Termination detection by using distributed snapshots
Information Processing Letters
Peer-to-Peer Membership Management for Gossip-Based Protocols
IEEE Transactions on Computers
The peer sampling service: experimental evaluation of unstructured gossip-based implementations
Proceedings of the 5th ACM/IFIP/USENIX international conference on Middleware
Correctness of a gossip based membership protocol
Proceedings of the twenty-fourth annual ACM symposium on Principles of distributed computing
HiScamp: self-organizing hierarchical membership protocol
EW 10 Proceedings of the 10th workshop on ACM SIGOPS European workshop
An efficient delay-optimal distributed termination detection algorithm
Journal of Parallel and Distributed Computing
T-Man: Gossip-based fast overlay topology construction
Computer Networks: The International Journal of Computer and Telecommunications Networking
Correctness of gossip-based membership under message loss
Proceedings of the 28th ACM symposium on Principles of distributed computing
CLON: Overlay Networks and Gossip Protocols for Cloud Environments
OTM '09 Proceedings of the Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009 on On the Move to Meaningful Internet Systems: Part I
GPC'11 Proceedings of the 6th international conference on Advances in grid and pervasive computing
Gossip-based clock synchronization for large decentralized systems
SelfMan'06 Proceedings of the Second IEEE international conference on Self-Managed Networks, Systems, and Services
A gossip-based mutual exclusion algorithm for cloud environments
GPC'12 Proceedings of the 7th international conference on Advances in Grid and Pervasive Computing
Hi-index | 0.00 |
Determining termination in dynamic environments is hard due to node joining and leaving. In previous studies on termination detection, some structures, such as spanning tree or computational tree, are used. In this work, we present an unstructured termination detection algorithm, which uses a gossip based scheme to cope with scalability and fault-tolerance issues. This approach allows the algorithm not to maintain specific structures even when nodes join and leave during runtime. These dynamic behaviors are prevalent in cloud computing environments and little attention has been paid by existing approaches. To measure the complexity of our proposed algorithm, a new metric, self-centered message complexity is used. Our evaluation over scalable settings shows that an unstructured approach has a significant merit to solve scalability and fault-tolerance problems with lower message complexity over existing algorithms.