Falling off the cliff: when systems go nonlinear

Authors:
Yvonne Coady;Russ Cox;John DeTreville;Peter Druschel;Joseph Hellerstein;Andrew Hume;Kimberly Keeton;Thu Nguyen;Christopher Small;Lex Stein;Andrew Warfield
Affiliations:
University of Victoria;MIT;Microsoft Research;Rice University;IBM Research;AT&T Research;HP Labs;Rutgers University;Vanu, Inc.;Harvard University;University of Cambridge
Venue:
HOTOS'05 Proceedings of the 10th conference on Hot Topics in Operating Systems - Volume 10
Year:
2005

Citing 8
Cited 2

Ethernet: distributed packet switching for local computer networks

Communications of the ACM
Low-rate TCP-targeted denial of service attacks: the shrew vs. the mice and elephants

Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Exploiting the Transients of Adaptation for RoQ Attacks on Internet Resources

ICNP '04 Proceedings of the 12th IEEE International Conference on Network Protocols
Robustness Testing of Java Server Applications

IEEE Transactions on Software Engineering
Stupid file systems are better

HOTOS'05 Proceedings of the 10th conference on Hot Topics in Operating Systems - Volume 10
Total recall: system support for automated availability management

NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
Microreboot — A technique for cheap recovery

OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Correlating instrumentation data to system states: a building block for automated diagnosis and control

OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6

Emergent (mis)behavior vs. complex software systems

Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
Interaction-aware scheduling of report-generation workloads

The VLDB Journal — The International Journal on Very Large Data Bases

Quantified Score

Hi-index	0.00

Visualization

Abstract

As the systems we build become more complex, understanding and managing their behavior becomes more challenging. If the system's inputs are within an acceptable range, it will behave predictably. However, the system may "fall off the cliff" if input values are outside this range. This nonlinear behavior is undesirable, because the system no longer behaves predictably: it may not be possible to use, control or even recover the system. In this paper, we describe what it means for a system to fall off the cliff. We outline methods for detecting and predicting these modes of nonlinear behavior, and propose several approaches for designing systems to cope with these instabilities, or to avoid them altogether. We conclude by outlining open research questions for investigation by the systems community.