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The Global ETD Search service is a free service for researchers
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Showing 21 to 23 of 23 (0.066 seconds)Spelling suggestions: "subject:"fördröjning"" "subject:"enfördröjning""
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Service Aware Traffic Distribution in Heterogeneous A2G NetworksTomic, David January 2019 (has links)
Airplanes have different ways to connect to the ground, including satellite air-to-ground communication (SA2GC) and direct air-to-ground communication (DA2GC). Each connection/link offers a different varying amount of transmission capacity over flight time. The traffic generated in the airplane must be forwarded/sent to ground over the available links. It is however not clear how the traffic should be forwarded so that traffic quality of service (QoS) requirements are met. The thesis at hand considers this question, and implements an algorithm handling the forwarding decision with three different forwarding schemes. Those consider traffic parameters in calculating a value assigned to each traffic flow, over a combination of priority, delay requirement and the number of times a traffic flow is dropped. The forwarding algorithm relies on proposed in-flight broadband connectivity (IFBC) network traffic and air-to-ground (A2G) link models, which aim at approximating the network environment of future IFBC networks. It is shown that QoS requirements of traffic flows in terms of packet loss and delay cannot be satisfied with capacities offered by current DA2GC and SA2GC technology. For a future scenario, with higher assumed link capacities, the QoS requirements are met to a higher extent. This is shown in lower packet loss and delay experienced by the respective traffic flows. Further, it is shown that the performance can be improved with specific forwarding schemes used by the forwarding algorithm. It is also investigated how a web cache can be used as a fallback technology. For this a required web cache hit rate is found, which should be high enough to offload the network with content served from the cache. Overall, the thesis aims at proposing an efficient traffic forwarding technique, and at giving insight into an alternative if this technique fails. / Flygplan har olika sätt att ansluta till marken, inklusive satellit-mark-kommunikation (SA2GC) och direkt luft till markkommunikation (DA2GC). Varje anslutning/länk erbjuder en annan varierande mängd överföringskapacitet under flygtid. Den trafik som genereras i flygplanet måste vidarebefordras/skickas till marken över de tillgängliga länkarna. Det är emellertid inte klart hur trafiken ska vidarebefordras så att trafiksäkerhetskvaliteten (QoS) uppfylls. Avhandlingen handlar om denna fråga och implementerar en algoritm som hanterar vidarebefordringsbeslutet med tre olika vidarebefordringssystem. De betraktar trafikparametrar vid beräkning av ett värde som tilldelas varje trafikflöde, över en kombination av prioritet, fördröjningskrav och antalet gånger ett trafikflöde tappas. Vidarebefordringsalgoritmen är beroende av föreslagna bredbandsförbindelser (IFBC) i nätverk och A2G-länkmodeller, som syftar till att approximera nätverksmiljön för framtida IFBC-nätverk. Det visas att QoS-krav på trafikflöden när det gäller paketförlust och fördröjning inte kan tillgodoses med kapacitet som erbjuds av nuvarande DA2GC- och SA2GC-teknik. För ett framtida scenario, med högre antagna länkkapacitet, uppfylls QoS-kraven i högre utsträckning. Detta visas med lägre paketförlust och fördröjning som upplevs av respektive trafikflöden. Vidare är det visat att prestanda kan förbättras med specifika vidarekopplingsscheman som används av vidarebefordringsalgoritmen. Det undersöks också hur en webbcache kan användas som en återgångsteknik. För detta hittas en obligatorisk webbcache-träfffrekvens, som bör vara tillräckligt hög för att ladda upp nätverket med innehåll som serveras från cacheminnet. Sammanfattningsvis syftar uppsatsen till att föreslå en effektiv trafiköverföringsteknik och att ge insikt om ett alternativ om denna teknik misslyckas.
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Toward Highly-efficient GPU-centric Networking / Mot Högeffektiva GPU-centrerade NätverkGirondi, Massimo January 2024 (has links)
Graphics Processing Units (GPUs) are emerging as the most popular accelerator for many applications, powering the core of Machine Learning applications and many computing-intensive workloads. GPUs have typically been consideredas accelerators, with Central Processing Units (CPUs) in charge of the mainapplication logic, data movement, and network connectivity. In these architectures,input and output data of network-based GPU-accelerated application typically traverse the CPU, and the Operating System network stack multiple times, getting copied across the system main memory. These increase application latency and require expensive CPU cycles, reducing the power efficiency of systems, and increasing the overall response times. These inefficiencies become of higher importance in latency-bounded deployments, or with high throughput, where copy times could easily inflate the response time of modern GPUs. The main contribution of this dissertation is towards a GPU-centric network architecture, allowing GPUs to initiate network transfers without the intervention of CPUs. We focus on commodity hardware, using NVIDIA GPUs and Remote Direct Memory Access over Converged Ethernet (RoCE) to realize this architecture, removing the need of highly homogeneous clusters and ad-hoc designed network architecture, as it is required by many other similar approaches. By porting some rdma-core posting routines to GPU runtime, we can saturate a 100-Gbps link without any CPU cycle, reducing the overall system response time, while increasing the power efficiency and improving the application throughput.The second contribution concerns the analysis of Clockwork, a State-of-The-Art inference serving system, showing the limitations imposed by controller-centric, CPU-mediated architectures. We then propose an alternative architecture to this system based on an RDMA transport, and we study some performance gains that such a system would introduce. An integral component of an inference system is to account and track user flows,and distribute them across multiple worker nodes. Our third contribution aims to understand the challenges of Connection Tracking applications running at 100Gbps, in the context of a Stateful Load Balancer running on commodity hardware. / <p>QC 20240315</p>
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Dynamic Speed Adaptation for Curves using Machine Learning / Dynamisk hastighetsanpassning för kurvor med maskininlärningNarmack, Kirilll January 2018 (has links)
The vehicles of tomorrow will be more sophisticated, intelligent and safe than the vehicles of today. The future is leaning towards fully autonomous vehicles. This degree project provides a data driven solution for a speed adaptation system that can be used to compute a vehicle speed for curves, suitable for the underlying driving style of the driver, road properties and weather conditions. A speed adaptation system for curves aims to compute a vehicle speed suitable for curves that can be used in Advanced Driver Assistance Systems (ADAS) or in Autonomous Driving (AD) applications. This degree project was carried out at Volvo Car Corporation. Literature in the field of speed adaptation systems and factors affecting the vehicle speed in curves was reviewed. Naturalistic driving data was both collected by driving and extracted from Volvo's data base and further processed. A novel speed adaptation system for curves was invented, implemented and evaluated. This speed adaptation system is able to compute a vehicle speed suitable for the underlying driving style of the driver, road properties and weather conditions. Two different artificial neural networks and two mathematical models were used to compute the desired vehicle speed in curves. These methods were compared and evaluated. / Morgondagens fordon kommer att vara mer sofistikerade, intelligenta och säkra än dagens fordon. Framtiden lutar mot fullständigt autonoma fordon. Detta examensarbete tillhandahåller en datadriven lösning för ett hastighetsanpassningssystem som kan beräkna ett fordons hastighet i kurvor som är lämpligt för förarens körstil, vägens egenskaper och rådande väder. Ett hastighetsanpassningssystem för kurvor har som mål att beräkna en fordonshastighet för kurvor som kan användas i Advanced Driver Assistance Systems (ADAS) eller Autonomous Driving (AD) applikationer. Detta examensarbete utfördes på Volvo Car Corporation. Litteratur kring hastighetsanpassningssystem samt faktorer som påverkar ett fordons hastighet i kurvor studerades. Naturalistisk bilkörningsdata samlades genom att köra bil samt extraherades från Volvos databas och bearbetades. Ett nytt hastighetsanpassningssystem uppfanns, implementerades samt utvärderades. Hastighetsanpassningssystemet visade sig vara kapabelt till att beräkna en lämplig fordonshastighet för förarens körstil under rådande väderförhållanden och vägens egenskaper. Två olika artificiella neuronnätverk samt två matematiska modeller användes för att beräkna fordonets hastighet. Dessa metoder jämfördes och utvärderades.
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