Multicast tree construction algorithm for dynamic traffic on software defined networks

Dynamic traffic of multicast communication in the Software Defined Network environment focused less though it is more natural and practical. In multicast communication, the traffic is dynamic due to the dynamic group memberships (i.e., participants join and leave the group anytime), which are not explored much in the previous research works. The multicast in dynamic traffic requires a method to handle dynamic group membership and minimum tree alteration for every join and leave of participants from the multicast group. This paper proposes a multicast tree construction algorithm, which considers receiving devices and network capability as base parameters to construct the multicast path. The proposed routing method uses Dijkstra’s Shortest Path algorithm for initial tree formation, identifies a multicast path, and processes the Shortest Path Tree to reduce the overall hop count and path cost. The multicast tree generated by the proposed enables the dynamic join and leaves of participating devices with reduced tree alteration using more common paths to reach the devices. The implementation and results show that the proposed method works efficiently in resource utilization with a reduced hop count and quality for multicast communication in static and dynamic scenarios. Also, the results demonstrate that the proposed method generates a stable common path for multicast communication.

Dynamic Group Recommendation Algorithm Based on Member Activity Level

The rapid development of social networks has led to an increased desire for group entertainment consumption, making the study of group recommender systems a hotspot. Existing group recommender systems focus too much on member preferences and ignore the impact of member activity level on recommendation results. To this end, a dynamic group recommendation algorithm based on the activity level of members is proposed. Firstly, the algorithm predicts the unknown preferences of members using a time-series-oriented rating prediction model. Secondly, considering the dynamic change of member activity level, the group profile is generated by designing a sliding time window to investigate the recent activity level of each member in the group at the recommended moment, and preference is aggregated based on the recent activity level of members. Finally, the group recommendations are generated based on the group profile. The experimental results show that the algorithm in this paper achieves a better recommendation result.

Environmental Stress Selects for Innovations That Drive Vibrio Symbiont Diversity

Symbiotic bacteria in the Vibrionaceae are a dynamic group of γ-Proteobacteria that are commonly found throughout the world. Although they primarily are free-living in the environment, they can be commonly found associated with various Eukarya, either as beneficial or pathogenic symbionts. Interestingly, this dual lifestyle (free-living or in symbiosis) enables the bacteria to have enormous ecological breadth, where they can accommodate a variety of stresses in both stages. Here, we discuss some of the most common stressors that Vibrio bacteria encounter when in their free-living state or associated with an animal host, and how some of the mechanisms that are used to cope with these stressors can be used as an evolutionary advantage that increases their diversity both in the environment and within their specific hosts.