Market-awareness in service-based systems

Service-based systems are applications built by composing pre-existing services. During design time and according to the specifications, a set of services is selected. Both, service providers and consumers exist in a service market that is constantly changing. Service providers continuously change their quality of services (QoS), and service consumers can update their specifications according to what the market is offering. Therefore, during runtime, the services are periodically and manually checked to verify if they still satisfy the specifications. Unfortunately, humans are overwhelmed with the degree of changes exhibited by the service market. Consequently, verification of the compliance specification and execution of the corresponding adaptations when deviations are detected cannot be carried out in a manual fashion. In this work, we propose a framework to enable online awareness of changes in the service market in both consumers and providers by representing them as active software agents. At runtime, consumer agents concretize QoS specifications according to the available market knowledge. Services agents are collectively aware of themselves and of the consumers' requests. Moreover, they can create and maintain virtual organizations to react actively to demands that come from the market. In this paper we show preliminary results that allow us to conclude that the creation and adaptation of service-based systems can be carried out by a self-organized service market system. © 2012 IEEE.

Mitigating the obsolescence of quality specifications models in service-based systems

Requirements-aware systems address the need to reason about uncertainty at runtime to support adaptation decisions, by representing quality of services (QoS) requirements for service-based systems (SBS) with precise values in run-time queryable model specification. However, current approaches do not support updating of the specification to reflect changes in the service market, like newly available services or improved QoS of existing ones. Thus, even if the specification models reflect design-time acceptable requirements they may become obsolete and miss opportunities for system improvement by self-adaptation. This articles proposes to distinguish "abstract" and "concrete" specification models: the former consists of linguistic variables (e.g. "fast") agreed upon at design time, and the latter consists of precise numeric values (e.g. "2ms") that are dynamically calculated at run-time, thus incorporating up-to-date QoS information. If and when freshly calculated concrete specifications are not satisfied anymore by the current service configuration, an adaptation is triggered. The approach was validated using four simulated SBS that use services from a previously published, real-world dataset; in all cases, the system was able to detect unsatisfied requirements at run-time and trigger suitable adaptations. Ongoing work focuses on policies to determine recalculation of specifications. This approach will allow engineers to build SBS that can be protected against market-caused obsolescence of their requirements specifications. © 2012 IEEE.

Run-time model evaluation for requirements model-driven self-adaptation

A self-adaptive system adjusts its configuration to tolerate changes in its operating environment. To date, requirements modeling methodologies for self-adaptive systems have necessitated analysis of all potential system configurations, and the circumstances under which each is to be adopted. We argue that, by explicitly capturing and modelling uncertainty in the operating environment, and by verifying and analysing this model at runtime, it is possible for a system to adapt to tolerate some conditions that were not fully considered at design time. We showcase in this paper our tools and research results. © 2012 IEEE.

Satisfying requirements for pervasive service compositions

Pervasive environments are characterised by highly heterogeneous services and mobile devices with dynamic availability. Approaches such as that proposed by the Connect project provide means to enable such systems to be discovered and composed, through mediation where necessary. As services appear and disappear, the set of feasible compositions changes. In such a pervasive environment, a designer encounters two related challenges: what goals it is reasonable to pursue in the current context and how to use the services presently available to achieve his goals. This paper proposes an approach to design service compositions, facilitating an interactive process to find the trade-off between the possible and the desirable. Following our approach, the system finds at runtime, where possible, compositions related to the developer's requirements. This process can realise the intent the developer specifies at design time, taking into account the services available at runtime, without a prohibitive level of pre-specification, inappropriate for such dynamic environments. © 2012 ACM.

Self-explanation in adaptive systems

The behaviour of self adaptive systems can be emergent. The difficulty in predicting the system's behaviour means that there is scope for the system to surprise its customers and its developers. Because its behaviour is emergent, a self-adaptive system needs to garner confidence in its customers and it needs to resolve any surprise on the part of the developer during testing and mainteinance. We believe that these two functions can only be achieved if a self-adaptive system is also capable of self-explanation. We argue a self-adaptive system's behaviour needs to be explained in terms of satisfaction of its requirements. Since self-adaptive system requirements may themselves be emergent, a means needs to be found to explain the current behaviour of the system and the reasons that brought that behaviour about. We propose the use of goal-based models during runtime to offer self-explanation of how a system is meeting its requirements, and why the means of meeting these were chosen. We discuss the results of early experiments in self-explanation, and set out future work. © 2012 C.E.S.A.M.E.S.

Run-time resolution of uncertainty

Requirements awareness should help optimize requirements satisfaction when factors that were uncertain at design time are resolved at runtime. We use the notion of claims to model assumptions that cannot be verified with confidence at design time. By monitoring claims at runtime, their veracity can be tested. If falsified, the effect of claim negation can be propagated to the system's goal model and an alternative means of goal realization selected automatically, allowing the dynamic adaptation of the system to the prevailing environmental context. © 2011 IEEE.

Foreword:first workshop requirements@run.time

The first edition of the Workshop requirements@run.time was held at the Eighteenth International Conference on Requirements Engineering (RE 2010) in the city of Sydney, NSW, Australia on the 28th of September 2010. It was organized by Pete Sawyer, Jon Whittle, Nelly Bencomo, Daniel Berry, and Anthony Finkelstein. This foreword presents a digest of the presentations and discussions that took place during the workshop. © 2010 IEEE.

On the use of software models during software execution

Increasingly software systems are required to survive variations in their execution environment without or with only little human intervention. Such systems are called "eternal software systems". In contrast to the traditional view of development and execution as separate cycles, these modern software systems should not present such a separation. Research in MDE has been primarily concerned with the use of models during the first cycle or development (i.e. during the design, implementation, and deployment) and has shown excellent results. In this paper the author argues that an eternal software system must have a first-class representation of itself available to enable change. These runtime representations (or runtime models) will depend on the kind of dynamic changes that we want to make available during execution or on the kind of analysis we want the system to support. Hence, different models can be conceived. Self-representation inevitably implies the use of reflection. In this paper the author briefly summarizes research that supports the use of runtime models, and points out different issues and research questions. © 2009 IEEE.

Dynamic decision networks for decision-making in self-adaptive systems:a case study

Bayesian decision theory is increasingly applied to support decision-making processes under environmental variability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. However, in the area of software engineering and speci?cally in the area of self-adaptive systems (SASs), little progress has been made in the application of Bayesian decision theory. We believe that techniques based on Bayesian Networks (BNs) are useful for systems that dynamically adapt themselves at runtime to a changing environment, which is usually uncertain. In this paper, we discuss the case for the use of BNs, speci?cally Dynamic Decision Networks (DDNs), to support the decision-making of self-adaptive systems. We present how such a probabilistic model can be used to support the decision making in SASs and justify its applicability. We have applied our DDN-based approach to the case of an adaptive remote data mirroring system. We discuss results, implications and potential bene?ts of the DDN to enhance the development and operation of self-adaptive systems, by providing mechanisms to cope with uncertainty and automatically make the best decision.

A socio-economic approach to online vision graph generation and handover in distributed smart camera networks

In this paper we propose an approach based on self-interested autonomous cameras, which exchange responsibility for tracking objects in a market mechanism, in order to maximise their own utility. A novel ant-colony inspired mechanism is used to grow the vision graph during runtime, which may then be used to optimise communication between cameras. The key benefits of our completely decentralised approach are on the one hand generating the vision graph online which permits the addition and removal cameras to the network during runtime and on the other hand relying only on local information, increasing the robustness of the system. Since our market-based approach does not rely on a priori topology information, the need for any multi-camera calibration can be avoided. © 2011 IEEE.

Socio-economic vision graph generation and handover in distributed smart camera networks

In this article we present an approach to object tracking handover in a network of smart cameras, based on self-interested autonomous agents, which exchange responsibility for tracking objects in a market mechanism, in order to maximise their own utility. A novel ant-colony inspired mechanism is used to learn the vision graph, that is, the camera neighbourhood relations, during runtime, which may then be used to optimise communication between cameras. The key benefits of our completely decentralised approach are on the one hand generating the vision graph online, enabling efficient deployment in unknown scenarios and camera network topologies, and on the other hand relying only on local information, increasing the robustness of the system. Since our market-based approach does not rely on a priori topology information, the need for any multicamera calibration can be avoided. We have evaluated our approach both in a simulation study and in network of real distributed smart cameras.

Improved adaptivity and robustness in decentralised multi-camera networks

In this paper we present increased adaptivity and robustness in distributed object tracking by multi-camera networks using a socio-economic mechanism for learning the vision graph. To build-up the vision graph autonomously within a distributed smart-camera network, we use an ant-colony inspired mechanism, which exchanges responsibility for tracking objects using Vickrey auctions. Employing the learnt vision graph allows the system to optimise its communication continuously. Since distributed smart camera networks are prone to uncertainties in individual cameras, such as failures or changes in extrinsic parameters, the vision graph should be sufficiently robust and adaptable during runtime to enable seamless tracking and optimised communication. To better reflect real smart-camera platforms and networks, we consider that communication and handover are not instantaneous, and that cameras may be added, removed or their properties changed during runtime. Using our dynamic socio-economic approach, the network is able to continue tracking objects well, despite all these uncertainties, and in some cases even with improved performance. This demonstrates the adaptivity and robustness of our approach.

Resource-aware configuration in smart camera networks

A recent trend in smart camera networks is that they are able to modify the functionality during runtime to better reflect changes in the observed scenes and in the specified monitoring tasks. In this paper we focus on different configuration methods for such networks. A configuration is given by three components: (i) a description of the camera nodes, (ii) a specification of the area of interest by means of observation points and the associated monitoring activities, and (iii) a description of the analysis tasks. We introduce centralized, distributed and proprioceptive configuration methods and compare their properties and performance. © 2012 IEEE.

A world full of surprises:Bayesian theory of surprise to quantify degrees of uncertainty

In the specific area of software engineering (SE) for self-adaptive systems (SASs) there is a growing research awareness about the synergy between SE and artificial intelligence (AI). However, just few significant results have been published so far. In this paper, we propose a novel and formal Bayesian definition of surprise as the basis for quantitative analysis to measure degrees of uncertainty and deviations of self-adaptive systems from normal behavior. A surprise measures how observed data affects the models or assumptions of the world during runtime. The key idea is that a "surprising" event can be defined as one that causes a large divergence between the belief distributions prior to and posterior to the event occurring. In such a case the system may decide either to adapt accordingly or to flag that an abnormal situation is happening. In this paper, we discuss possible applications of Bayesian theory of surprise for the case of self-adaptive systems using Bayesian dynamic decision networks. Copyright © 2014 ACM.

Models@run.time:foundations, applications, and roadmaps

Traditionally, research on model-driven engineering (MDE) has mainly focused on the use of models at the design, implementation, and verification stages of development. This work has produced relatively mature techniques and tools that are currently being used in industry and academia. However, software models also have the potential to be used at runtime, to monitor and verify particular aspects of runtime behavior, and to implement self-* capabilities (e.g., adaptation technologies used in self-healing, self-managing, self-optimizing systems). A key benefit of using models at runtime is that they can provide a richer semantic base for runtime decision-making related to runtime system concerns associated with autonomic and adaptive systems. This book is one of the outcomes of the Dagstuhl Seminar 11481 on models@run.time held in November/December 2011, discussing foundations, techniques, mechanisms, state of the art, research challenges, and applications for the use of runtime models. The book comprises four research roadmaps, written by the original participants of the Dagstuhl Seminar over the course of two years following the seminar, and seven research papers from experts in the area. The roadmap papers provide insights to key features of the use of runtime models and identify the following research challenges: the need for a reference architecture, uncertainty tackled by runtime models, mechanisms for leveraging runtime models for self-adaptive software, and the use of models at runtime to address assurance for self-adaptive systems.