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David J. Singer

Publications and source records attributed to David J. Singer.

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No Free Lunch for Avoiding Clustering Vulnerabilities in Distributed Systems

Emergent design failures are ubiquitous in complex systems, and often arise when system elements cluster. Approaches to systematically reduce clustering could improve a design's resilience, but reducing clustering is difficult if it is driven by collective interactions among design elements. Here, we use techniques from statistical physics to identify mechanisms by which spatial clusters of design elements emerge in complex systems modelled by heterogeneous networks. We find that, in addition to naive, attraction-driven clustering, heterogeneous networks can exhibit emergent, repulsion-driven clustering. We draw quantitative connections between our results on a model system in naval engineering to entropy-driven phenomena in nanoscale self-assembly, and give a general argument that the clustering phenomena we observe should arise in many distributed systems. We identify circumstances under which generic design problems will exhibit trade-offs between clustering and uncertainty in design objectives, and we present a framework to identify and quantify trade-offs to manage clustering vulnerabilities.

physics.soc-ph

Avoidance, Adjacency, and Association in Distributed Systems Design

Patterns of avoidance, adjacency, and association in complex systems design emerge from the system's underlying logical architecture (functional relationships among components) and physical architecture (component physical properties and spatial location). Understanding the physical--logical architecture interplay that gives rise to patterns of arrangement requires a quantitative approach that bridges both descriptions. Here, we show that statistical physics reveals patterns of avoidance, adjacency, and association across sets of complex, distributed system design solutions. Using an example arrangement problem and tensor network methods, we identify several phenomena in complex systems design, including placement symmetry breaking, propagating correlation, and emergent localization. Our approach generalizes straightforwardly to a broad range of complex systems design settings where it can provide a platform for investigating basic design phenomena.

physics.soc-ph

Robust Design from Systems Physics

A crucial challenge in engineering modern, integrated systems is to produce robust designs. Ensuring robust design is difficult because subsystem couplings produce unpredictable response to changes in whole system specifications. Here, we show that the response of design elements to whole-system specification changes can be characterized, as materials are, using strong/weak and brittle/ductile dichotomies. We find these dichotomies emerge from a mesoscale treatment of early stage design problems that we cast in terms of stress-strain relationships. Compared with other state-of-the-art techniques, we propose a two-factor robustness metric that is computable for sets of design solutions. We illustrate the use of this approach with examples of arrangement problems drawn from naval engineering, however our approach is immediately applicable to a broad range of problems in integrated systems design.

physics.soc-ph

Statistical Physics of Design

A key challenge in complex design problems that permeate science and engineering is the need to balance design objectives for specific design elements or subsystems with global system objectives. Global objectives give rise to competing design pressures, whose effects can be difficult to trace in subsystem design. Here, using examples from arrangement problems, we show that the systems-level application of statistical physics principles, which we term "systems physics", provides a detailed characterization of subsystem design in terms of the concepts of stress and strain from materials physics. We analyze instances of routing problems in naval architectures, and show that systems physics provides a direct means of classifying architecture types, and quantifying trade-offs between subsystem- and overall performance. Our approach generalizes straightforwardly to design problems in a wide range of other disciplines that require concrete understanding of how the pressure to meet overall design objectives drives the outcomes for component subsystems.

physics.soc-ph