The Company's BIG-IP application delivery controllers (ADCs) is positioned in front of Web and application servers, balancing traffic and performing compute-intensive functions, such as encrypting and un-encrypting transmissions, screening traffic for security threats, maintaining open connections with servers, speeding the flow of traffic, managing access to applications and data.
They quickly tried to screen the traffic, brandishing arms as they looked for the van in traffic jams across the suburban roads.
The filtering for screen on traffic helps to characterize active use despite nearby users rather than simple background traffic (e.g., Facebook newsfeed).
In this work, we focus on the problem of policing screen mirroring traffic.
"The Jaccard index-infused markovian clustering policer (JIMC-P)" presents our new proposed mechanism for screen mirroring traffic.
In an enterprise-like environment, an additional burden is expected to be added to the network by screen mirroring traffic.
In "The Jaccard index-infused Markovian clustering policer (JIMC-P)" we will explain why a new mechanism is needed for screen mirroring traffic.
The results presented in Table 3 and Fig. 2 are derived by running a similar experiment for Dataset 2, with the difference that the declared movies, instead of being screen mirroring traffic videos, were videos from movies.
As it will be shown in our results in "Results and discussion", our previously proposed mechanism, FSA-TB [26] confirms, for the first dataset of screen mirroring traffic, its excellence in comparison with all other mechanisms from the literature.
This new mechanism, JIMC-P, is based on the Jaccard Index-Infused Markovian Clustering model presented in our recent work on screen mirroring traffic modeling in [15], i.e., it is a token bucket mechanism in which tokens are generated based on the JIMC model instead of being generated based on a mean rate.
