the other hand, self-managed architectures have increasingly matured in their capacity to coordinate environments predominated by uncertainties. Thus making them a right fit for managing cloud-based systems. However, given the massive resource pool of the cloud, stateof-the-art centralised self-managed architectures are not scalable and are inherently brittle. Therefore, we propose a decentralised resource control mechanism which meets the unique robustness, scalability and resilience requirements of the cloud. The design of the mechanism gains inspiration from market control theory and a novel use of reputation metrics. In addition, an innovative self-managed cloud architecture has been designed based on the control mechanism. Early results from simulation studies show that the approach is feasible at reducing the SLA violations incurred by cloud providers.