Optimization of PSWAN in terms of cost and bandwidth

PSWAN is an internetworking project undertaken by Govt. of India at Pondicherry. It covers a vast area, under it there are various state headquarters and district headquarters. Approximately 3000 systems are using its internet services. Since the number of systems are more and the bandwidth required is less so optimization was needed. Optimization was required without hardware modifications, so we defined some of the parameters through which we can achieve the optimization of this network, these parameters are 1. Type of protocol 2. Type of Topology 3. Access policies 4. Load balancing 5. Traffic bottle neck 6. Bandwidth utilization. To make the network cost effective, some small networks were moved to broadband network so that bandwidth usage can be mitigated and consequently network will get optimized. Since this project (PSWAN) is using the CISCO devices only so it was easy to simulate the network, we used OPNET simulator as it is precise than other simulators. First the operational network was simulated and then the proposed one, proposed model showed evident positive results. The simulation tool used is Opnet. OPNET is extensive and powerful simulation software with wide variety of capabilities. It enables the possibility to simulate entire heterogeneous networks with various protocol

An efficient location-based forwarding strategy for named data networking and LEO satellite communications

Low Earth orbit (LEO) satellite constellations are increasingly gaining attention as future global Internet providers. At the same time, named data networking (NDN) is a new data-centric architecture that has been recently proposed to replace the classic TCP/IP architecture since it is particularly well suited to the most common usage of the Internet nowadays as a content delivery network. Certainly, the use of NDN is especially convenient in highly dynamic network environments, such as those of next LEO constellations incorporating inter-satellite links (ISL). Among other native facilities, such as inbuilt security, NDN readily supports the mobility of clients, thus helping to overcome one of the main problems raised in LEO satellite networks. Moreover, thanks to a stateful forwarding plane with support for multicast transmission and inbuilt data caches, NDN is also able to provide a more efficient usage of the installed transmission capacity. In this paper, we propose a new location-based forwarding strategy for LEO satellite networks that takes advantage of the knowledge of the relative position of the satellites and the grid structure formed by the ISLs to perform the forwarding of NDN packets. So, forwarding at each node is done using only local information (node and destination locations), without the need of interchanging information between nodes, as is the case with conventional routing protocols. Using simulation, we show that the proposed forwarding strategy is a good candidate to promote the efficient and effective future use of the NDN architecture in LEO satellite networks.Ministerio de Ciencia e Innovación | Ref. PID2020-113240RB-I0

Reliability aware NoC router architecture using input channel buffer sharing

To address the increasing demand for reliability in on-chip networks, we proposed a novel Reliability Aware Virtual channel (RAVC) NoC router micro-architecture that enables both dynamic virtual channel allocations and the rational sharing among the buffers of different input channels. In particular, in the case of failure in routers, the virtual channels of routers surrounding the faulty routers can be totally recaptured and reassigned to other input ports. Moreover, our proposed RAVC router isolates the faulty router from occupying network bandwidth. Experimental result shows that proposed micro-architecture provides 7.1 % and 3.1 % average latency decrease under uniform and transpose traffic pattern. Considering the existence of failures in routers of on-chip network, RAVC provides 28 % and 16 % decrease in the average packet latency under the uniform and transpose traffic pattern respectively

The Effect Of Hot Spots On The Performance Of Mesh--Based Networks

Direct network performance is affected by different design parameters which include number of virtual channels, number of ports, routing algorithm, switching technique, deadlock handling technique, packet size, and buffer size. Another factor that affects network performance is the traffic pattern. In this thesis, we study the effect of hotspot traffic on system performance. Specifically, we study the effect of hotspot factor, hotspot number, and hot spot location on the performance of mesh-based networks. Simulations are run on two network topologies, both the mesh and torus. We pay more attention to meshes because they are widely used in commercial machines. Comparisons between oblivious wormhole switching and chaotic packet switching are reported. Overall packet switching proved to be more efficient in terms of throughput when compared to wormhole switching. In the case of uniform random traffic, it is shown that the differences between chaotic and oblivious routing are indistinguishable. Networks with low number of hotspots show better performance. As the number of hotspots increases network latency tends to increase. It is shown that when the hotspot factor increases, performance of packet switching is better than that of wormhole switching. It is also shown that the location of hotspots affects network performance particularly with the oblivious routers since their achieved latencies proved to be more vulnerable to changes in the hotspot location. It is also shown that the smaller the size of the network the earlier network saturation occurs. Further, it is shown that the chaos router’s adaptivity is useful in this case. Finally, for tori, performance is not greatly affected by hotspot presence. This is mostly due to the symmetric nature of tori

Schaltzeitprädiktion und Routenoptimierung für die Ampelassistenz in Smart Cities

Die zunehmende Digitalisierung und Vernetzung bietet im Automobilbereich und in urbanen Räumen Chancen für neue Assistenzfunktionen, die zu einem sicheren, komfortableren und effizienteren Fahren führen können. Das Potential durch die Nutzung gesammelter Daten ist enorm - so können Gefahren frühzeitig erkannt, Staus noch präziser vorhergesagt, oder die CO2-Emission reduziert werden. Insbesondere in urbanen Räumen ist die Lichtsignalanlage in ihrer Aufgabe der Steuerung von Verkehrsströmen hauptverantwortlich für viele Staus und Unfälle, aber auch für erhöhten Schadstoffausstoßwieder beschleunigender Fahrzeuge. Mit dem Wissen über die zukünftigen Signalzustände einer Lichtsignalanlage können diese negativen Nebeneffekte abgemildert werden. So kann mithilfe einer Grünband-Geschwindigkeitsempfehlung die Fahrzeuggeschwindigkeit so angepasst werden, dass ein Stopp vermieden wird. Eine Rotlichtüberfahrenswarnung kann unterstützen, Unfälle zu vermeiden, indem vor bevorstehenden Rotlichtverstößen gewarnt wird. Eine ampeladaptive Route kann wiederum Staus an roten Ampeln reduzieren. In der vorliegenden Arbeit werden neue Konzepte zur Prognose zukünftiger Signalzustände an koordinierten Knotenpunkten in Städten mithilfe von crowdsourcing-Daten erforscht. Einerseits wird hierzu der Aspekt einer vernetzten Schaltzeitprognose mittels Kommunikation zwischen Fahrzeugen und der Verkehrsinfrastruktur untersucht. Dazu wird ein Verfahren zur großflächigen Schaltzeitprognose in urbanen Räumen mithilfe vergangener Schaltzeitinformationen entwickelt. Die zentrale Herausforderung stellt hierbei, neben der Akquise von Daten, die Verkehrsabhängigkeit vieler Lichtsignalanlagen. Das entwickelte Verfahren wird anhand der Referenzstadt München implementiert und evaluiert. Darüber hinaus wird ein Verfahren zur Schätzung zukünftiger Signalzustände mithilfe von Daten aus dem Fahrzeugumfeld entwickelt. Entgegen des ersteren Ansatzes werden nicht mehr Verkehrszentralen als Datenquelle herangezogen, sondern gesammelte Daten aus dem Fahrzeug verwendet. Konkret wird die Floating Car Data Technologie auf die Nutzbarkeit zur Rekonstruktion und Schätzung von Schaltzeiten untersucht. Hierbei wird neben einer Potentialabschätzung der Technologie für dieses Einsatzszenario ein Modell zur Extraktion von Schaltzeitinformationen mittels gängiger Machine- Learning Methoden vorgestellt. Ein weiterer Forschungsschwerpunkt der vorliegenden Arbeit liegt auf der Konzeption eines dynamischen Routingverfahrens, welches die zukünftigen Schaltzeiten im Verkehrsnetz in die Routenwahl einbezieht. Ziel dieses ampeladaptiven Routingverfahrens ist es, die Wartezeit an Ampeln aufgrund von Rotzeiten zu minimieren und so die Reisezeit zu verkürzen. Hierzu müssen zunächst Verfahren entwickelt werden, um Unzulänglichkeiten in der bestehenden Graphstruktur in Navigationskarten effzient zu beheben, so dass Informationen über zukünftige Signalzustände an Knotenpunkten korrekt berücksichtigt werden können. Ferner wird eine Bewertung der tatsächlichen Reisezeitersparnis im realistischen Umfeld durchgeführt. Hierzu wird für ein Testfeld in München der Größe 100 km2 eine simulative Untersuchung des zu erwartenden Reisezeitgewinnes des ampeladaptiven Routingverfahrens im Vergleich zum klassischen Routingverfahren durchgeführt.The increasing digitization provides both for the automotive sector and for urban regions opportunities for new assisting functionalities, leading to a saver, more comfortable and more efficient driving. The potential through the use of collected data is enormous - risk can be detected in good time, jams be predicted more accurately and the CO2-emission be reduced. Especially in urban regions, the traffic light, in its task of managing traffic, is one of the main causes for jams, accidents, but also for increased exhaust emission by accelerating vehicles. With the knowledge of a traffic light's future signal states, these negative side-effects could be softened. Thus, a vehicles velocity could be adapted by green light optimal speed advisory so as to avoid a complete stop at a traffic light, a red light violation advisory may help avoiding accidents and a traffic light adaptive route can help reducing jams at crossings. In this thesis, new concepts for the prediction of future signal states at signalized crossings in cities based on crowdsourcing-data are the scope of research. On the one hand, the aspect of a connected switching time prediction through the communication between vehicles and traffic management institution is examined. For this purpose, a concept for the extensive switching time prediction with historical data is developed. The central challenge is thereby besides the acquisition of data the fact that many traffic lights are traffic adaptive. The proposed concept is implemented and evaluated for the city of Munich. Furthermore, a procedure for the estimation of future signal states based on data from the vehicle environment is developed. Opposed to the first approach, no longer data from public authorities is used, but data collected by vehicles. More precisely, the Floating Car Data Technology is examined for its usability to reconstruct and estimate switching times of traffic lights. At this, both an assessment of the potential of this technology for this use-case and a procedure for the extraction of switching time information by means of current machine learning methods is proposed. Another research focus of this thesis is the conception of a dynamic routing strategy taking into account the future switching times in a traffic network. The aim of this so-called traffic-adaptive Routing strategy is to reduce waiting times at traffic lights due to red phases and by that, to minimize the traveling time. To this, a method has to be developed so as to eliminate inadequacies of the underlying graph structure in navigation maps. This is necessary for a proper consideration of future signal states at signalized crossings. Moreover, an evaluation of the probable traveling time gain under realistic conditions is conducted. For that, a test field of 100 km^2 in Munich is chosen to estimate the expected traveling time gain of a traffic adaptive routing strategy over a standard routing strategy

Schaltzeitprädiktion und Routenoptimierung für die Ampelassistenz in Smart Cities

Die zunehmende Digitalisierung und Vernetzung bietet im Automobilbereich und in urbanen Räumen Chancen für neue Assistenzfunktionen, die zu einem sicheren, komfortableren und effizienteren Fahren führen können. Das Potential durch die Nutzung gesammelter Daten ist enorm - so können Gefahren frühzeitig erkannt, Staus noch präziser vorhergesagt, oder die CO2-Emission reduziert werden. Insbesondere in urbanen Räumen ist die Lichtsignalanlage in ihrer Aufgabe der Steuerung von Verkehrsströmen hauptverantwortlich für viele Staus und Unfälle, aber auch für erhöhten Schadstoffausstoßwieder beschleunigender Fahrzeuge. Mit dem Wissen über die zukünftigen Signalzustände einer Lichtsignalanlage können diese negativen Nebeneffekte abgemildert werden. So kann mithilfe einer Grünband-Geschwindigkeitsempfehlung die Fahrzeuggeschwindigkeit so angepasst werden, dass ein Stopp vermieden wird. Eine Rotlichtüberfahrenswarnung kann unterstützen, Unfälle zu vermeiden, indem vor bevorstehenden Rotlichtverstößen gewarnt wird. Eine ampeladaptive Route kann wiederum Staus an roten Ampeln reduzieren. In der vorliegenden Arbeit werden neue Konzepte zur Prognose zukünftiger Signalzustände an koordinierten Knotenpunkten in Städten mithilfe von crowdsourcing-Daten erforscht. Einerseits wird hierzu der Aspekt einer vernetzten Schaltzeitprognose mittels Kommunikation zwischen Fahrzeugen und der Verkehrsinfrastruktur untersucht. Dazu wird ein Verfahren zur großflächigen Schaltzeitprognose in urbanen Räumen mithilfe vergangener Schaltzeitinformationen entwickelt. Die zentrale Herausforderung stellt hierbei, neben der Akquise von Daten, die Verkehrsabhängigkeit vieler Lichtsignalanlagen. Das entwickelte Verfahren wird anhand der Referenzstadt München implementiert und evaluiert. Darüber hinaus wird ein Verfahren zur Schätzung zukünftiger Signalzustände mithilfe von Daten aus dem Fahrzeugumfeld entwickelt. Entgegen des ersteren Ansatzes werden nicht mehr Verkehrszentralen als Datenquelle herangezogen, sondern gesammelte Daten aus dem Fahrzeug verwendet. Konkret wird die Floating Car Data Technologie auf die Nutzbarkeit zur Rekonstruktion und Schätzung von Schaltzeiten untersucht. Hierbei wird neben einer Potentialabschätzung der Technologie für dieses Einsatzszenario ein Modell zur Extraktion von Schaltzeitinformationen mittels gängiger Machine- Learning Methoden vorgestellt. Ein weiterer Forschungsschwerpunkt der vorliegenden Arbeit liegt auf der Konzeption eines dynamischen Routingverfahrens, welches die zukünftigen Schaltzeiten im Verkehrsnetz in die Routenwahl einbezieht. Ziel dieses ampeladaptiven Routingverfahrens ist es, die Wartezeit an Ampeln aufgrund von Rotzeiten zu minimieren und so die Reisezeit zu verkürzen. Hierzu müssen zunächst Verfahren entwickelt werden, um Unzulänglichkeiten in der bestehenden Graphstruktur in Navigationskarten effzient zu beheben, so dass Informationen über zukünftige Signalzustände an Knotenpunkten korrekt berücksichtigt werden können. Ferner wird eine Bewertung der tatsächlichen Reisezeitersparnis im realistischen Umfeld durchgeführt. Hierzu wird für ein Testfeld in München der Größe 100 km2 eine simulative Untersuchung des zu erwartenden Reisezeitgewinnes des ampeladaptiven Routingverfahrens im Vergleich zum klassischen Routingverfahren durchgeführt.The increasing digitization provides both for the automotive sector and for urban regions opportunities for new assisting functionalities, leading to a saver, more comfortable and more efficient driving. The potential through the use of collected data is enormous - risk can be detected in good time, jams be predicted more accurately and the CO2-emission be reduced. Especially in urban regions, the traffic light, in its task of managing traffic, is one of the main causes for jams, accidents, but also for increased exhaust emission by accelerating vehicles. With the knowledge of a traffic light's future signal states, these negative side-effects could be softened. Thus, a vehicles velocity could be adapted by green light optimal speed advisory so as to avoid a complete stop at a traffic light, a red light violation advisory may help avoiding accidents and a traffic light adaptive route can help reducing jams at crossings. In this thesis, new concepts for the prediction of future signal states at signalized crossings in cities based on crowdsourcing-data are the scope of research. On the one hand, the aspect of a connected switching time prediction through the communication between vehicles and traffic management institution is examined. For this purpose, a concept for the extensive switching time prediction with historical data is developed. The central challenge is thereby besides the acquisition of data the fact that many traffic lights are traffic adaptive. The proposed concept is implemented and evaluated for the city of Munich. Furthermore, a procedure for the estimation of future signal states based on data from the vehicle environment is developed. Opposed to the first approach, no longer data from public authorities is used, but data collected by vehicles. More precisely, the Floating Car Data Technology is examined for its usability to reconstruct and estimate switching times of traffic lights. At this, both an assessment of the potential of this technology for this use-case and a procedure for the extraction of switching time information by means of current machine learning methods is proposed. Another research focus of this thesis is the conception of a dynamic routing strategy taking into account the future switching times in a traffic network. The aim of this so-called traffic-adaptive Routing strategy is to reduce waiting times at traffic lights due to red phases and by that, to minimize the traveling time. To this, a method has to be developed so as to eliminate inadequacies of the underlying graph structure in navigation maps. This is necessary for a proper consideration of future signal states at signalized crossings. Moreover, an evaluation of the probable traveling time gain under realistic conditions is conducted. For that, a test field of 100 km^2 in Munich is chosen to estimate the expected traveling time gain of a traffic adaptive routing strategy over a standard routing strategy

Performance evaluation of distributed crossbar switch hypermesh

The interconnection network is one of the most crucial components in any multicomputer as it greatly influences the overall system performance. Several recent studies have suggested that hypergraph networks, such as the Distributed Crossbar Switch Hypermesh (DCSH), exhibit superior topological and performance characteristics over many traditional graph networks, e.g. k-ary n-cubes. Previous work on the DCSH has focused on issues related to implementation and performance comparisons with existing networks. These comparisons have so far been confined to deterministic routing and unicast (one-to-one) communication. Using analytical models validated through simulation experiments, this thesis extends that analysis to include adaptive routing and broadcast communication. The study concentrates on wormhole switching, which has been widely adopted in practical multicomputers, thanks to its low buffering requirement and the reduced dependence of latency on distance under low traffic. Adaptive routing has recently been proposed as a means of improving network performance, but while the comparative evaluation of adaptive and deterministic routing has been widely reported in the literature, the focus has been on graph networks. The first part of this thesis deals with adaptive routing, developing an analytical model to measure latency in the DCSH, and which is used throughout the rest of the work for performance comparisons. Also, an investigation of different routing algorithms in this network is presented. Conventional k-ary n-cubes have been the underlying topology of contemporary multicomputers, but it is only recently that adaptive routing has been incorporated into such systems. The thesis studies the relative performance merits of the DCSH and k-ary n-cubes under adaptive routing strategy. The analysis takes into consideration real-world factors, such as router complexity and bandwidth constraints imposed by implementation technology. However, in any network, the routing of unicast messages is not the only factor in traffic control. In many situations (for example, parallel iterative algorithms, memory update and invalidation procedures in shared memory systems, global notification of network errors), there is a significant requirement for broadcast traffic. The DCSH, by virtue of its use of hypergraph links, can implement broadcast operations particularly efficiently. The second part of the thesis examines how the DCSH and k-ary n-cube performance is affected by the presence of a broadcast traffic component. In general, these studies demonstrate that because of their relatively high diameter, k-ary n-cubes perform poorly when message lengths are short. This is consistent with earlier more simplistic analyses which led to the proposal for the express-cube, an enhancement of the basic k-ary n-cube structure, which provides additional express channels, allowing messages to bypass groups of nodes along their paths. The final part of the thesis investigates whether this "partial bypassing" can compete with the "total bypassing" capability provided inherently by the DCSH topology