2,336,786 research outputs found
Large-scale Complex IT Systems
This paper explores the issues around the construction of large-scale complex
systems which are built as 'systems of systems' and suggests that there are
fundamental reasons, derived from the inherent complexity in these systems, why
our current software engineering methods and techniques cannot be scaled up to
cope with the engineering challenges of constructing such systems. It then goes
on to propose a research and education agenda for software engineering that
identifies the major challenges and issues in the development of large-scale
complex, software-intensive systems. Central to this is the notion that we
cannot separate software from the socio-technical environment in which it is
used.Comment: 12 pages, 2 figure
Architectural implications for context adaptive smart spaces
Buildings and spaces are complex entities containing complex social structures and interactions. A smart space is a composite of the users that inhabit it, the IT infrastructure that supports it, and the sensors and appliances that service it. Rather than separating the IT from the buildings and from the appliances that inhabit them and treating them as separate systems, pervasive computing combines them and allows them to interact. We outline a reactive context architecture that supports this vision of integrated smart spaces and explore some implications for building large-scale pervasive systems
Deterministic hierarchical networks
It has been shown that many networks associated with complex systems are
small-world (they have both a large local clustering coefficient and a small
diameter) and they are also scale-free (the degrees are distributed according
to a power law). Moreover, these networks are very often hierarchical, as they
describe the modularity of the systems that are modeled. Most of the studies
for complex networks are based on stochastic methods. However, a deterministic
method, with an exact determination of the main relevant parameters of the
networks, has proven useful. Indeed, this approach complements and enhances the
probabilistic and simulation techniques and, therefore, it provides a better
understanding of the systems modeled. In this paper we find the radius,
diameter, clustering coefficient and degree distribution of a generic family of
deterministic hierarchical small-world scale-free networks that has been
considered for modeling real-life complex systems
Prospects for large-scale financial systems simulation
As the 21st century unfolds, we find ourselves having to control, support, manage or otherwise cope with large-scale complex adaptive systems to an extent that is unprecedented in human history. Whether we are concerned with issues of food security, infrastructural resilience, climate change, health care, web science, security, or financial stability, we face problems that combine scale, connectivity, adaptive dynamics, and criticality. Complex systems simulation is emerging as the key scientific tool for dealing with such complex adaptive systems. Although a relatively new paradigm, it is one that has already established a track record in fields as varied as ecology (Grimm and Railsback, 2005), transport (Nagel et al., 1999), neuroscience (Markram, 2006), and ICT (Bullock and Cliff, 2004). In this report, we consider the application of simulation methodologies to financial systems, assessing the prospects for continued progress in this line of research
COORDINATION AND CONTROL OF LARGE-SCALE COMPLEX IT SYSTEMS: AN INTERDISCIPLINARY REVIEW OF THE LITERATURE
Since the emergence and widespread adoption of computer networks in the 1990s, developed societies have grown increasingly dependent on complex software-intensive systems. Such systems underpin business-critical applications in domains ranging from health care and financial markets to manufacturing and defense, where failure would have profound social and economic consequences Sommerville et al. (2012). These large-scale complexes IT systems are usually created and evolved dynamically through the integration of independently built and controlled heterogeneous components
Nonextensive statistical mechanics and complex scale-free networks
One explanation for the impressive recent boom in network theory might be
that it provides a promising tool for an understanding of complex systems.
Network theory is mainly focusing on discrete large-scale topological
structures rather than on microscopic details of interactions of its elements.
This viewpoint allows to naturally treat collective phenomena which are often
an integral part of complex systems, such as biological or socio-economical
phenomena. Much of the attraction of network theory arises from the discovery
that many networks, natural or man-made, seem to exhibit some sort of
universality, meaning that most of them belong to one of three classes: {\it
random}, {\it scale-free} and {\it small-world} networks. Maybe most important
however for the physics community is, that due to its conceptually intuitive
nature, network theory seems to be within reach of a full and coherent
understanding from first principles ..
A Generic Agent Organisation Framework For Autonomic Systems
Autonomic computing is being advocated as a tool for managing large, complex computing systems. Specifically, self-organisation provides a suitable approach for developing such autonomic systems by incorporating self-management and adaptation properties into large-scale distributed systems. To aid in this development, this paper details a generic problem-solving agent organisation framework that can act as a modelling and simulation platform for autonomic systems. Our framework describes a set of service-providing agents accomplishing tasks through social interactions in dynamically changing organisations. We particularly focus on the organisational structure as it can be used as the basis for the design, development and evaluation of generic algorithms for self-organisation and other approaches towards autonomic systems
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