With the increasing dependence on software systems, their longevity is becoming a pressing need. Stability is envisioned as a primary property to achieve longevity. Stability has been defined and treated in many different ways in the literature. We conduct a systematic literature review to analyse the state-of-the-art related to stability as a software property. We formulate a taxonomy for characterising the notion, analyse the definitions found in the literature, and present research studies dealing with stability. Also, as architecture is one of the software artefacts with profound effects throughout the software lifecycle, we focus on software engineering practices for realising architectural stability. The analysis results show a wide variation in dimensions when dealing with stability. The state-of-the-art indicates the need for a shift towards a multi-dimensional concept that could cope with runtime dynamics and emerging software paradigms. More research efforts should be directed toward the identified gaps. The presented taxonomy and analysis of the literature aim to help the research community in consolidating the existing research efforts and deriving future developments.

Background : Self-awareness has been recently receiving attention in computing systems for enriching autonomous software systems operating in dynamic environments. Objective : We aim to investigate the adoption of computational self-awareness concepts in autonomic software systems and motivate future research directions on self-awareness and related problems. Method : We conducted a systemic literature review to compile the studies related to the adoption of self-awareness in software engineering and explore how self-awareness is engineered and incorporated in software systems. From 865 studies, 74 studies have been selected as primary studies. We have analysed the studies from multiple perspectives, such as motivation, inspiration, and engineering approaches, among others. Results : Results have shown that self-awareness has been used to enable self-adaptation in systems that exhibit uncertain and dynamic behaviour. Though there have been recent attempts to define and engineer self-awareness in software engineering, there is no consensus on the definition of self-awareness. Also, the distinction between self-aware and self-adaptive systems has not been systematically treated. Conclusions : Our survey reveals that self-awareness for software systems is still a formative field and that there is growing attention to incorporate self-awareness for better reasoning about the adaptation decision in autonomic systems. Many pending issues and open problems outline possible research directions.

Given the elasticity, on-demand nature, and runtime dynamics of the cloud, a stable self-adaptive architecture should keep the fulfilment of Quality of Service objectives stable, while performing stable adaptations that converge towards these objectives. The dynamic management and selection of architectural tactics, as adaptation mechanisms, shall be in the heart of the adaptation process, as being essential for effective and stable adaptations. This calls for measuring the impact of tactics on the stability of inter-related quality attributes during run-time. In this paper, we introduce a Markovian-based analytical model for dynamically assessing the impact of tactics on the stability behaviour of self-adaptive cloud architectures. The model also employs self-awareness capabilities for betterinforming the selection of optimal tactics configurations leading to stability. Experimental evaluations have shown the accuracy and efficiency of the model in measuring and predicting the impact of tactics on stabilising the Quality of Service provision and the adaptation process.

Architecture-based self-adaptation has been recognised as one of the prominent ways to design autonomic systems, where self-manageable architectures tend to achieve the required level of dynamicity and compliance with the continual changing in QoS requirements during run-time. Self-awareness and self-expression have recently emerged as promising architectural concepts in the field of self-adaptive software. Self-aware architecture patterns are envisioned as enabler for self-adaptation, but they tend to provide limited support for the QoS run-time requirements. While the research community has developed in architecture quality management, patterns and tactics, addressing quality attributes in self-aware architectures has not been tackled yet. In this paper, we aim to provide quality-driven architectural patterns for emerging class of architecture enabled by the principles of self-awareness. We report on the feasibility of correlating QoS tactics with self-aware capabilities to better respond to QoS run-time requirements and trade-offs. We describe novel extensions which make the correlation between QoS tactics and self-awareness explicit. We quantitatively evaluate the feasibility, generality and fitness of the proposed approach, as well as its potential applicability to self-aware architectures. Though the proposed extensions can potentially benefit architectures which leverage on self-awareness, we use the case of cloud auto-scaling architecture.

Stakeholders and organisations are increasingly looking for long-lived software. As architectures have a profound effect on the operational life-time of the software and the quality of the service provision, architectural stability could be considered a primary criterion towards achieving the long-livety of the software. Architectural stability is envisioned as the next step in quality attributes, combining many inter-related qualities. This research suggests the notion of behavioural stability as a primary criterion for evaluating whether the architecture maintains achieving the expected quality attributes, maintaining architecture robustness, and evaluating how well the architecture accommodates run-time evolutionary changes. The research investigates the notion of architecture stability at run-time in the context of self-adaptive software architectures. We expect to define, characterise and analyse this intuitive concept, as well as identify the consequent trade-offs to be dynamically managed and enhance the self-adaptation process for a long-lived software.

Cloud federation allows individual cloud providers dynamically collaborate to offer services to their end-users with the Quality of Service (QoS) targets agreed in the Service Level Agreements (SLA). However, the current federated cloud models are not QoS-oriented or SLA-aware. This paper proposes a QoS-oriented federated cloud computing framework where multiple independent cloud providers can cooperate seamlessly to provide scalable QoS-assured services and discusses a high level architecture of the federation components. The distinct features of the proposed federation framework is its QoS-orientation that can trigger the on-demand resource provisioning across multiple providers, hence helping to maximize QoS targets and resources usage, eliminate SLA violations and enhance SLA formalization.

Computer networking courses are in deep need of efficient educational approaches and tools. The classical physical labs are burdened by the equipments' initial costs and maintenance with a frequent upgrading demanded by ever changing technology. The use of Network Simulators, like ns2, ns3, OPNET, and others, are also overloaded by a group of technical limitations and hence unreliable results. In this paper, we address the "Junosphere" tool, developed by Juniper Networks, in which network planning, modeling, designing, testing, examining "what-if" scenarios and training exercises could be conducted in a risk free environment. Hereby, this paper addresses solutions provided by Junosphere in the educational perspective, and goes through their features and components.