When raft meets SDN: How to elect a leader over a network

This paper discusses the benefits in the operation of a Raft based SDN controller cluster, when the election of the cluster leader becomes more or less 'fair. Raft is a leader based consensus algorithm, which is used by the most popular open-source SDN controllers for replicating the network state. It requires all state changes to be confirmed by the leader, thus the leader election is very crucial for the Raft performance. In case that the inter-controller communication delay is the same for all controller pairs, the election process is absolute fair, meaning that the leadership is shared equally among the controllers. In all other cases, some controllers become leaders more frequently in benefit or at a cost of the average time required for a network state update. In this paper, we model this time as a function of the leadership probabilities of the cluster controllers. We also model these probabilities as a function of the time that each controller is waiting after detecting the current leader failure and before starting its campaign. We configure different ranges for the controller waiting times, adjusting the leadership probabilities and decreasing the average response time. Our model is confirmed by testbed experimentation. © 2020 IEEE

On Using Raft Over Networks: Improving Leader Election

Raft is a state-of-the-art consensus algorithm for state replication over a distributed system of nodes. According to Raft, all state updates occurring anywhere in the system are forwarded to the leader, which is elected among the system nodes to collect and replicate these updates to all other nodes. Thus, the time required for the state replication, named as system response time, depends on the delays between the leader and all other nodes. After multiple node failures and leadership transitions, each node can be leader with a probability that affects the expected response time. The leadership probabilities, in turn, are affected by the random intervals that nodes are waiting, after detecting a leader failure and before competing for the successive leadership. The Raft designers suggest the ranges of these intervals to be equal for all nodes. However, this may result in increased expected response time. In this paper, mathematical models are presented for estimating the ranges resulting in the desired leadership probabilities. The presented theoretical results are also confirmed by testbed experimentation with an open-source and widely used Raft implementation. © 2004-2012 IEEE

Experimental evaluation of a Follow-me MEC Cloud-Native 5G network

Cloud-native approaches for network functions In the 5G context have been embraced by the community, as they allow flexible management, reconfiguration and monitoring of the network in an end-to-end manner. As network softwarization extends to the RAN, empowered through the cellular stack disaggregation, even base station components can be executed as a cloud-native service. As low latency access is needed in the 5G and beyond networks for serving ultra Reliable Low Latency Communications, Multi-Access Edge Computing (MEC) needs to be integrated in the overall architecture. As the user moves among different RANs, the latency of accessing the service needs to be preserved for providing users with a seamless experience. To accomplish such behavior, migrations of the hosted services are needed, though not fully compatible with the cloud-native approach, and placing them closer to the network access point of the user. In this work, we experiment with a cloud-native end-to-end network, enhanced with Follow-me MEC functionalities. Heterogeneous access is provided at the RAN level, using disaggregated base stations, and MEC is integrated on the fronthaul of the network, ensuring low-latency access to services. The entire network is instantiated in a cloud-native manner, using a widely adopted container orchestration solution. Our results show that the scheme is able to provide low latency access to the hosted services, while the UE remains agnostic of the entire process and without any drops of the already established connections. © 2021 IEEE

Enabling distributed spectral awareness for disaggregated 5G ultra-dense hetnets

Future wireless infrastructures will have to deal with significantly higher loads of data, produced by applications demanding higher capacity with lower latency for the exchanged information. 5G networks are expected to resolve these problems, by bringing in advanced flexibility of deployment through the Cloud-RAN concept, and new air interfaces. Dense Heterogeneous Networks will also assist in the smooth transition to a holistic approach for managing the wireless network, especially through their integration at the base station level. Nevertheless, networks operating under a very tight spectral environment, with limited spectrum resources, need to be aware of their expected performance in order to optimally decide their operating frequency. In this work, we consider a disaggregated heterogeneous base station, complying with the Cloud-RAN concept, and integrate spectral aware non-3GPP access (WiFi) to the base station. We use multi-technology links to serve the end users, through two different paths: an LTE and a WiFi. The WiFi units are able to automatically discover their operating frequency ensuring efficient data delivery to the end users. We integrate all our contributions in a framework and deploy it over a real testbed. Our experimental results illustrate enhanced performance for the non-3GPP access of the network, delivering more than 5 times better throughput for high interference scenarios. © 2019 IEEE

Cooperative handoff in wireless networks

In 802.11-based wireless networks the stations (STAs) are associated with the available access points (APs) and communicate through them. In traditional handoff schemes the STAs get information about the active APs in their neighborhood by scanning the available channels and listen to transmitted beacons. This paper proposes a 802.11k compliant framework for cooperative handoff where the STAs are informed about the active APs by exchanging information with neighboring STAs. In this way we minimize the delay of the scanning procedure. We evaluate the performance of our mechanisms through simulations and we show that when our cooperative framework is applied the network performance is significantly improved. Consequently, our system is more capable in meeting the needs of QoS-sensitive applications. © 2008 IEEE