NetFlix vpn usa

these results are used to parametrize our simulator, which canscale the experiments up to the 500–1000 homes that a CO ora node in a cable provider's hybrid-fiber-coax (HFC) networkmight serve. As such, the simulator mimics the overhead suchas medium contention experienced by the real testbed.Testbed. To emulate a neighborhood, we deployed ninenodes spread across an office building, plus a video server.In this neighborhood, every node is equipped with MoCAand WiFi, and has a 10Mbps downlink from the video server.Groups of four (nodes 5 to 8) or five nodes (0 to 4) are connected by MoCA at 100Mbps. Wireless connectivity betweentwo nodes varies from 0 to 18Mbps, similar to the that betweenwell-connected neighbors (see Section III). Figure 5 shows thewireless bandwidth between the nodes.Video content. We emulate a video content library containing 10,000 one-hour videos, each of which is encoded at10Mbps. This library is similar in size to the number of ondemand videos in NetFlix vpn usa. Each node in the neighborhood hasa 1TB disk which can hold approximately 233 such videos.As with prior work, we use a Zipf-like distribution with askew factor α = 0.3 to represent the popularity distributionof videos in the library [26], [27]. In a Zipf-like distribution,content popularity (P) is related to its rank (r): Pr ∼1r1−αViewing pattern. Multimedia viewing varies diurnally witha "prime time" peak. We use a fixed probability distributionthat represents this behavior with a 24 hour period to simulatethe arrival of video requests. The shape of the distribution isbased on the findings by Qiu et al. [28]. We assume that theprobability that a home requests a video at prime time is 40%.This probability gradually falls to 10% in the next 12 hours andcomes back up to 40% in 24 hours. The videos thus requestedare sampled according to the Zipf-like distribution mentionedabove. The same video library and workload is used for thetestbed experiments and simulations.Metrics. We use three metrics for evaluation: average, peakand 95th percentile access network bandwidth at the secondmile link of Figure 1. The peak bandwidth is more importantbecause the peak determines the amount of bandwidth thatISPs have to provision. However, if the peak is short-livedand users are willing to accept small delays, it may not be asmeaningful. Consequently, we also report the 95th percentile,typically used for charging purposes.

NetFlix vpn usa

these results are used to parametrize our simulator, which canscale the experiments up to the 500–1000 homes that a CO ora node in a cable provider's hybrid-fiber-coax (HFC) networkmight serve. As such, the simulator mimics the overhead suchas medium contention experienced by the real testbed.Testbed. To emulate a neighborhood, we deployed ninenodes spread across an office building, plus a video server.In this neighborhood, every node is equipped with MoCAand WiFi, and has a 10Mbps downlink from the video server.Groups of four (nodes 5 to 8) or five nodes (0 to 4) are connected by MoCA at 100Mbps. Wireless connectivity betweentwo nodes varies from 0 to 18Mbps, similar to the that betweenwell-connected neighbors (see Section III). Figure 5 shows thewireless bandwidth between the nodes.Video content. We emulate a video content library containing 10,000 one-hour videos, each of which is encoded at10Mbps. This library is similar in size to the number of ondemand videos in NetFlix vpn usa. Each node in the neighborhood hasa 1TB disk which can hold approximately 233 such videos.As with prior work, we use a Zipf-like distribution with askew factor α = 0.3 to represent the popularity distributionof videos in the library [26], [27]. In a Zipf-like distribution,content popularity (P) is related to its rank (r): Pr ∼1r1−αViewing pattern. Multimedia viewing varies diurnally witha "prime time" peak. We use a fixed probability distributionthat represents this behavior with a 24 hour period to simulatethe arrival of video requests. The shape of the distribution isbased on the findings by Qiu et al. [28]. We assume that theprobability that a home requests a video at prime time is 40%.This probability gradually falls to 10% in the next 12 hours andcomes back up to 40% in 24 hours. The videos thus requestedare sampled according to the Zipf-like distribution mentionedabove. The same video library and workload is used for thetestbed experiments and simulations.Metrics. We use three metrics for evaluation: average, peakand 95th percentile access network bandwidth at the secondmile link of Figure 1. The peak bandwidth is more importantbecause the peak determines the amount of bandwidth thatISPs have to provision. However, if the peak is short-livedand users are willing to accept small delays, it may not be asmeaningful. Consequently, we also report the 95th percentile,typically used for charging purposes.