اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدOn the Design and Implementation of Structured P2P VPNs
173
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Centralized Virtual Private Networks (VPNs) when used in distributed systems have performance constraints as all traffic must traverse through a central server. In recent years, there has been a paradigm shift towards the use of P2P in VPNs to alleviate pressure placed upon the central server by allowing participants to communicate directly with each other, relegating the server to handling session management and supporting NAT traversal using relays when necessary. Another, less common, approach uses unstructured P2P systems to remove all centralization from the VPN. These approaches currently lack the depth in security options provided by other VPN solutions, and their scalability constraints have not been well studied. In this paper, we propose and implement a novel VPN architecture, which uses a structured P2P system for peer discovery, session management, NAT traversal, and autonomic relay selection and a central server as a partially-automated public key infrastructure (PKI) via a user-friendly web interface. Our model also provides the first design and implementation of a P2P VPN with full tunneling support, whereby all non-P2P based Internet traffic routes through a trusted third party and does so in a way that is more secure than existing full tunnel techniques. To verify our model, we evaluate our reference implementation by comparing it quantitatively to other VPN technologies focusing on latency, bandwidth, and memory usage. We also discuss some of our experiences with developing, maintaining, and deploying a P2P VPN.
قيم البحث
اقرأ أيضاً
Higher dimensional classification has attracted more attentions with increasing demands for more flexible services in the Internet. In this paper, we present the design and implementation of a two dimensional router (TwoD router), that makes forwardi
ng decisions based on both destination and source addresses. This TwoD router is also a key element in our current effort towards two dimensional IP routing. With one more dimension, the forwarding table will grow explosively given a straightforward implementation. As a result, it is impossible to fit the forwarding table to the current TCAM, which is the de facto standard despite its limited capacity. To solve the explosion problem, we propose a forwarding table structure with a novel separation of TCAM and SRAM. As such, we move the redundancies in expensive TCAM to cheaper SRAM, while the lookup speed is comparable with conventional routers. We also design the incremental update algorithms that minimize the number of accesses to memory. We evaluate our design with a real implementation on a commercial router, Bit-Engine 12004, with real data sets. Our design does not need new devices, which is favorable for adoption. The results also show that the performance of our TwoD router is promising.
Long Range (LoRa) network is emerging as one of the most promising Low Power Wide Area (LPWA) networks, since it enables the energy-constraint devices distributed over wide areas to establish affordable connectivity. However, how to implement a cost-
effective and flexible LoRa network is still an open challenge. This paper aims at exposing a feasible solution of design and implementation, allowing users to conveniently build a private LoRa network for various IoT applications. Firstly, several typical application scenarios of LoRa network are discussed. Then, the LoRa system architecture is presented with the functionality of each component. We address the hardware design and implementation of LoRa Gateway, which is the bridge between LoRa nodes and LoRa network server. Especially, the paper contributes by proposing an improved software architecture of LoRa network server whose source codes are open on GitHub. Under the architecture, LoRa network server is divided into four decoupled modules and uses the messaging system based on streaming data for the interaction between modules to guarantee scalability and flexibility. Finally, extensive experiments are conducted to evaluate the performance of LoRa networks in typical environments.
Time-sensitive wireless networks are an important enabling building block for many emerging industrial Internet of Things (IoT) applications. Quick prototyping and evaluation of time-sensitive wireless technologies are desirable for R&D efforts. Soft
ware-defined radio (SDR), by allowing wireless signal processing on a personal computer (PC), has been widely used for such quick prototyping efforts. Unfortunately, because of the textit{uncontrollable delay} between the PC and the radio board, SDR is generally deemed not suitable for time-sensitive wireless applications that demand communication with low and deterministic latency. For a rigorous evaluation of its suitability for industrial IoT applications, this paper conducts a quantitative investigation of the synchronization accuracy and end-to-end latency achievable by an SDR wireless system. To this end, we designed and implemented a time-slotted wireless system on the Universal Software Radio Peripheral (USRP) SDR platform. We developed a time synchronization mechanism to maintain synchrony among nodes in the system. To reduce the delays and delay jitters between the USRP board and its PC, we devised a {textit{Just-in-time}} algorithm to ensure that packets sent by the PC to the USRP can reach the USRP just before the time slots they are to be transmitted. Our experiments demonstrate that $90%$ ($100%$) of the time slots of different nodes can be synchronized and aligned to within $ pm 0.5$ samples or $ pm 0.05mu s$ ($ pm 1.5$ samples or $ pm 0.15mu s$), and that the end-to-end packet delivery latency can be down to $3.75ms$. This means that SDR-based solutions can be applied in a range of IIoT applications that require tight synchrony and moderately low latency, e.g., sensor data collection, automated guided vehicle (AGV) control, and Human-Machine-Interaction (HMI).
The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated by a server. FedL ignores two important characteristics of contemporary
wireless networks, however: (i) the network may contain heterogeneous communication/computation resources, while (ii) there may be significant overlaps in devices local data distributions. In this work, we develop a novel optimization methodology that jointly accounts for these factors via intelligent device sampling complemented by device-to-device (D2D) offloading. Our optimization aims to select the best combination of sampled nodes and data offloading configuration to maximize FedL training accuracy subject to realistic constraints on the network topology and device capabilities. Theoretical analysis of the D2D offloading subproblem leads to new FedL convergence bounds and an efficient sequential convex optimizer. Using this result, we develop a sampling methodology based on graph convolutional networks (GCNs) which learns the relationship between network attributes, sampled nodes, and resulting offloading that maximizes FedL accuracy. Through evaluation on real-world datasets and network measurements from our IoT testbed, we find that our methodology while sampling less than 5% of all devices outperforms conventional FedL substantially both in terms of trained model accuracy and required resource utilization.
Asterisk and Open IMS use SIP signal protocol to enable both of them can be connected. To facilitate both relationships, Enum server- that is able to translate the numbering address such as PSTN (E.164) to URI address (Uniform Resource Identifier)- c
an be used. In this research, we interconnect Open IMS and Asterisk server Enum server. We then analyze the server performance and PDD (Post Dial Delay) values resulted by the system. As the result of the experiment, we found that, for a call from Open IMS user to analog Asterisk telephone (FXS) with a arrival call each servers is 30 call/sec, the maximum PDD value is 493.656 ms. Open IMS is able to serve maximum 30 call/s with computer processor 1.55 GHz, while the Asterisk with computer processor 3.0 GHz, may serve up to 55 call/sec. Enum on server with 1.15 GHz computer processor have the capability of serving maximum of 8156 queries/sec.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
