اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدOn-Demand WebRTC Tunneling in Restricted Networks
31
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
In this paper we present the implementation of a WebRTC gateway service that can forward ad-hoc RTP data plane traffic from a browser on one local network to a browser on another local network. The advantage compared to the existing IETF STUN (RFC 5389), TURN (RFC 5766) and ICE (RFC 5245) protocols is that it does not require a public host and port mapping for each participating local host, and it works with more restrictive firewall policies. WebRTC implements ICE which combines STUN and TURN probes to automatically find the best connection between two peers who want to communicate. In corporate networks, simple hole punching and NAT traversal techniques typically do not work, e.g. because of symmetric NATs. Dynamic allocation of ports on an external 3rd party relay service is also typically blocked on restricted hosts. In our use case, doctors at hospitals can only access port 80 through the hospital firewall on external machines, and they need to communicate with patients who are typically behind a NAT in a local WiFi network. VPN solutions only work for staff but not between patients and staff. Our solution solves this problem by redirecting all WebRTC traffic through a gateway service on the local network that has a secure tunnel established with a public gateway. The public gateway redirects traffic from multiple concurrent streams securely between local gateway services that connect to it. The local gateways also communicate with browsers on their local network to mimic a direct browser-to-browser connection without having to change the browser runtime. We have demonstrated that this technique works well within the hospital network and arbitrary patient networks, without the need for any individual host configuration. In our evaluation we show that the latency overhead is 18-20 ms for each concurrent stream added to the same gateway service.
قيم البحث
اقرأ أيضاً
6G will exploit satellite, aerial and terrestrial platforms jointly to improve radio access capability and to unlock the support of on-demand edge cloud services in the three dimensional space (3D) by incorporating Mobile Edge Computing (MEC) functio
nalities on aerial platforms and low orbit satellites. This will extend the MEC support to devices and network elements in the sky and will forge a space borne MEC enabling intelligent personalized and distributed on demand services. 3D end users will experience the impression of being surrounded by a distributed computer fulfilling their requests in apparently zero latency. In this paper, we consider an architecture providing communication, computation, and caching (C3) services on demand, anytime and everywhere in 3D space, building on the integration of conventional ground (terrestrial) base stations and flying (non-terrestrial) nodes. Given the complexity of the overall network, the C3 resources and the management of the aerial devices need to be jointly orchestrated via AI-based algorithms, exploiting virtualized networks functions dynamically deployed in a distributed manner across terrestrial and non-terrestrial nodes.
155 -
M. Rajendiran
, S. K. Srivatsa Department of Computer Science andn Engineering
, Sathyabama University
2011
Adhoc networks are characterized by connectivity through a collection of wireless nodes and fast changing network topology. Wireless nodes are free to move independent of each other which makes routing much difficult. This calls for the need of an ef
ficient dynamic routing protocol. Mesh-based multicast routing technique establishes communications between mobile nodes of wireless adhoc networks in a faster and efficient way. In this article the performance of prominent on-demand routing protocols for mobile adhoc networks such as ODMRP (On Demand Multicast Routing Protocol), AODV (Adhoc on Demand Distance Vector) and FSR (Fisheye State Routing protocol) was studied. The parameters viz., average throughput, packet delivery ration and end-to-end delay were evaluated. From the simulation results and analysis, a suitable routing protocol can be chosen for a specified network. The results show that the ODMRP protocol performance is remarkably superior as compared with AODV and FSR routing protocols. Keywords: MANET, Multicast Routing, ODMRP, AODV, FSR.
We design a dispatch system to improve the peak service quality of video on demand (VOD). Our system predicts the hot videos during the peak hours of the next day based on the historical requests, and dispatches to the content delivery networks (CDNs
) at the previous off-peak time. In order to scale to billions of videos, we build the system with two neural networks, one for video clustering and the other for dispatch policy developing. The clustering network employs autoencoder layers and reduces the video number to a fixed value. The policy network employs fully connected layers and ranks the clustered videos with dispatch probabilities. The two networks are coupled with weight-sharing temporal layers, which analyze the video request sequences with convolutional and recurrent modules. Therefore, the clustering and dispatch tasks are trained in an end-to-end mechanism. The real-world results show that our approach achieves an average prediction accuracy of 17%, compared with 3% from the present baseline method, for the same amount of dispatches.
We propose a generic system model for a special category of interdependent networks, demand-supply networks, in which the demand and the supply nodes are associated with heterogeneous loads and resources, respectively. Our model sheds a light on a un
ique cascading failure mechanism induced by resource/load fluctuations, which in turn opens the door to conducting stress analysis on interdependent networks. Compared to the existing literature mainly concerned with the node connectivity, we focus on developing effective resource allocation methods to prevent these cascading failures from happening and to mitigate/confine them upon occurrence in the network. To prevent cascading failures, we identify some dangerous stress mechanisms, based on which we quantify the robustness of the network in terms of the resource configuration scheme. Afterward, we identify the optimal resource configuration under two resource/load fluctuations scenarios: uniform and proportional fluctuations. We further investigate the optimal resource configuration problem considering heterogeneous resource sharing costs among the nodes. To mitigate/confine ongoing cascading failures, we propose two network adaptations mechanisms: intentional failure and resource re-adjustment, based on which we propose an algorithm to mitigate an ongoing cascading failure while reinforcing the surviving network with a high robustness to avoid further failures.
The unmanned aerial vehicles base stations (UAV-BSs) have great potential in being widely used in many dynamic application scenarios. In those scenarios, the movements of served user equipments (UEs) are inevitable, so the UAV-BSs needs to be re-posi
tioned dynamically for providing seamless services. In this paper, we propose a system framework consisting of UEs clustering, UAV-BS placement, UEs trajectories prediction, and UAV-BS reposition matching scheme, to serve the UEs seamlessly as well as minimize the energy cost of UAV-BSs reposition trajectories. An Echo State Network (ESN) based algorithm for predicting the future trajectories of UEs and a Kuhn-Munkres-based algorithm for finding the energy-efficient reposition trajectories of UAV-BSs is designed, respectively. We conduct a simulation using a real open dataset for performance validation. The simulation results indicate that the proposed framework achieves high prediction accuracy and provides the energy-efficient matching scheme.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
