Abstract: The ever-growing complexity of software systems makes it increasingly challenging to foresee at design time all interactions between a system and its environment. Most self-adaptive systems trigger adaptations through operators that are statically confi gured for specifi c environment and system conditions. However, in the occurrence of uncertain conditions, self-adaptive decisions may not be eff ective and might lead to a disruption of the desired non-functional attributes. To address this, we propose an approach that improves the planning stage by predicting the outcome of each strategy. In detail, we automatically derive a stochastic model from a formal architecture description of the managed system with the changes imposed by each strategy. Such information is used to optimize the self-adaptation decisions to fulfill the desired quality goals. To assess the effectiveness of our approach we apply it to a cloud-based news system and predicted the reliability for each possible adaptation strategy. The results obtained from our approach are compared to a representative static planning algorithm as well as to an oracle that always makes the ideal decision. Experiments show that our method improves both availability and cost when compared to the static planning algorithm, while being close to the oracle. Our approach may therefore be used to optimize self-adaptation planning.

Abstract: Self-adaptive systems must decide which adaptations to apply and when. In reactive approaches, adaptations are chosen and executed after some issue in the system has been detected (e.g., unforeseen attacks or failures). In proactive approaches, predictions are used to prepare the system for some future event (e.g., traffic spikes during holidays). In both cases, the choice of adaptation is based on the estimated impact it will have on the system. Current decision-making approaches assume that the impact will be instantaneous, whereas it is common that adaptations take time to produce their impact. Ignoring this latency is problematic because adaptations may not achieve their effect in time for a predicted event. Furthermore, lower impact but quicker adaptations may be ignored altogether, even if over time the accrued impact is actually higher. In this article, we introduce a novel approach to choosing adaptations that considers these latencies. To show how this improves adaptation decisions, we use a two-pronged approach: (i) model checking of Stochastic Multiplayer Games (SMGs) enables us to understand best- and worst-case scenarios of optimal latency-aware and non-latency-aware adaptation without the need to develop specific adaptation algorithms. However, since SMGs do not provide an algorithm to make choices at runtime, we propose a (ii) latency-aware adaptation algorithm to make decisions at runtime. Simulations are used to explore more detailed adaptation behavior and to check if the performance of the algorithm falls within the bounds predicted by SMGs. Our results show that latency awareness improves adaptation outcomes and also allows a larger set of adaptations to be exploited.

Abstract: Self-adaptive systems have the ability to adapt their behavior to dynamic operating conditions. In reaction to changes in the environment, these systems determine the appropriate corrective actions based in part on information about which action will have the best impact on the system. Existing models used to describe the impact of adaptations are either unable to capture the underlying uncertainty and variability of such dynamic environments, or are not compositional and described at a level of abstraction too low to scale in terms of specification effort required for non-trivial systems. In this paper, we address these shortcomings by describing an approach to the specification of impact models based on architectural system descriptions, which at the same time allows us to represent both variability and uncertainty in the outcome of adaptations, hence improving the selection of the best corrective action. The core of our approach is a language equipped with a formal semantics defined in terms of Discrete Time Markov Chains that enables us to describe both the impact of adaptation tactics, as well as the assumptions about the environment. To validate our approach, we show how employing our language can improve the accuracy of predictions used for decision-making in the Rainbow framework for architecture-based self-adaptation.

Abstract: Software systems are increasingly called upon to autonomously manage their goals in changing contexts and environments, and under evolving requirements. In some circumstances, autonomous systems cannot be fully automated but instead cooperate with human operators to maintain and adapt themselves. Furthermore, there are times when a choice should be made between doing a manual or automated repair. Involving operators in self-adaptation should itself be adaptive, and consider aspects such as the training, attention, and ability of operators. Not only do these aspects change from person to person, but they may change with the same person. These aspects make the choice of whether to involve humans non-obvious. Self-adaptive systems should trade-off whether to involve operators, taking these aspects into consideration along with other business qualities it is attempting to achieve. In this chapter, we identify the various roles that operators can perform in cooperating with self-adaptive systems. We focus on humans as effectors - doing tasks which are difficult or infeasible to automate. We describe how we modified our self-adaptive framework, Rainbow, to involve operators in this way, which involved choosing suitable human models and integrating them into the existing utility trade-off decision models of Rainbow. We use probabilistic modeling and quantitative verification to analyze the trade-offs of involving humans in adaptation, and complement our study with experiments to show how different business preferences and modalities of human involvement may result in different outcomes.

Abstract: Software architecture modeling is important for analyzing system quality attributes, particularly security. However, such analyses often assume that the architecture is completely known in advance. In many modern domains, especially those that use plugin-based frameworks, it is not possible to have such a complete model because the software system continuously changes. The Android mobile operating system is one such framework, where users can install and uninstall apps at run time. We need ways to model and analyze such architectures that strike a balance between supporting the dynamism of the underlying platforms and enabling analysis, particularly throughout a system�s lifetime. In this paper, we describe a formal architecture style that captures the modifiable architectures of Android systems, and that supports security analysis as a system evolves. We illustrate the use of the style with two security analyses: a predicatebased approach defined over architectural structure that can detect some common security vulnerabilities, and inter-app permission leakage determined by model checking. We also show how the evolving architecture of an Android device can be obtained by analysis of the apps on a device, and provide some performance evaluation that indicates that the architecture can be amenable for use throughout the system�s lifetime.

Abstract: Run-time generation of adaptation plans is a powerful mechanism that helps a self-adaptive system to meet its goals in a dynamically changing environment. In the past, researchers have demonstrated successful use of various automated planning techniques to generate adaptation plans at run time. However, for a planning technique, there is often a trade-off between timeliness and optimality of the solution. For some self-adaptive systems, ideally, one would like to have a planning approach that is both quick and finds an optimal adaptation plan. To find the right balance between these conflicting requirements, this paper introduces a hybrid planning approach that combines more than one planner to obtain the benefits of each. In this paper, to instantiate a hybrid planner we combine deterministic planning with Markov Decision Process (MDP) planning to obtain the best of both worlds: deterministic planning provides plans quickly when timeliness is critical, while allowing MDP planning to generate optimal plans when the system has sufficient time to do so. We validate the hybrid planning approach using a realistic workload pattern in a simulated cloud-based self-adaptive system.

Abstract: Proactive latency-aware adaptation is an approach for self-adaptive systems that improves over reactive adaptation by considering both the current and anticipated adaptation needs of the system, and taking into account the latency of adaptation tactics so that they can be started with the necessary lead time. Making an adaptation decision with these characteristics requires solving an optimization problem to select the adaptation path that maximizes an objective function over a finite look-ahead horizon. Since this is a problem of selecting adaptation actions in the context of the probabilistic behavior of the environment, Markov decision processes (MDP) are a suitable approach. However, given all the possible interactions between the different and possibly concurrent adaptation tactics, the system, and the environment, constructing the MDP is a complex task. Probabilistic model checking can be used to deal with this problem since it takes as input a formal specification of the stochastic system, which is internally translated into an MDP, and solved. One drawback of this solution is that the MDP has to be constructed every time an adaptation decision has to be made to incorporate the latest predictions of the environment behavior. In this paper we present an approach that eliminates that run-time overhead by constructing most of the MDP offline, also using formal specification. At run time, the adaptation decision is made by solving the MDP through stochastic dynamic programming, weaving in the stochastic environment model as the solution is computed. Our experimental results show that this approach reduces the adaptation decision time by an order of magnitude compared to the probabilistic model checking approach, while producing the same results.

Abstract: Planning in CPSs requires temporal reasoning to handle the dynamics of the environment, including human behavior, as well as temporal constraints on system goals and durations of actions that systems and human actors may take. The discrete abstraction of time in a state space planning should have a time sampling parameter value that satisfi es some relation to achieve a certain precision. In particular, the sampling period should be small enough to allow the dynamics of the problem domain to be modeled with sufficient precision. Meanwhile, in many cases, events in the far future (relative to the sampling period) may be relevant to the decision making earlier in the planning timeline; therefore, a longer planning look-ahead horizon can yield a closer-to-optimal plan. Unfortunately, planning with a uniform fine-grained discrete abstraction of time and a long look-ahead horizon is typically computationally infeasible. In this paper, we propose a multiscale temporal planning approach - formulated as MDP planning - to preserve the required time fidelity of the problem domain and at the same time approximate a globally optimal plan. We illustrate our approach in a middleware used to monitor large sensor networks.

Abstract: The term big data arose under the explosive increase of global data as a technology that is able to store and process big and varied volumes of data, providing both enterprises and science with deep insights over its clients/experiments. Cloud computing provides a reliable, fault-tolerant, available and scalable environment to harbour big data distributed management systems. Within the context of this paper we present an overview of both technologies and cases of success when integrating big data and cloud frameworks. Although big data solves much of our current problems it still presents some gaps and issues that raise concern and need improvement. Security, privacy, scalability, data governance policies, data heterogeneity, disaster recovery mechanisms, and other challenges are yet to be addressed. Other concerns are related to cloud computing and its ability to deal with exabytes of information or address exaflop computing efficiently. This paper presents an overview of both cloud and big data technologies describing the current issues with these technologies.

Abstract: In an organization, the interactions users have with software leaves patterns or traces of the parts of the systems accessed. These interactions can be associated with the underlying software architecture. The first step in detecting problems like insider threat is to detect those traces that are anomalous. In this paper, we present a method to find anomalous users leveraging these interaction traces, categorized by user roles. We propose a model based approach to cluster user sequences and find outliers. Such a technique could be useful in finding potentially anomalous users, insiders, or compromised accounts.We show that the approach works on a simulation of a large scale system based on and Amazon Web application style.

Abstract: Modern frameworks are required to be extendable as well as secure. However, these two qualities are often at odds. In this poster we describe an approach that uses a combination of static analysis and run-time management, based on software architecture models, that can improve security while maintaining framework extendability. We implement a prototype of the approach for the Android platform. Static analysis identifi es the architecture and communication patterns among the collection of apps on an Android device and which communications might be vulnerable to attack. Run-time mechanisms monitor these potentially vulnerable communication patterns, and adapt the system to either deny them, request explicit approval from the user, or allow them.

Abstract: Recent research in embedded and cyber-physical systems has developed theories and tools for integration of heterogeneous components and models. These efforts, although important, are insufficient for high-quality and error-free systems integration since inconsistencies between system elements may stem from factors not directly represented in models (e.g., analysis tools and expert disagreements). Therefore, we need to broaden our perspective on integration, and devise approaches in three novel directions of integration: modeling methods, data sets, and humans. This paper summarizes the latest advances, and discusses those directions and associated challenges in integration for cyber-physical systems.