China
Africa
Explore chapters and articles related to this topic
Concept of IP-Address Lookup and Routing Table
Published in Weidong Wu, Packet Forwarding Technologies, 2007
The IPv4 address space includes several “special use” prefixes that have been reserved by the Internet Assigned Numbers Authority (IANA). Network operators should not accept or send advertisements for these addresses (called Martians), as summarized in Table 2.4. The class A block 0.0.0.0/8 includes the address 0.0.0.0 which is commonly used for default routes. The 127.0.0.0/8 prefix is reserved for loop-back addresses used by a host or router to identify itself. Three prefixes are reserved for private networks that use the IP protocols, as discussed in RFC 1918 [21]. The 224.0.0.0/3 block is devoted to class D (multicast) and class E (reserved) addresses. The 169.254.0.0/16 block is dedicated for auto-configuration of hosts when no dynamic host configuration protocol (DHCP) server is available. The prefix 192.0.2.0/24 is used for example IP addresses in documentation and code fragments. Several prefixes are allocated to the infrastructure at the public Internet exchange points. In addition, prefix 128.0.0.0/16 is reserved by IANA.
High-Performance Switch/Routers
Published in James Aweya, Designing Switch/Routers, 2023
The switch/routers may support DHCP client-based auto-configuration features, simplifying user deployment and configuration (plug-and-play). Enterprises can use this feature to automate IP address and feature configuration without the presence of a highly trained network administrator on-site. Technicians can simply power up a switch/router and the unit will automatically get its IP address and configuration from DHCP and TFTP servers. These sophisticated features make converged network services easier to install, manage, and upgrade – and they significantly reduce operations costs.
Knowledge-based cyber-physical systems for assembly automation
Published in Production & Manufacturing Research, 2019
Munir Merdan, Timon Hoebert, Erhard List, Wilfried Lepuschitz
As most CPS involve the cooperation of a high number of components (Kang, Kapitanova, & Son, 2012), explicitly programming the relationships between the system components and considering the quantity of interrelationships related to failures poses significant challenges (Wang & Lin, 2009). In this context, automatic configuration is proposed as a reasonable means for making a system scalable and robust in the presence of changes and for supporting dynamic adaptation (Kramer & Magee, 2007). Hence, technologies and methodologies are required for supporting the development process of large-scale CPS with physically distributed resources (Wolf, 2009). Model-driven engineering is a software development methodology which exploits domain models as a solution to handle the complexity of software development by raising the abstraction level and automating labor-intensive and error-prone tasks (Vyatkin, 2013). The approach has been promoted as a promising methodology for designing control applications of CPS (Balaji, Al Faruque, Dutt, Gupta, & Agarwal, 2015; Thramboulidis, Bochalis, & Bouloumpasis, 2017). Models allow domain specific software descriptions reflecting the heterogeneity of the developed system and may serve as primary development artifacts, which increases the softwares comprehensibility and reusability (Ringert, Roth, Rumpe, & Wortmann, 2015). Besides, defined models are meant to be much more human-oriented than common code artefacts, which are naturally machine-oriented and software can be defined with concepts that are not necessarily dependent on the underlying platform or technology (Ciccozzi & Spalazzese, 2016). Furthermore, to develop a self-configurable CPS, a model-based approach may well be beneficial (Hehenberger et al., 2016). Self-configuration refers to the automatic deployment of a system configuration in response to changes in the environment. This system configuration is allocated to available hardware resources for instance by an autonomic service manager without any developer efforts (Dai, Dubinin, Christensen, Vyatkin, & Guan, 2017). Achieving a reliable auto-configuration management is an important challenge that has to be solved for enabling plug-and-play components, which autonomously embed themselves in the CPS without any special initialization procedure or elaborate human involvement (Reinhart et al., 2010). By using automatic configuration instead of manually adapting control software, engineering time and costs are reduced while increasing the softwares quality (Fischer, Vogel-Heuser, & Friedrich, 2015). This is especially of significance concerning the deployment and maintenance of nodes in large networks of small devices, e.g. sensor networks, as their manual configuration is rather impractical (Lanthaler, n.d.; Vicaire, Hoque, Xie, & Stankovic, 2012).