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The capability of dynamically adapting to distinct runtime conditions is an important issue when designing distributed systems where negotiated quality of service (QoS) cannot always be delivered between processes. Providing fault tolerance for such dynamic environments is a challenging task. Considering such a context, this paper proposes an adaptive programming model for fault-tolerant distributed computing, which provides upper-layer applications with process state information according to the current system synchrony (or QoS). The underlying system model is hybrid, composed by a synchronous part (where there are time bounds on processing speed and message delay) and an asynchronous part (where there is no time bound). However, such a composition can vary over time, and, in particular, the system may become totally asynchronous (e.g., when the underlying system QoS degrade) or totally synchronous. Moreover, processes are not required to share the same view of the system synchrony at a given time. To illustrate what can be done in this programming model and how to use it, the consensus problem is taken as a benchmark problem. This paper also presents an implementation of the model that relies on a negotiated quality of service (QoS) for communication channels.

A number of approaches for endowing systems with self-adaptive behavior have been proposed over the past years. Among such efforts, architecture-centric solutions with explicit representation of feedback loops have currently been advocated as a promising research landscape. Although noteworthy results have been achieved on some fronts, the lack of systematic representations of architectural knowledge and effective support for well-informed trade-off decisions still poses significant challenges when designing modern self-adaptive systems. In this paper, we present a systematic and flexible representation of design dimensions related to feedback control concerns, a set of metrics which estimate quality attributes of resulting automated designs, and a search-based approach to find out a set of Pareto-optimal candidate architectures. The proposed approach has been fully implemented in a supporting tool and a case study with a selfadaptive cloud computing environment has been undertaken.The results indicate that our approach effectively captures the most prominent degrees of freedom when designing self-adaptive systems, helps to elicit effective subtle designs, and provides useful support for early analysis of trade-off decisions.

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