| 1. |
- Nguyen, Van-Giang, 1989-, et al.
(författare)
-
On Auto-scaling and Load Balancing for User-plane Functions in a Softwarized 5G Core
- 2021
-
Ingår i: Proceedings of the 2021 17th International Conference on Network and Service Management: Smart Management for Future Networks and Services, CNSM 2021. - : IEEE. - 9783903176362 ; , s. 132-138
-
Konferensbidrag (refereegranskat)abstract
- In the fifth generation (5G) mobile networks, the number of user plane functions has increased, and, in contrast to previous generations. They can be deployed in a decentralized way and auto-scaled independently from their control plane functions. Moreover, the performance of the user plane functions can be boosted with the adoption of advanced acceleration techniques such as Vector Packet Processing (VPP). However, the increased number of user plane functions has also made load balancing a necessity, something we find has so far received little attention. Moreover, the introduction of VPP poses a challenge to the design of the auto-scaling of user-plane functions. In this paper, we address these two challenges by proposing a novel performance indicator for making better auto-scaling decisions, and by proposing three new dynamic load-balancing algorithms for the user plane of a VPP-based, softwarized 5G network. The novel performance indicator is estimated based on the VPP vector rate, and is used as a threshold for the auto-scaling process. The dynamic load-balancing algorithms take into account the number of bearers allocated for each user plane function and their VPP vector rate. We validated and evaluated our proposed solution in a 5G testbed. Our experiment results show that the scaling helps to reduce the packet latency for the user plane traffic, and our proposed load-balancing algorithms seem to give a better distribution of traffic load as compared to traditional static algorithms.
|
|
| 2. |
- Nguyen, Van-Giang, 1989-, et al.
(författare)
-
On Auto-scaling and Load Balancing for User-plane Gateways in a Softwarized 5G Network
- 2021
-
Ingår i: Proceedings of the 2021 17th International Conference on Network and Service Management. - : IEEE. - 9783903176362 ; , s. 132-138
-
Konferensbidrag (refereegranskat)abstract
- In the fifth generation (SG) mobile networks, the number of user-plane gateways has increased, and, in contrast to previous generations they can be deployed in a decentralized way and auto-scaled independently from their control-plane functions. Moreover, the performance of the user-plane gateways can be boosted with the adoption of advanced acceleration techniques such as Vector Packet Processing (VPP). However, the increased number of user-plane gateways has also made load balancing a necessity, something we find has so far received little attention. Moreover, the introduction of VPP poses a challenge to the design of the auto-scaling of user- plane gateways. In this paper, we address these two challenges by proposing a novel performance indicator for making better auto-scaling decisions, and by proposing three new dynamic load- balancing algorithms for the user plane of a VPP-based, softwarized SG network. The novel performance indicator is estimated based on the VPP vector rate and is used as a threshold for the auto-scaling process. The dynamic load-balancing algorithms take into account the number of bearers allocated for each user-plane gateway and their VPP vector rate. We validate and evaluate our proposed solution in a SG testbed. Our experiment results show that the scaling helps to reduce the packet latency for the user-plane traffic, and that our proposed load-balancing algorithms can give a better distribution of traffic load as compared to traditional static algorithms.
|
|
| 3. |
- Seo, Eunil, 1970-, et al.
(författare)
-
Auction-based Federated Learning using Software-defined Networking for resource efficiency
- 2021
-
Ingår i: Proceedings of the 2021 17th International Conference on Network and Service Management: Smart Management for Future Networks and Services, CNSM 2021. - : IEEE. - 9783903176362 ; , s. 42-48
-
Konferensbidrag (refereegranskat)abstract
- The training of global models using federated learning (FL) strategies is complicated by variations in local model quality arising from variation in data distribution across individual clients. A wide range of training strategies could be created by varying the size and distribution of the training data and the number of training iterations to be performed. All these variables affect both model quality and resource consumption. To facilitate the selection of good training strategies, we propose an auction-based FL method that can identify a training strategy that is optimal in terms of resource management efficiency subject to a given model quality requirement. An auction method is used to dynamically select resource-efficient FL clients and local models to minimize resource usage. This is enabled by using Software-defined Networking (SDN) to support the dynamic management of FL clients. We show that resource-optimal FL strategies can be implemented in the cloud/edge services market; dynamic quality-based model selection can reduce resource costs by up to 17% from the FL server's perspective. Moreover, the client utility function presented herein helps FL clients adopt practical trading strategies to cooperate efficiently with FL servers.
|
|
