Last weekend, Fred Hapgood blogged at CIO magazine about network management and monitoring. He described how current models are hitting a wall of complexity and numbers. Conventional networks, and particularly their management, do not scale gracefully. As networks get larger and more complex their management problems will keep get more difficult even faster and the time scales for solving problems get smaller.
Hapgood points out in his blog that many researchers are now looking toward biological models for management and control. Biology is rich with large networks—protein cascades, gene switching networks, intercellular networks, nervous systems, and whole ecosystems that efficiently organize a large number of unreliable and dynamically changing components. These networks manifest adaptive and robust behaviors, despite the lack of any central management. This robustness and tolerance of diversity is in sharp contrast with man-made networks despite embracing far more individual variation among its nodes.
Hapgood went on to cite recent work primarily from the University of Bologna that tried to develop taxonomy of the modes of biological signaling and how they might apply to intra-node network communications and control. These modes of communications handle increase and loss of nodes well; more important, they degrade well, providing reduced functionality rather than failure. Fred was kind enough to share the paper with me (and I have placed it here).
The paper classifies biological signaling patterns into plain diffusion, reaction-diffusion, proliferation, and stigmergy. It goes on to consider biological entities as instances of object oriented design; and the signals as design patterns. Through simulations and modeling, they demonstrate effective and performant control of large systems. I would have liked the authors to reach for one more abstraction, to consider invoking these patterns in aspect oriented design. It is a fascinating and useful article.
Austrian school economics and developmental biology have long swapped concepts and vocabulary to describe the development and behavior of as complex adaptive systems. I think we are, as Fred suggests, beginning to recognize networked engineered systems as complex adaptive systems with the capability for their own emergent behaviors.
Complex adaptive systems have large numbers of diverse agents that interact. Each agent reacts to the actions of the other agents and to changes in environment. Agents are autonomous, using distributed control and decentralized decision making. Eventually, the dominant interaction becomes the agents interacting with the system environment that was itself created by the agents’ own independent decision making.
In economics, we call the order that arises out of markets emergent self organization. In biology, we call it embryology. In either case, a large scale pattern emerges out of the smaller decisions and interactions. The emergent pattern is not imposed top-down, but rather arises from decentralized agents interacting within bounds of distributed control (or self control if you will).
A characteristic of meta-systems (or systems of systems) that demonstrate self organization is resilience in the face of change, what the economists call adaptive capacity. Market design theory, in the news this week with a new Nobel Prize, is in part concerned with ensuring adaptive capacity.
We are just beginning to apply the concepts of biology and markets to aggregates of engineered systems. In nature, systems that have too many direct interactions become brittle, and break badly. The Cleveland Outage of 2001 could be described as such a shattering, with the cracks extending into Canada and the East Coast. Less control and more heterogeneity in agents may be what we need to acquire resilience in our engineered systems.