When network congestion occurs, routers become overloaded and either cannot forward packets fast enough or must discard queued packets to make room for new arrivals. Congestion is caused by packet arrival rates exceeding link capacity, insufficient memory, bursty traffic, or slow processors. Congestion control aims to efficiently use the network at high load and involves all routers and hosts, while flow control operates point-to-point between sender and receiver. Congestion control techniques include warning bits, choke packets, load shedding, random early discard, and traffic shaping to detect, recover from, and avoid congestion.
The document discusses congestion control in computer networks. It defines congestion as occurring when the load on a network is greater than the network's capacity. Congestion control aims to control congestion and keep the load below capacity. The document outlines two categories of congestion control: open-loop control, which aims to prevent congestion; and closed-loop control, which detects congestion and takes corrective action using feedback from the network. Specific open-loop techniques discussed include admission control, traffic shaping using leaky bucket and token bucket algorithms, and traffic scheduling.
The document discusses congestion control in computer networks. It defines congestion as occurring when the load on a network is greater than the network's capacity. Congestion control aims to control congestion and keep the load below capacity. The document separates congestion control mechanisms into two categories: open-loop control, which aims to prevent congestion; and closed-loop control, which detects congestion and takes corrective actions through feedback. Specific open-loop techniques discussed are admission control, traffic shaping using leaky bucket and token bucket algorithms, and traffic scheduling.
This document discusses computer networks and congestion control techniques. It provides information on routing algorithms, causes of congestion, effects of congestion, and open-loop and closed-loop congestion control methods. Specifically, it describes the leaky bucket algorithm and token bucket algorithm for traffic shaping, and how they regulate data flow rates to prevent network congestion.
The document discusses various congestion control algorithms and quality of service techniques used in computer networks. It describes approaches like traffic-aware routing, admission control, traffic throttling, and load shedding to control congestion. It also explains how quality of service is achieved through integrated services, differentiated services, and techniques like traffic shaping, packet scheduling, buffering, and jitter control.
APNIC Chief Scientist Geoff Huston gives a presentation on Buffers, Buffer Bloat and BBR at NZNOG 2020 in Christchurch, New Zealand, from 28 to 31 January 2020.
This document discusses congestion control and internetworking at the network layer. It begins by defining congestion and the factors that can cause it. It then covers general principles of congestion control such as increasing resources or decreasing traffic. The document discusses congestion control techniques for virtual circuit and datagram subnets, including admission control and choke packets. It also covers internetworking concepts like concatenated virtual circuits, connectionless internetworking, tunneling, and fragmentation.
The document discusses different routing methods used in computer networks, including:
- Network-specific routing which treats all hosts on the same network as a single entity in the routing table.
- Host-specific routing which explicitly defines routes to individual host addresses in the routing table.
- Default routing which uses a single default route for all unknown destinations.
It also covers routing protocols like RIP and OSPF, explaining how they establish and maintain routing tables dynamically as the network changes. Distance vector protocols like RIP propagate full routing tables between routers, while link-state protocols like OSPF flood link state information to build independent views of the network topology.
The document discusses quality of service (QoS) techniques in computer networks. It describes four characteristics of data flows: reliability, delay, jitter, and bandwidth. It then discusses several QoS mechanisms including flow classes, scheduling, traffic shaping using leaky bucket and token bucket algorithms, resource reservation, admission control, Integrated Services (IntServ) model, and Differentiated Services (DiffServ) model. The IntServ model provides per-flow reservations using RSVP, while the DiffServ model provides class-based service using traffic conditioners and per-hop behaviors.
This document discusses quality of service (QoS) networking. It will cover topics like queue management, traffic shaping, admission control, routing protocols, Integrated Services, Differentiated Services, MPLS, and traffic engineering. The course will include proposals, paper presentations, quizzes, and participation. QoS aims to provide predictable network performance by prioritizing some types of traffic over others. It allows resources to be allocated to high priority services at the expense of lower priority traffic. The document discusses challenges in providing these guarantees and techniques like resource reservation, traffic contracts, scheduling algorithms, and statistical approaches.
The transport layer provides process-to-process communication between applications on networked devices. It handles addressing with port numbers, encapsulation/decapsulation of data, multiplexing/demultiplexing data to the correct processes, flow control to prevent buffer overflows, error control with packet sequencing and acknowledgments, and congestion control to regulate data transmission and avoid overwhelming network switches and routers. Key functions of the transport layer enable reliable data transfer between applications across the internet.
This document discusses TCP and a new flow control algorithm called BBR. It provides background on TCP and how its sending rate is controlled via ACK pacing. While TCP rates increased from kilobits to gigabits per second over time, it is not keeping up with optical transmission speeds approaching terabits. BBR aims to be more efficient than TCP by probing the network to detect the onset of queueing rather than relying on packet loss. Testing shows BBR can crowd out other flows and operate inefficiently against itself. While promising for high speeds, BBR may not scale well if widely adopted and requires further research to improve fairness against other flows.
This document discusses switching, routing, and flow control in interconnection networks. It covers different switching mechanisms like packet switching and circuit switching. It also discusses routing algorithms and techniques to avoid deadlocks like virtual channels and deadlock-free routing. The key topics are how packets are routed through switches, challenges like tree saturation and deadlocks, and approaches to provide reliable communication while matching the capabilities of the network hardware.
Traffic characterization parameters like bandwidth, delay, and jitter requirements are used to specify network traffic flows. Traffic shaping techniques like leaky bucket and token bucket regulate traffic into defined patterns to facilitate admission control and traffic policing. The leaky bucket traffic shaper uses a finite bucket that leaks data out at a constant rate to shape traffic bursts according to the bucket size and leak rate. Queue scheduling disciplines like weighted fair queueing determine which packet is served next to affect packet delay, bandwidth, and jitter. Resource reservation protocols negotiate quality of service guarantees by reserving required network resources.
Bit stuffing adds an extra 0 bit whenever there are five consecutive 1s in data to prevent the receiver from mistaking the data for a flag. Congestion control techniques like warning bits, choke packets, load shedding, random early discard, and traffic shaping are used to efficiently manage network traffic during periods of high load. Traffic shaping algorithms like the leaky bucket and token bucket algorithms control transmission rates to smooth bursts and reduce congestion. The leaky bucket discards packets when the buffer overflows, while the token bucket does not discard packets but instead discards tokens.
High performance browser networking ch1,2,3Seung-Bum Lee
Presentation material including summary of "High Performance Browser Networking" by Ilya Grigorik. This book includes very good summary of computer network not only for internet browsing but also multimedia streaming.
This presentation about Conjestion control will enrich your knowledge about this topic.and use this presentation for your reference this presentation with the Leaky bucket algorithm.
1. The document discusses quality of service (QoS) mechanisms in computer networks. It describes the differences between best effort and QoS networks and outlines two styles of QoS - worst-case and average-case.
2. It then covers basic QoS mechanisms like leaky buckets and token buckets that are used to police traffic entering the network. Integrated Services (IntServ) and Differentiated Services (DiffServ) models for providing QoS are also introduced.
3. Resource reservation protocols like RSVP are explained, including how they set up reservation state along network paths using PATH and RESV messages to signal bandwidth requirements from end hosts to routers.
1. The document discusses quality of service (QoS) mechanisms in computer networks. It covers topics like best effort vs. QoS service, resource reservation using leaky and token buckets, Integrated Services (IntServ) and Differentiated Services (DiffServ) architectures, and economics of QoS.
2. It provides details on basic QoS mechanisms like leaky and token buckets that are used to police resource reservations. It also describes the IntServ and RSVP signaling protocol that is used for per-flow reservation in the IntServ architecture.
3. The document outlines different reservation styles in RSVP like fixed, shared explicit, and wildcard filters that determine how reservations can be shared among multiple
1. The document discusses quality of service (QoS) mechanisms in computer networks. It describes the differences between best effort service and QoS, which aims to provide guarantees for bandwidth, latency, and jitter.
2. The document outlines two main QoS architectures - Integrated Services (IntServ) which provides per-flow reservations and Differentiated Services (DiffServ) which uses traffic classes. It also discusses resource reservation using leaky and token bucket algorithms.
3. RSVP is described as the signaling protocol used to establish per-flow state through PATH and RESV messages. It supports different reservation styles like fixed, shared explicit, and wildcard filters to efficiently share resources among senders.
1. The document discusses quality of service (QoS) mechanisms in computer networks, including leaky and token buckets used to police traffic and provide bandwidth guarantees.
2. It describes Integrated Services (IntServ) and Differentiated Services (DiffServ) approaches to implementing QoS.
3. Key aspects of QoS covered include resource reservation, admission control, scheduling, and the use of RSVP signaling to set up reservations along network paths.
What is Quality of Service?
-Basic mechanisms
-Leaky and token buckets
-Integrated Services (IntServ)
-Differentiated Services (DiffServ)
-Economics and Social factors facing QoS
-QoS Vs. Over Provisioning
Introduction, Virtual and Datagram networks, study of router, IP protocol and addressing in the Internet, Routing algorithms, Broadcast and Multicast routing
The document discusses different routing methods used in computer networks, including:
- Network-specific routing which treats all hosts on the same network as a single entity in the routing table.
- Host-specific routing which explicitly defines routes to individual host addresses in the routing table.
- Default routing which uses a single default route for all unknown destinations.
It also covers routing protocols like RIP and OSPF, explaining how they establish and maintain routing tables dynamically as the network changes. Distance vector protocols like RIP propagate full routing tables between routers, while link-state protocols like OSPF flood link state information to build independent views of the network topology.
The document discusses quality of service (QoS) techniques in computer networks. It describes four characteristics of data flows: reliability, delay, jitter, and bandwidth. It then discusses several QoS mechanisms including flow classes, scheduling, traffic shaping using leaky bucket and token bucket algorithms, resource reservation, admission control, Integrated Services (IntServ) model, and Differentiated Services (DiffServ) model. The IntServ model provides per-flow reservations using RSVP, while the DiffServ model provides class-based service using traffic conditioners and per-hop behaviors.
This document discusses quality of service (QoS) networking. It will cover topics like queue management, traffic shaping, admission control, routing protocols, Integrated Services, Differentiated Services, MPLS, and traffic engineering. The course will include proposals, paper presentations, quizzes, and participation. QoS aims to provide predictable network performance by prioritizing some types of traffic over others. It allows resources to be allocated to high priority services at the expense of lower priority traffic. The document discusses challenges in providing these guarantees and techniques like resource reservation, traffic contracts, scheduling algorithms, and statistical approaches.
The transport layer provides process-to-process communication between applications on networked devices. It handles addressing with port numbers, encapsulation/decapsulation of data, multiplexing/demultiplexing data to the correct processes, flow control to prevent buffer overflows, error control with packet sequencing and acknowledgments, and congestion control to regulate data transmission and avoid overwhelming network switches and routers. Key functions of the transport layer enable reliable data transfer between applications across the internet.
This document discusses TCP and a new flow control algorithm called BBR. It provides background on TCP and how its sending rate is controlled via ACK pacing. While TCP rates increased from kilobits to gigabits per second over time, it is not keeping up with optical transmission speeds approaching terabits. BBR aims to be more efficient than TCP by probing the network to detect the onset of queueing rather than relying on packet loss. Testing shows BBR can crowd out other flows and operate inefficiently against itself. While promising for high speeds, BBR may not scale well if widely adopted and requires further research to improve fairness against other flows.
This document discusses switching, routing, and flow control in interconnection networks. It covers different switching mechanisms like packet switching and circuit switching. It also discusses routing algorithms and techniques to avoid deadlocks like virtual channels and deadlock-free routing. The key topics are how packets are routed through switches, challenges like tree saturation and deadlocks, and approaches to provide reliable communication while matching the capabilities of the network hardware.
Traffic characterization parameters like bandwidth, delay, and jitter requirements are used to specify network traffic flows. Traffic shaping techniques like leaky bucket and token bucket regulate traffic into defined patterns to facilitate admission control and traffic policing. The leaky bucket traffic shaper uses a finite bucket that leaks data out at a constant rate to shape traffic bursts according to the bucket size and leak rate. Queue scheduling disciplines like weighted fair queueing determine which packet is served next to affect packet delay, bandwidth, and jitter. Resource reservation protocols negotiate quality of service guarantees by reserving required network resources.
Bit stuffing adds an extra 0 bit whenever there are five consecutive 1s in data to prevent the receiver from mistaking the data for a flag. Congestion control techniques like warning bits, choke packets, load shedding, random early discard, and traffic shaping are used to efficiently manage network traffic during periods of high load. Traffic shaping algorithms like the leaky bucket and token bucket algorithms control transmission rates to smooth bursts and reduce congestion. The leaky bucket discards packets when the buffer overflows, while the token bucket does not discard packets but instead discards tokens.
High performance browser networking ch1,2,3Seung-Bum Lee
Presentation material including summary of "High Performance Browser Networking" by Ilya Grigorik. This book includes very good summary of computer network not only for internet browsing but also multimedia streaming.
This presentation about Conjestion control will enrich your knowledge about this topic.and use this presentation for your reference this presentation with the Leaky bucket algorithm.
1. The document discusses quality of service (QoS) mechanisms in computer networks. It describes the differences between best effort and QoS networks and outlines two styles of QoS - worst-case and average-case.
2. It then covers basic QoS mechanisms like leaky buckets and token buckets that are used to police traffic entering the network. Integrated Services (IntServ) and Differentiated Services (DiffServ) models for providing QoS are also introduced.
3. Resource reservation protocols like RSVP are explained, including how they set up reservation state along network paths using PATH and RESV messages to signal bandwidth requirements from end hosts to routers.
1. The document discusses quality of service (QoS) mechanisms in computer networks. It covers topics like best effort vs. QoS service, resource reservation using leaky and token buckets, Integrated Services (IntServ) and Differentiated Services (DiffServ) architectures, and economics of QoS.
2. It provides details on basic QoS mechanisms like leaky and token buckets that are used to police resource reservations. It also describes the IntServ and RSVP signaling protocol that is used for per-flow reservation in the IntServ architecture.
3. The document outlines different reservation styles in RSVP like fixed, shared explicit, and wildcard filters that determine how reservations can be shared among multiple
1. The document discusses quality of service (QoS) mechanisms in computer networks. It describes the differences between best effort service and QoS, which aims to provide guarantees for bandwidth, latency, and jitter.
2. The document outlines two main QoS architectures - Integrated Services (IntServ) which provides per-flow reservations and Differentiated Services (DiffServ) which uses traffic classes. It also discusses resource reservation using leaky and token bucket algorithms.
3. RSVP is described as the signaling protocol used to establish per-flow state through PATH and RESV messages. It supports different reservation styles like fixed, shared explicit, and wildcard filters to efficiently share resources among senders.
1. The document discusses quality of service (QoS) mechanisms in computer networks, including leaky and token buckets used to police traffic and provide bandwidth guarantees.
2. It describes Integrated Services (IntServ) and Differentiated Services (DiffServ) approaches to implementing QoS.
3. Key aspects of QoS covered include resource reservation, admission control, scheduling, and the use of RSVP signaling to set up reservations along network paths.
What is Quality of Service?
-Basic mechanisms
-Leaky and token buckets
-Integrated Services (IntServ)
-Differentiated Services (DiffServ)
-Economics and Social factors facing QoS
-QoS Vs. Over Provisioning
Introduction, Virtual and Datagram networks, study of router, IP protocol and addressing in the Internet, Routing algorithms, Broadcast and Multicast routing
Welcome to the May 2025 edition of WIPAC Monthly celebrating the 14th anniversary of the WIPAC Group and WIPAC monthly.
In this edition along with the usual news from around the industry we have three great articles for your contemplation
Firstly from Michael Dooley we have a feature article about ammonia ion selective electrodes and their online applications
Secondly we have an article from myself which highlights the increasing amount of wastewater monitoring and asks "what is the overall" strategy or are we installing monitoring for the sake of monitoring
Lastly we have an article on data as a service for resilient utility operations and how it can be used effectively.
This research is oriented towards exploring mode-wise corridor level travel-time estimation using Machine learning techniques such as Artificial Neural Network (ANN) and Support Vector Machine (SVM). Authors have considered buses (equipped with in-vehicle GPS) as the probe vehicles and attempted to calculate the travel-time of other modes such as cars along a stretch of arterial roads. The proposed study considers various influential factors that affect travel time such as road geometry, traffic parameters, location information from the GPS receiver and other spatiotemporal parameters that affect the travel-time. The study used a segment modeling method for segregating the data based on identified bus stop locations. A k-fold cross-validation technique was used for determining the optimum model parameters to be used in the ANN and SVM models. The developed models were tested on a study corridor of 59.48 km stretch in Mumbai, India. The data for this study were collected for a period of five days (Monday-Friday) during the morning peak period (from 8.00 am to 11.00 am). Evaluation scores such as MAPE (mean absolute percentage error), MAD (mean absolute deviation) and RMSE (root mean square error) were used for testing the performance of the models. The MAPE values for ANN and SVM models are 11.65 and 10.78 respectively. The developed model is further statistically validated using the Kolmogorov-Smirnov test. The results obtained from these tests proved that the proposed model is statistically valid.
Welcome to MIND UP: a special presentation for Cloudvirga, a Stewart Title company. In this session, we’ll explore how you can “mind up” and unlock your potential by using generative AI chatbot tools at work.
Curious about the rise of AI chatbots? Unsure how to use them-or how to use them safely and effectively in your workplace? You’re not alone. This presentation will walk you through the practical benefits of generative AI chatbots, highlight best practices for safe and responsible use, and show how these tools can help boost your productivity, streamline tasks, and enhance your workday.
Whether you’re new to AI or looking to take your skills to the next level, you’ll find actionable insights to help you and your team make the most of these powerful tools-while keeping security, compliance, and employee well-being front and center.
David Boutry - Specializes In AWS, Microservices And PythonDavid Boutry
With over eight years of experience, David Boutry specializes in AWS, microservices, and Python. As a Senior Software Engineer in New York, he spearheaded initiatives that reduced data processing times by 40%. His prior work in Seattle focused on optimizing e-commerce platforms, leading to a 25% sales increase. David is committed to mentoring junior developers and supporting nonprofit organizations through coding workshops and software development.
Jacob Murphy Australia - Excels In Optimizing Software ApplicationsJacob Murphy Australia
In the world of technology, Jacob Murphy Australia stands out as a Junior Software Engineer with a passion for innovation. Holding a Bachelor of Science in Computer Science from Columbia University, Jacob's forte lies in software engineering and object-oriented programming. As a Freelance Software Engineer, he excels in optimizing software applications to deliver exceptional user experiences and operational efficiency. Jacob thrives in collaborative environments, actively engaging in design and code reviews to ensure top-notch solutions. With a diverse skill set encompassing Java, C++, Python, and Agile methodologies, Jacob is poised to be a valuable asset to any software development team.
Newly poured concrete opposing hot and windy conditions is considerably susceptible to plastic shrinkage cracking. Crack-free concrete structures are essential in ensuring high level of durability and functionality as cracks allow harmful instances or water to penetrate in the concrete resulting in structural damages, e.g. reinforcement corrosion or pressure application on the crack sides due to water freezing effect. Among other factors influencing plastic shrinkage, an important one is the concrete surface humidity evaporation rate. The evaporation rate is currently calculated in practice by using a quite complex Nomograph, a process rather tedious, time consuming and prone to inaccuracies. In response to such limitations, three analytical models for estimating the evaporation rate are developed and evaluated in this paper on the basis of the ACI 305R-10 Nomograph for “Hot Weather Concreting”. In this direction, several methods and techniques are employed including curve fitting via Genetic Algorithm optimization and Artificial Neural Networks techniques. The models are developed and tested upon datasets from two different countries and compared to the results of a previous similar study. The outcomes of this study indicate that such models can effectively re-develop the Nomograph output and estimate the concrete evaporation rate with high accuracy compared to typical curve-fitting statistical models or models from the literature. Among the proposed methods, the optimization via Genetic Algorithms, individually applied at each estimation process step, provides the best fitting result.
[PyCon US 2025] Scaling the Mountain_ A Framework for Tackling Large-Scale Te...Jimmy Lai
Managing tech debt in large legacy codebases isn’t just a challenge—it’s an ongoing battle that can drain developer productivity and morale. In this talk, I’ll introduce a Python-powered Tech Debt Framework bar-raiser designed to help teams tackle even the most daunting tech debt problems with 100,000+ violations. This open-source framework empowers developers and engineering leaders by: - Tracking Progress: Measure and visualize the state of tech debt and trends over time. - Recognizing Contributions: Celebrate developer efforts and foster accountability with contribution leaderboards and automated shoutouts. - Automating Fixes: Save countless hours with codemods that address repetitive debt patterns, allowing developers to focus on higher-priority work.
Through real-world case studies, I’ll showcase how we: - Reduced 70,000+ pyright-ignore annotations to boost type-checking coverage from 60% to 99.5%. - Converted a monolithic sync codebase to async, addressing blocking IO issues and adopting asyncio effectively.
Attendees will gain actionable strategies for scaling Python automation, fostering team buy-in, and systematically reducing tech debt across massive codebases. Whether you’re dealing with type errors, legacy dependencies, or async transitions, this talk provides a roadmap for creating cleaner, more maintainable code at scale.
Construction Materials (Paints) in Civil EngineeringLavish Kashyap
This file will provide you information about various types of Paints in Civil Engineering field under Construction Materials.
It will be very useful for all Civil Engineering students who wants to search about various Construction Materials used in Civil Engineering field.
Paint is a vital construction material used for protecting surfaces and enhancing the aesthetic appeal of buildings and structures. It consists of several components, including pigments (for color), binders (to hold the pigment together), solvents or thinners (to adjust viscosity), and additives (to improve properties like durability and drying time).
Paint is one of the material used in Civil Engineering field. It is especially used in final stages of construction project.
Paint plays a dual role in construction: it protects building materials and contributes to the overall appearance and ambiance of a space.
The main purpose of the current study was to formulate an empirical expression for predicting the axial compression capacity and axial strain of concrete-filled plastic tubular specimens (CFPT) using the artificial neural network (ANN). A total of seventy-two experimental test data of CFPT and unconfined concrete were used for training, testing, and validating the ANN models. The ANN axial strength and strain predictions were compared with the experimental data and predictions from several existing strength models for fiber-reinforced polymer (FRP)-confined concrete. Five statistical indices were used to determine the performance of all models considered in the present study. The statistical evaluation showed that the ANN model was more effective and precise than the other models in predicting the compressive strength, with 2.8% AA error, and strain at peak stress, with 6.58% AA error, of concrete-filled plastic tube tested under axial compression load. Similar lower values were obtained for the NRMSE index.
DeFAIMint | 🤖Mint to DeFAI. Vibe Trading as NFTKyohei Ito
DeFAI Mint: Vive Trading as NFT.
Welcome to the future of crypto investing — radically simplified.
"DeFAI Mint" is a new frontier in the intersection of DeFi and AI.
At its core lies a simple idea: what if _minting one NFT_ could replace everything else? No tokens to pick.
No dashboards to manage. No wallets to configure.
Just one action — mint — and your belief becomes an AI-powered investing agent.
---
In a market where over 140,000 tokens launch daily, and only experts can keep up with the volatility.
DeFAI Mint offers a new paradigm: "Vibe Trading".
You don’t need technical knowledge.
You don’t need strategy.
You just need conviction.
Each DeFAI NFT carries a belief — political, philosophical, or protocol-based.
When you mint, your NFT becomes a fully autonomous AI agent:
- It owns its own wallet
- It signs and sends transactions
- It trades across chains, aligned with your chosen thesis
This is "belief-driven automation". Built to be safe. Built to be effortless.
- Your trade budget is fixed at mint
- Every NFT wallet is isolated — no exposure beyond your mint
- Login with Twitter — no crypto wallet needed
- No \$SOL required — minting is seamless
- Fully autonomous, fully on-chain execution
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Under the hood, DeFAI Mint runs on "Solana’s native execution layer", not just as an app — but as a system-level innovation:
- "Metaplex Execute" empowers NFTs to act as wallets
- "Solana Agent Kit v2" turns them into full-spectrum actors
- Data and strategies are stored on distributed storage (Walrus)
Other chains can try to replicate this.
Only Solana makes it _natural_.
That’s why DeFAI Mint isn’t portable — it’s Solana-native by design.
---
Our Vision?
To flatten the playing field.
To transform DeFi × AI from privilege to public good.
To onboard 10,000× more users and unlock 10,000× more activity — starting with a single mint.
"DeFAI Mint" is where philosophy meets finance.
Where belief becomes strategy.
Where conviction becomes capital.
Mint once. Let it invest. Live your life.
The TRB AJE35 RIIM Coordination and Collaboration Subcommittee has organized a series of webinars focused on building coordination, collaboration, and cooperation across multiple groups. All webinars have been recorded and copies of the recording, transcripts, and slides are below. These resources are open-access following creative commons licensing agreements. The files may be found, organized by webinar date, below. The committee co-chairs would welcome any suggestions for future webinars. The support of the AASHTO RAC Coordination and Collaboration Task Force, the Council of University Transportation Centers, and AUTRI’s Alabama Transportation Assistance Program is gratefully acknowledged.
This webinar overviews proven methods for collaborating with USDOT University Transportation Centers (UTCs), emphasizing state departments of transportation and other stakeholders. It will cover partnerships at all UTC stages, from the Notice of Funding Opportunity (NOFO) release through proposal development, research and implementation. Successful USDOT UTC research, education, workforce development, and technology transfer best practices will be highlighted. Dr. Larry Rilett, Director of the Auburn University Transportation Research Institute will moderate.
For more information, visit: https://aub.ie/trbwebinars
5. Several alternatives possible:
a) Next-hop routing-Only holds the next hop
instead of full path
b) Network-specific routing- Only specify the
network address instead of every host
c) Host-specific routing-Destination host is
given in routing table.
d) Default routing- One entry for default.
10. Routing Table
1. Static
• Contains information inserted
manually.
• Does not change with time.
2. Dynamic
• Updated periodically depending on
network condition.
• Uses protocols like RIP, OSPF, BGP,
etc.
11. Congestion Control
Congestion is a situation in which too many packets
are present in (a part of) the subnet,
performance degrades.
Factors causing congestion:
• The input traffic rate exceeds the capacity of the
output lines.
• The routers are too slow to perform bookkeeping
tasks (queuing buffers, updating tables, etc.).
• The routers' buffer is too limited.
13. Congestion control is different from flow control:
• Congestion is a global issue, involving the behavior of all
the hosts, all the routers, the store-and-forward processing
within the routers, etc.
• Flow control relates to the point-to-point traffic between a
given sender and a given receiver.
• A situation requiring flow control: a fiber optic network
with a capacity of 1000 gigabits/sec on which a
supercomputer was trying to transfer a file to a personal
computer at 1Gbps.
• A situation requiring congestion control: a store-and-
forward network with 1-Mbps lines and 1000 large
minicomputers, half of which were trying to transfer files at
100 kbps to the other half.
14. General principles of congestion control
Open loop solutions solve the problem by good design, in essence,
to make sure the problem does not occur in the first place.
• Tools include deciding when to accept new traffic, when to discard
packets and which ones, and how to schedule packets at various
points in the network.
• A common fact: they make decisions without regard to the current
state of the network.
• Closed loop solutions are based on the concept of a feedback loop,
which consists of the following three parts:
– Monitor the system to detect when and where congestion occurs.
– Pass this information to places where actions can be taken.
– Adjust system operation to correct the problem.
15. How to correct the congestion problem?
• Increase the resource:
– Using an additional line to temporarily increase the
bandwidth between certain points.
– Splitting traffic over multiple routes.
– Using spare (extra) routers.
• Decrease the load:
– denying service to some users,
– degrading service to some or all users, and
– having users schedule their demands in a more
predictable way.
16. Congestion prevention policies
• The open loop congestion control approach tries
to achieve the goal by using appropriate policies
at various levels.
Traffic shaping
• One of the main causes of congestion is that
traffic is often bursty. Another open loop method
is forcing the packets to be transmitted at a more
predictable rate. This method is widely used in
ATM networks and is called traffic shaping.
17. The leaky bucket algorithm
• Each host is connected to the network by an
interface containing a leaky bucket - a finite
internal queue.
• The outflow is at a constant rate when there is
any packet in the bucket, and zero when the
bucket is empty.
• If a packet arrives at the bucket when it is full,
the packet is discarded.
19. The token bucket algorithm
• The leaky bucket algorithm enforces a rigid output pattern at the
average rate, no matter how bursty the traffic is.
• For many applications, it is better to allow the output to speed up
somewhat when large bursts arrive, so a more flexible algorithm is
needed, preferably on that never loses data.
• The bucket holds tokens, generated by a clock at the rate of one
token every sec.
• For a packet to be transmitted, it must capture and destroy one
token.
• The token bucket algorithm allows saving up to the maximum size
of the bucket, which means that bursts of up to packets can be sent
at the maximum speed (for a certain period of time).
21. Difference between Leaky bucket and
Token bucket algorithms:
• The Token bucket algorithm provides a different kind of traffic shaping than
the Leaky bucket algorithm.
• Such as:
• The leaky bucket algorithm does not allow idle hosts to save up permission to
send large burst later. Where as token bucket algorithm does allow saving, up
to the maximum size of the bucket, n i.e. at a time n packets can send through
the hole.
• Token bucket algorithm throws away tokens when the bucket fills up but
never discards packets. Where as the leaky bucket algorithm discards packets
when the bucket fills up.
• In token bucket algorithm, regulating the host and router to stop sending
packets while bucket’s input still pouring in may result in lost data. To
implement this system, token bucket used one variable that counts token. The
counter is incremented by one every second and decremented by one whenever
a packet is sent. When the counter is zero, no packets may be sent. Where as in
leaky bucket algorithm, there is no scheme to do this.