Grassroots Approach to Self-management in Large-Scale Distributed Systems
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.
KeywordsLoad Balance Overlay Network Autonomic Computing Partial View Random Topology
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