Epidemic algorithms for replicated database maintenance
PODC '87 Proceedings of the sixth annual ACM Symposium on Principles of distributed computing
The Vision of Autonomic Computing
Computer
Epidemic-Style Proactive Aggregation in Large Overlay Networks
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
Robust Aggregation Protocols for Large-Scale Overlay Networks
DSN '04 Proceedings of the 2004 International Conference on Dependable Systems and Networks
A Robust Protocol for Building Superpeer Overlay Topologies
P2P '04 Proceedings of the Fourth International Conference on Peer-to-Peer Computing
The peer sampling service: experimental evaluation of unstructured gossip-based implementations
Proceedings of the 5th ACM/IFIP/USENIX international conference on Middleware
Infrastructure for Engineered Emergence on Sensor/Actuator Networks
IEEE Intelligent Systems
A survey of autonomic communications
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Self-organization algorithms for autonomic systems in the SelfLet approach
Proceedings of the 1st international conference on Autonomic computing and communication systems
Autonomous Deployment of Self-Organizing Mobile Sensors for a Complete Coverage
IWSOS '08 Proceedings of the 3rd International Workshop on Self-Organizing Systems
An agent-based methodology for self-* systems
Multiagent and Grid Systems
Self-star Properties in Complex Information Systems
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Traditionally, autonomic computing is envisioned as replacing the human factor in the deployment, administration and maintenance of computer systems that are ever more complex. Partly to ensure a smooth transition, the design philosophy of autonomic computing systems remains essentially the same as traditional ones, only autonomic components are added to implement functions such as monitoring, error detection, repair, etc. In this position paper we outline an alternative approach which we call “grassroots self-management”. While this approach is by no means a solution to all problems, we argue that recent results from fields such as agent-based computing, the theory of complex systems and complex networks can be efficiently applied to achieve important autonomic computing goals, especially in very large and dynamic environments. Unlike traditional compositional design, in the grassroots approach, desired properties like self-healing and self-organization are not programmed explicitly but rather “emerge” from the local interactions among the system components. Such solutions are potentially more robust to failures, are more scalable and are extremely simple to implement. We discuss the practicality of grassroots autonomic computing through the examples of data aggregation, topology management and load balancing in large dynamic networks.