Mark Burgess (computer scientist)

From Wikipedia, the free encyclopedia
Jump to: navigation, search

Mark Burgess (born 19 February 1966) is a researcher and writer at Oslo University College in Norway, who is well known for work in computer science in the field of policy-based configuration management.

Mark Burgess was born in Maghull in the United Kingdom to English parents. He grew up in Bloxham, a small village in Oxfordshire from the age of 5-18, attending Bloxham Primary School, Warriner Secondary School and Banbury Upper School.

Originally interested in physics and astronomy, he went to study astrophysics at the (then) School of Physics at the University of Newcastle upon Tyne, where he later switched to pure Physics and then Theoretical Physics for his bachelor's degree. He stayed on to obtain a Doctor of Philosophy in Theoretical Physics in Newcastle, in the field of Spontaneous Symmetry Breaking in Non-Abelian Gauge Theories, for which he received the Runcorn Prize.

After completing a doctorate he pursued a post-doctoral fellowship at the University of Oslo in Norway and has lived in Oslo ever since. While at Oslo he developed an interest in the behaviour of computers as dynamic systems and began to apply ideas from physics to describe computer behaviour.

Mark Burgess is perhaps best known as the author of the popular configuration management software package CFEngine, but has also made important contributions to the theory of the field of automation and policy based management, including the idea of operator convergence and promise theory.

Other free software projects initiated by Mark Burgess

  • FaultCat - Fault Tree Analysis software (with Erik Haugvaldstad, Øystein Heskestad, Dagfinn Steinnes)
  • Archipelago - Graph Theoretic Centrality Decomposition Visualizer
  • Cellular automaton Simulation Environment (Later developed by Hårek Haugerud, Thomas Sevaldrud, and others)
  • ECG machine-learning monitoring project, later integrated into CFEngine 2 as cfenvd, and CFEngine 3 as cf-monitord

Contributions to Computer Science[edit]

Burgess has made contributions to theoretical and empirical computer science, mainly in the area of the behaviour of computing infrastructure and services.[1] In the early 1990s Burgess asserted that programmatic models of computer programs could not describe observed behaviour at the macroscopic scale, and that statistical physics could be used instead, thus likening artificial systems to a quasi-natural phenomenon.[2]


In 1993, Burgess introduced the software CFEngine based in intuitions and practice, focusing on the idea of repeatable desired end-state `convergence', to manage system configuration. The term convergence, used by Burgess, is now often inaccurately just called idempotence, as convergence in his meaning implied both desired end-state and idempotence of an error correction operator at the desired end-state. Shifting interest from Theoretical Physics to Computer Science, Burgess then began to explore the ad hoc choices initially made, and set out to find a scientific method for understanding such choices in computing systems.

Computer Immunology, Anomaly Detection, and Machine Learning[edit]

Following a position paper `manifesto' pointing out the research challenges needed to make self-repairing systems,[3] Burgess undertook to study computer systems as a number of empirical phenomena, taking an approach based on physics to learn first about the scales and patterns. The idea of self-healing, or self-maintaining systems was originally referred to as Computer Immunology, as it was inspired by research into the Danger Model of human immune systems. The empirical studies were published in various formats between 1999 and 2003, culminating in a journal summary review,[4] and a more practical method for automated machine learning of system behavioural characters.[5] This incorporated the idea of so-called exponential smoothing (which was called a geometric average) for fast learning, along with a two-dimensional, cylindrical time model[6] which was based on the result that network client-server traffic would be expected to behave like a quasi-periodic stochastic function (a characteristic of a system driven close to equilibrium),.[7][8]

The notion of an equilibrium or steady state operation thus became the baseline, replacing arbitrary thresholds used in the monitoring software of the day. The software CFEngine became the proof of concept platform using these methods for system state anomaly detection, from 2002 to the present, but received little use. Modern monitoring companies Circonus (founded 2010) and Vivid Cortex (founded 2012) now implement these concepts in dedicated software products.

Theoretical models[edit]

Based on these fundamental empirical studies, Burgess argued for two kinds of theoretical model to describe systems, which he called type 1 and type 2.[9] Type 1 models were dynamical performance models that described machines as changing phenomena. Type 2 were semantic models, concerning the efficacy and influence of human decisions on behaviour, called policy, or desired-state computing. He later developed these further and made connection with Claude Shannon's work on error correction in a paper discussing how separation of timescales plays an important role in computer science, by analogy with physics.[10] With Trond Reitan, Burgess showed that the question of when was the optimal time to backup data could be answered scientifically.[11]

The studies carried out between 1998 and 2002 led to a monograph Analytical Network and System Administration: Managing Human-Computer Systems.[12] Although quite comprehensive about some aspects of systems, Burgess identified a missing piece to the story, namely how to describe distributed cooperation between computers in networks. This prompted later work, which became Promise Theory,[13] proposed at the Distributed Systems, Operations and Management conference in Barcelona in 2005.[14]

The computer science community has had a mixed response to the hybrid nature of the infrastructure work, which seemed to view as being somewhere between traditional computing and physics. However, by now it has become almost ubiquitous, and its approaches and results are in general use.[citation needed]

Graph Theoretical ideas[edit]

Another recurring theme of Burgess's work has been graph theory. Working with search engine researchers Geoffrey Canright and Knut Engø Monsen, Burgess developed a page ranking algorithm similar to PageRank eigenvalue sink remedies in directed graphs.[15] This work also met with resistance from the American journal establishment, and was delayed before final publication.[16] With PhD Student Kyrre Begnum, he explored the related technique of Principal Component Analysis for analysing correlations in the machine-learned anomalies described above.[17] Graphs as a model of security made another connection with physics, through the idea of percolation, or path criticality[18]

Knowledge management[edit]

Since 2007, Burgess has turned his attention to the matter of knowledge representations and knowledge management, often using Promise Theory as an agency model.[19]


Mark Burgess is the author of a number of books

He is also the author of many popular and fictional writings.

External links[edit]


  1. ^ M. Burgess, website
  2. ^ M. Burgess, In Search of Certainty, XtAxis Press, 2013
  3. ^ Computer Immunology USENIX LISA conference, 1998
  4. ^ Measuring system normality ACM Transactions on Computing Systems 20, p.125-160
  5. ^ M. Burgess, Two dimensional time-series for anomaly detection and regulation in adaptive systems, in Proceedings of 13th IFIP/IEEE International Workshop on Distributed System, operations and management (DSOM 2002)
  6. ^ M. Burgess, Two dimensional time-series for anomaly detection and regulation in adaptive systems, in Proceedings of 13th IFIP/IEEE International Workshop on Distributed System, operations and management (DSOM 2002). "Management Technologies for E-Commerce and E-Business Applications" Springer 2002
  7. ^ M. Burgess, Thermal, non-equilibrium phase space for networked computers, Phys. Rev. E (2000)62:1738
  8. ^ M. Burgess, The kinematics of distributed computing, Int. J. Mod Phys. C12 759-789 (2001)
  9. ^ M. Burgess, Theoretical System Administration, USENIX LISA Conference proceedings 2000
  10. ^ M. Burgess, On the theory of system administration. Science of Computer Programming 49, 2003. p1-46
  11. ^ A risk analysis of disk backup or repository maintenance. Science of Computer Programming 2007;64:312-331
  12. ^ Mark Burgess, Analytical Network and System Administration: Managing Human-Computer Systems, J. Wiley and Sons, 2004
  13. ^ J.A. Bergstra and M. Burgess, Promise Theory: Principles and Applications, XtAxis press 2014
  14. ^ M. Burgess, An Approach to Understanding Policy Based on Autonomy and Voluntary Cooperation, Lecture Notes in Computer Science Volume 3775, 2005, pp 97-108
  15. ^ Mining Topological Importance From The Eigenvectors Of Directed Graphs (2007)
  16. ^ J. Bjelland, M. Burgess, G. Canright and K. Engø-Monsen, Importance functions for directed graphs, 2004, Journal of Data Mining and Knowledge Discovery as ``Mining Topological Importance From The Eigenvectors Of Directed Graphs 20:98-151 (2010).
  17. ^ K. Begnum and M. Burgess, Principal components and importance ranking of distributed anomalies, Machine Learning Journal, 58: 217-230, (2005)
  18. ^ M. Burgess and G. Canright, A Graphical Model of Computer Security (From Access Control to Social Engineering), in International Journal of Information Security (IJIS), vol 3, 70-85 (2004).
  19. ^ M. Burgess What's Wrong with Knowledge Management? And the Emergence of Ontology