Challenges of Engineer Autonomic Behaviors

A well recognized research challenge for future large-scale pervasive computing scenarios relates to the need of enforcing autonomic, self-managing and self-adaptive, behaviour, both at the level of the infrastructure and at that of its services.

Indeed, the increasing decentralization and dynamics of the current and emerging ICT scenarios (a variety of highly-distributed devices of an ephemeral and/or mobile nature, with users and developers capable of injecting new components and services at any time) make it impossible for developers and system managers to stay in the control loop and directly intervene in the system for configuration and maintenance activities (e.g., for configuring or personalizing new distributed devices or services, for fixing problems in existing devices and services, or for optimizing resource exploitation).

Accordingly, in the past few years, a large number of research proposals have been made, at both the infrastructural and service levels, to promote autonomic and adaptive behaviour in pervasive computing systems. However, in our opinion, most of the current proposals suffer from several limitations when dived in future scenarios.

First, a number of approaches propose “add-ons” to be integrated in existing frameworks as, e.g., in the case of those autonomic computing approach “à la IBM”, suggesting coupling sophisticated control loops to existing systems to support self-management. The result is often in an increased complexity of current frameworks, which definitely does not suit the characteristics and the need for lightness of pervasive scenarios.

Second, a number of other proposals suggest relying on light-weight and fully decentralized approaches, typically inspired by natural phenomena of self-organization and self-adaptation. However, most of these exploit the natural inspiration only for the implementation of specific algorithmic solutions or for realizing specific distributed services, either at the infrastructural or at the user level, rather than for attacking the issue of autonomic self-adaptation in a comprehensive way.

Third, and although an increasing number of researchers focuses on the social aspects of pervasive computing and on the provisioning of innovative social services, they do not properly account for the social level as an indistinguishable dimension of the overall pervasive computing fabric. That is, they do not account for the fact that users, other than simply consumers or producers of services, are de facto devices of the overall infrastructure, and contribute to it via human sensing, actuating, and computing capabilities, other than via an inherent strive for adaptation.

Based on the above considerations, our belief is that – rather than looking for one-of solutions to specific adaptation problems from specific limited viewpoints – there is need to deeply rethink the modelling and architecting of modern pervasive systems. As challenging as this can be, one should try to account in a foundational and holistic way for the overall complex needs of adaptation and autonomic behaviour of future pervasive computing systems. The final goal should be that of making such systems inherently capable of autonomic self-management and adaptation at the collective level, and having the distinction between infrastructural, services, and social levels, blur or vanish completely.

The achievement of the outlined broad goal calls for facing a number of specific challenging research issues, necessary to compose the global picture. These may include, among the others:

• Supporting Comprehensive Self-awareness. While the need for situation-awareness is already a recognized issue in pervasive computing, future scenarios will require autonomous adaptation activities to be driven by more comprehensive levels of awareness than traditionally enforced in context-aware computing models, where it is typically up to each component to access and digest the information it needs to take adaptation decisions. Such awareness models should encompass situations occurring at the many different levels of the system, as well as the strict locality of components, and be eventually able to provide components of the pervasive computing infrastructure with expressive and compact representations of complex multi-faceted situations, so as to effectively drive each and every activity of the components in a collectively coordinated way.
• Reconciling Top-Down vs Bottom Up Approaches. Along with the traditional “top-down” approaches to the engineering of pervasive computing systems, in which specific functionalities or behaviour are achieved by explicit design, “bottom-up” approaches are being proposed and adopted too (as we have already anticipated), to achieve functionalities via spontaneous self-organization, as it happens in natural systems. Most likely, both approaches will coexist in future pervasive computing scenarios, the former applied to the engineering of specific local functionalities, the latter applied to the engineering of large-scale behaviours (or simply emerge as a natural collective phenomena of the system). Thus, there will be need to understand the trade-offs between the power of top-down adaptation and bottom-up one, also by studying how the two approaches can coexist and possibly conflict in future systems, and possibly contributing in smoothing the tension between the two.
• Enforcing Decentralized Control. In large-scale systems, we should never forget that the existence and exploitation of phenomena of collective adaptation must necessarily come along with models and tools making it possible to control “by design” the overall behaviour of the pervasive systems and its sub-parts. Clearly, due to the inherent decentralization of the systems, such control tools too should make it possibly to direct the behaviours of the system in a decentralized way, and should be coupled by means to “measure” such behaviours in order to understand if the control is effective. The issue of defining sound measures for future pervasive computing scenarios can define in itself a challenging research area, given the size of the target scenario and the many – and often quite ill-defined – purposes it has to concurrently serve.

The need for laying out brand new foundations for the modelling and architecting of autonomic and self-adaptive pervasive computing systems, opens up a large number of fascinating and challenging research questions. In the ASCENS project, we are trying to understand how it is possible to identify a sound set of self-aware adaptation patterns for ensemble, and how such patterns can be used for engineering both top-down and bottom-up adaptation schemes, other than for enforcing in a decentralized way control over the global behaviour of ensembles.

In any case, the short list of challenges that we have provided above and that we are currently tackling in ASCENS is far from being exhaustive. For instance, it does not account for the many inter-disciplinary issues that the understanding of large-scale socio-technical organisms (as future pervasive computing systems will be) and their collective adaptive behaviours involve , calling from exploiting the modern lessons of applied psychology, sociology, social anthropology, and macro-economy, other than those of systemic biology, ecology and complexity science. Plenty of room for additional projects to complement ASCENS!