An Asynchronous Distributed Training Algorithm Based on Gossip

Jia, Zhenhong; Tu, Jun; Ren, Donglin

doi:10.1109/icbda55095.2022.9760359

Cited by 2 publications

(2 citation statements)

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“…Finally, gossip is emerging as candidate messaging technology for enabling the convergence of training process in distributed AI applications. For example, gossip is used to efficiently exchange model information between computing clusters [10], [40], [41]. In fact, centralized ML algorithms, adopting stochastic gradient descent, may suffer from variable latency.…”

Section: Related Workmentioning

confidence: 99%

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Gossip-Based Monitoring Protocol for 6G Networks

Femminella

Reali

2023

IEEE Trans. Netw. Serv. Manage.

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The service function (SF) area has gained increasing attention in the last years due its ability to combine the advantages of cloud computing with network softwarization. By decoupling SFs from the physical equipment where they are executed, it is possible to make network services scalable and flexible. These advantages become even more evident in the forthcoming 6G networks, where the overall environment is expected to become more dynamic and cloud-based, with SFs deployed as cloud-native functions. However, in order to efficiently manage and compose services using these SFs, it is necessary to monitor the available resources of the nodes where they can be deployed, in addition to exchange information relevant to the operational status of active SFs. To this aim, we propose a lightweight monitoring architecture by using agents in charge of monitoring the status of SFs running in co-located clusters. These monitoring agents exchange their information by means of a gossip protocol, which allows increasing the reliability of the process. In this way, it is possible to keep service decisions as local as possible, limiting the interactions with centralized decision and orchestration platforms, and thus increasing network scalability and responsiveness. Performance evaluation shows the effectiveness of the proposed solution, and demonstrates that the network overhead of the distributed monitoring process is definitely affordable.

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Section: Related Workmentioning

confidence: 99%

“…In fact, centralized ML algorithms, adopting stochastic gradient descent, may suffer from variable latency. The decentralized and asynchronous nature of gossip can successfully address this issue [10], [41].…”

Section: Related Workmentioning

confidence: 99%