CONTEXT AWARE MEETING AUGMENTATION

In some cases, participants in an online meeting would like to know meeting-specific information about other participants in the online meeting. Techniques described herein collect meeting- or context-specific data for each participant in an online conference/meeting from multiple sources and apply an information filter to allow other authorized participants to view specific information about the participant. According to additional techniques discussed herein, agents collect the context-specific information from an enterprise database and filter the information that can be viewed by different participants based on their roles

PREDICTIVE AND ADAPTIVE MAC LEARNING AS A SERVICE FOR CLOUD AND WIRELESS ENVIRONMENTS

The protocols typically used by overlay technologies, such as Ethernet Virtual Private Network (EVPN), Virtual Extensible Local Area Network (VxLAN), Network Virtualization Overlays (NVO3), etc. are not designed to perform Media Access Control (MAC) learning validation. Thus, overlay environments may be subject to Denial-of-Service (DOS attacks), MAC spoofing, and other potential attacks/issues. Presented herein are techniques to facilitate a centralized service that performs MAC learning, referred to herein as MAC Learning-as-a-Service (MLaaS), such that MAC addresses, Internet Protocol (IP) addresses, and network virtualization edge (NVE) devices can be learned for an overlay environment. Such techniques as presented herein can provide for increasing the MAC scale when compared to network devices, can facilitate providing MAC/AP address priorities in Ternary Content-Addressable Memory (TCAM) of NVE devices based on certain device groups, can facilitate efficient MAC/IP address validation via machine learning and simulated direct probing, and can prevent traffic loss during MAC movement, which can be predicted through machine learning

TIME AND CONTEXT VARIABLE TOPOLOGIES (TCVT) FOR SUSTAINABILITY-AWARE NETWORK OPERATIONAL FOCUS MODES

Techniques are presented herein that leverage the concept of Time-Variant Routing (TVR) to create time and context variable topologies (TCVTs) that offer a scalable and versatile solution for supporting a proportional response to changes in a network’s traffic levels, allowing a network operator to maximize sustainability gains in a network having variable, tidal, and periodic traffic. Aspects of the presented techniques encompass sub-topological planes (which can be made available to a topology as a function of time), network operational focus modes (which employ such planes to create objective-optimized topologies), and mappings between the two. The techniques allow an optimal set of topological elements to be identified based on different variables including, for example, a traffic demand and a renewable energy makeup (e.g., a carbon intensity). By hiding all of the complexity from a user, the techniques offer a simplified user experience through which such a user need only select different network operational focus modes to manage very complex outcomes