Self-Aware Computing Systems

pp 191-235


Architectures for Collective Self-aware Computing Systems

  • Ada DiaconescuAffiliated withTelecom Paris Tech Email author 
  • , Kirstie L. BellmanAffiliated withThe Aerospace Corporation
  • , Lukas EsterleAffiliated withVienna University of Technology
  • , Holger GieseAffiliated withHasso-Plattner-Institut
  • , Sebastian GötzAffiliated withUniversity of Technology Dresden
  • , Peter LewisAffiliated withAston Lab for Intelligent Collectives Engineering (ALICE), Aston University
  • , Andrea ZismanAffiliated withThe Open University

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This chapter aims to discuss the architectural aspects relevant to collectives of self-aware computing systems. Here, collectives consist of several self-aware computing systems that interact in some way. Their interactions may, potentially, lead to the formation of a self-aware collective of systems. Hence, the chapter defines different types of interactions that can link systems into a collective and then discusses the conditions under which self-awareness can be achieved within such collectives. Furthermore, the chapter identifies some of the most relevant architectural concerns that occur when linking multiple self-aware systems into a (self-aware) collective and defines these in the form of a generic meta-architecture for collectives of self-aware systems. Architectural concerns can represent both static and dynamic aspects of system collectives. Static concerns include the self-awareness levels of systems in a collective; the system interrelations, such as competition and cooperation; and several organisation patterns for systems in a collective, such as hierarchy or peer-to-peer designs. Dynamic concerns address changes that may occur over time, with respect to the above-mentioned aspects, based on the experience and learning of systems within the collective. More advanced topics discuss the manner in which the creation of collectives from interrelated systems can be applied recursively, adopting different architectural choices and combinations at each level, and potentially leading to a wide range of variations in the resulting self-awareness characteristics. The chapter concludes by indicating the main contributions and targeted beneficiaries of this chapter and points to the most important challenges to address in future research.