ESR: An efficient, scalable and robust overlay for autonomic communications
Abstract
Autonomic Communication (AC) refers to self-managing systems which are capable of supporting self-configuration, self-healing and self-optimization. However, information reflection and collection, lack of centralized control, non-cooperation and so on are just some of the challenges within AC systems. Since many self-* properties are achieved by a group of autonomous entities that coordinate in a peer-to-peer (P2P) fashion, thus, it has opened the door to migrating research techniques from P2P systems. Motivated by the challenges in AC and based on comprehensive analysis of popular P2P applications, we present a novel Efficient, Scalable and Robust (ESR) Peer-to-Peer (P2P) overlay, which is inspired by two other scientific areas (i. e. conditioning monkeys and prime meridian). Differing from current structured and unstructured, or meshed and tree-like P2P overlay, the ESR is a whole new three-dimensional structure to improve the efficiency of routing, while information exchanges take in immediate neighbors with local information to make the system scalable and fault-tolerant. Meanwhile, rather than a complex game theory or incentive mechanism, a simple but effective punish mechanism has been presented based on a new ID structure which can guarantee the continuity of each node’s record in order to discourage negative behavior on a autonomous environment as AC. Large number of experiment results show the advantages of the ESR.
Department(s)
Computer Science
Document Type
Conference Proceeding
DOI
https://doi.org/10.1007/978-3-319-27119-4_29
Keywords
Autonomic communications, Information interaction, P2P overlay, Super node
Publication Date
1-1-2015
Recommended Citation
Liu, Jiaqi, Guojun Wang, Deng Li, and Hui Liu. "Esr: an efficient, scalable and robust overlay for autonomic communications." In International Conference on Algorithms and Architectures for Parallel Processing, pp. 415-429. Springer, Cham, 2015.
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)