In this session we’ll take a high-level overview of AWS Lambda, a serverless compute platform that has changed the way that developers around the world build applications. We’ll explore how Lambda works under the hood, the capabilities it has, and how it is used. By the end of this talk you’ll know how to create Lambda based applications and deploy and manage them easily.
Speaker: Chris Munns - Principal Developer Advocate, AWS Serverless Applications, AWS
by Gavin Adams, Sr. IoT Specialist SA AWS
Join us for AWS IoT day at the AWS San Francisco Loft. AWS IoT enables you to easily connect and manage millions of devices securely. You can gather data from, run sophisticated analytics on, and take actions in real-time on your diverse fleet of IoT devices from edge to the cloud. You will build IoT applications with AWS IoT experts. AWS IoT provides edge-based software and cloud-based services so you can easily build IoT applications. Edge-based software, including AWS Greengrass, enables you to securely connect devices, gather data and take intelligent actions locally even when Internet connectivity is down. Cloud-based services, including AWS IoT Core, allow you to quickly onboard large and diverse fleets, maintain fleet health, and keep fleets secure.
In this session we’ll take a high-level overview of AWS Lambda, a serverless compute platform that has changed the way that developers around the world build applications. We’ll explore how Lambda works under the hood, the capabilities it has, and how it is used. By the end of this talk you’ll know how to create Lambda based applications and deploy and manage them easily.
Speaker: Chris Munns - Principal Developer Advocate, AWS Serverless Applications, AWS
by Gavin Adams, Sr. IoT Specialist SA AWS
Join us for AWS IoT day at the AWS San Francisco Loft. AWS IoT enables you to easily connect and manage millions of devices securely. You can gather data from, run sophisticated analytics on, and take actions in real-time on your diverse fleet of IoT devices from edge to the cloud. You will build IoT applications with AWS IoT experts. AWS IoT provides edge-based software and cloud-based services so you can easily build IoT applications. Edge-based software, including AWS Greengrass, enables you to securely connect devices, gather data and take intelligent actions locally even when Internet connectivity is down. Cloud-based services, including AWS IoT Core, allow you to quickly onboard large and diverse fleets, maintain fleet health, and keep fleets secure.
IoT Malware Detection through Threshold Random WalksBiagio Botticelli
Presentation of my Master Thesis Project in Engineering in Computer Science of University of Rome "La Sapienza".
The thesis applies the Threshold Random Walk probabilistic algorithm to make an online detection of IoT Malware Families.
Introduction to Event Driven ArchitectureCitiusTech
In this document, we present the idea of EDA as a favored software architecture pattern, and check out how a leading healthcare system leveraged EDA to successfully meet its dynamic business needs.
AWS Lambda를 활용한 Amazon Alexa Skills 개발 방법 (윤석찬) - Startup DeepDive ConferenceAmazon Web Services Korea
Startup DeepDive Conference - WIS 2017
Amaon Echo를 통한 음성인식 기반 인공 지능 스마트홈 서비스를 제공하는 Alexa서비스와 이를 통한 서드 파티 음성 인식 앱을 개발하는 Alexa Skills Kits에 대해 자세히 소개합니다. 특히, Alexa 서비스를 기반한 글로벌 디바이스 사용자를 위해 AWS Lambda와 같은 서버리스(Serverless) 아키텍처로 음성 서비스를 만들고 배포할 수 있는 방법을 간단한 데모와 함께 알아봅니다. 본 세션을 통해 Alexa 기반 인공 지능 애플리케이션을 디자인하는 방법 및 모범 사례에 대해서도 공유합니다
워크로드에 맞는 데이터베이스 찾기
박주연 솔루션즈 아키텍트, AWS
급진적으로 늘어나는 데이터, 점차 다양해지는 워크로드의 특성에 적합한 데이터 관리를 위해 AWS는 광범위한 데이터베이스 선택 옵션을 제공합니다. 본 세션에서는 관계형 데이터베이스를 비롯, NoSQL, 인메모리, 그래프 등 특정 애플리케이션에 적합한 데이터베이스를 선택할 수 있도록 AWS가 제공하는 데이터베이스 서비스의 개요를 소개합니다. Amazon RDS와 Aurora, DynamoDB, Elasticache 및 Neptune 서비스에 대해 알아봅니다.
Building Speech Enabled Products with Amazon Polly & Amazon LexAmazon Web Services
by Dario Rivera, Solutions Architect, AWS
This session will introduce you to Amazon Polly, a deep learning service that turns text into lifelike speech. Polly enables existing applications to speak as a first class feature and creates the opportunity for entirely new categories of speech-enabled products – from mobile apps and cars, to devices and appliances. Polly includes 47 lifelike voices and support for 24 languages, so you can select the ideal voice and distribute your speech-enabled applications in many geographies. Polly is easy to use – you just send the text you want converted into speech to the Polly API, and Polly immediately returns the audio stream to your application so you can play it directly or store it in a standard audio file format, such as MP3. Polly supports Speech Synthesis Markup Language (SSML) tags like prosody so you can adjust the speech rate, pitch, or volume. Polly is a secure service that delivers all of these benefits at high scale and at low latency. You can cache and replay Polly’s generated speech at no additional cost. Polly lets you convert 5M characters per month for free during the first year. Polly’s pay-as-you-go pricing, low cost per request, and lack of restrictions on storage and reuse of voice output make it a cost-effective way to enable speech synthesis everywhere. Join this session to learn more and find out how you get can started with Amazon Polly, today!
게임사들이 활용할 수 있는 네이버클라우드플랫폼 서비스들과 클라우드 상에서 게임 인프라 구축시 고려해야 할 포인트들에 대해서 소개합니다. | Introduce the Naver Cloud Platform service that game companies can use and the points to consider when building game infrastructure in the cloud.
Top 5 Event Streaming Use Cases for 2021 with Apache KafkaKai Wähner
Apache Kafka and Event Streaming are two of the most relevant buzzwords in tech these days. Ever wonder what the predicted TOP 5 Event Streaming Architectures and Use Cases for 2021 are? Check out the following presentation. Learn about edge deployments, hybrid and multi-cloud architectures, service mesh-based microservices, streaming machine learning, and cybersecurity.
On-demand video recording: https://videos.confluent.io/watch/XAjxV3j8hzwCcEKoZVErUJ
Image Processing involves the immense utilisation of Wavelet Transforms, and to apply on images require the knowledge of its application two dimensions.
본 세션에서는 Amazon의 관리형 데이터베이스 서비스(RDS) 중 기존 상용데이터베이스의 5배 성능 및 1/10 가격으로도 확장성을 보장하는 Aurora에 대한 소개 및 사용법 그리고 기존의 DB에서의 마이그레이션 방법에 대해 소개해드립니다. 10월 리인벤트를 통해 동경 리전에 Aurora를 사용가능하게 되었습니다.
The Rise Of Event Streaming – Why Apache Kafka Changes EverythingKai Wähner
Business digitalization trends like microservices, the Internet of Things or Machine Learning are driving the need to process events at a whole new scale, speed and efficiency. Traditional solutions like ETL/data integration or messaging are not build to serve these needs.
Today, the open source project Apache Kafka® is being used by thousands of companies including over 60% of the Fortune 100 to power and innovate their businesses by focusing their data strategies around event-driven architectures leveraging event streaming.We will discuss the market and technology changes that have given rise to Kafka and to Event Streaming, and we will introduce the audience to the key aspects of building an Event streaming platform with Kafka. Examples of productive use cases from the automotive, manufacturing and transportation sector will showcase the power of event streaming.
IoT Malware Detection through Threshold Random WalksBiagio Botticelli
Presentation of my Master Thesis Project in Engineering in Computer Science of University of Rome "La Sapienza".
The thesis applies the Threshold Random Walk probabilistic algorithm to make an online detection of IoT Malware Families.
Introduction to Event Driven ArchitectureCitiusTech
In this document, we present the idea of EDA as a favored software architecture pattern, and check out how a leading healthcare system leveraged EDA to successfully meet its dynamic business needs.
AWS Lambda를 활용한 Amazon Alexa Skills 개발 방법 (윤석찬) - Startup DeepDive ConferenceAmazon Web Services Korea
Startup DeepDive Conference - WIS 2017
Amaon Echo를 통한 음성인식 기반 인공 지능 스마트홈 서비스를 제공하는 Alexa서비스와 이를 통한 서드 파티 음성 인식 앱을 개발하는 Alexa Skills Kits에 대해 자세히 소개합니다. 특히, Alexa 서비스를 기반한 글로벌 디바이스 사용자를 위해 AWS Lambda와 같은 서버리스(Serverless) 아키텍처로 음성 서비스를 만들고 배포할 수 있는 방법을 간단한 데모와 함께 알아봅니다. 본 세션을 통해 Alexa 기반 인공 지능 애플리케이션을 디자인하는 방법 및 모범 사례에 대해서도 공유합니다
워크로드에 맞는 데이터베이스 찾기
박주연 솔루션즈 아키텍트, AWS
급진적으로 늘어나는 데이터, 점차 다양해지는 워크로드의 특성에 적합한 데이터 관리를 위해 AWS는 광범위한 데이터베이스 선택 옵션을 제공합니다. 본 세션에서는 관계형 데이터베이스를 비롯, NoSQL, 인메모리, 그래프 등 특정 애플리케이션에 적합한 데이터베이스를 선택할 수 있도록 AWS가 제공하는 데이터베이스 서비스의 개요를 소개합니다. Amazon RDS와 Aurora, DynamoDB, Elasticache 및 Neptune 서비스에 대해 알아봅니다.
Building Speech Enabled Products with Amazon Polly & Amazon LexAmazon Web Services
by Dario Rivera, Solutions Architect, AWS
This session will introduce you to Amazon Polly, a deep learning service that turns text into lifelike speech. Polly enables existing applications to speak as a first class feature and creates the opportunity for entirely new categories of speech-enabled products – from mobile apps and cars, to devices and appliances. Polly includes 47 lifelike voices and support for 24 languages, so you can select the ideal voice and distribute your speech-enabled applications in many geographies. Polly is easy to use – you just send the text you want converted into speech to the Polly API, and Polly immediately returns the audio stream to your application so you can play it directly or store it in a standard audio file format, such as MP3. Polly supports Speech Synthesis Markup Language (SSML) tags like prosody so you can adjust the speech rate, pitch, or volume. Polly is a secure service that delivers all of these benefits at high scale and at low latency. You can cache and replay Polly’s generated speech at no additional cost. Polly lets you convert 5M characters per month for free during the first year. Polly’s pay-as-you-go pricing, low cost per request, and lack of restrictions on storage and reuse of voice output make it a cost-effective way to enable speech synthesis everywhere. Join this session to learn more and find out how you get can started with Amazon Polly, today!
게임사들이 활용할 수 있는 네이버클라우드플랫폼 서비스들과 클라우드 상에서 게임 인프라 구축시 고려해야 할 포인트들에 대해서 소개합니다. | Introduce the Naver Cloud Platform service that game companies can use and the points to consider when building game infrastructure in the cloud.
Top 5 Event Streaming Use Cases for 2021 with Apache KafkaKai Wähner
Apache Kafka and Event Streaming are two of the most relevant buzzwords in tech these days. Ever wonder what the predicted TOP 5 Event Streaming Architectures and Use Cases for 2021 are? Check out the following presentation. Learn about edge deployments, hybrid and multi-cloud architectures, service mesh-based microservices, streaming machine learning, and cybersecurity.
On-demand video recording: https://videos.confluent.io/watch/XAjxV3j8hzwCcEKoZVErUJ
Image Processing involves the immense utilisation of Wavelet Transforms, and to apply on images require the knowledge of its application two dimensions.
본 세션에서는 Amazon의 관리형 데이터베이스 서비스(RDS) 중 기존 상용데이터베이스의 5배 성능 및 1/10 가격으로도 확장성을 보장하는 Aurora에 대한 소개 및 사용법 그리고 기존의 DB에서의 마이그레이션 방법에 대해 소개해드립니다. 10월 리인벤트를 통해 동경 리전에 Aurora를 사용가능하게 되었습니다.
The Rise Of Event Streaming – Why Apache Kafka Changes EverythingKai Wähner
Business digitalization trends like microservices, the Internet of Things or Machine Learning are driving the need to process events at a whole new scale, speed and efficiency. Traditional solutions like ETL/data integration or messaging are not build to serve these needs.
Today, the open source project Apache Kafka® is being used by thousands of companies including over 60% of the Fortune 100 to power and innovate their businesses by focusing their data strategies around event-driven architectures leveraging event streaming.We will discuss the market and technology changes that have given rise to Kafka and to Event Streaming, and we will introduce the audience to the key aspects of building an Event streaming platform with Kafka. Examples of productive use cases from the automotive, manufacturing and transportation sector will showcase the power of event streaming.
Asynchronous Transfer ModeATM is originally the transfer mode for implementin...JebaRaj26
ATM is a connection-oriented, high-speed, low-delay switching and transmission technology that uses short and fixed-size packets, called cells, to transport information.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
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- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
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Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
2. 12/20/12 16:27 2
Overview
0 Why Traffic Management?
0 Network Congestion
0 Effects of Network Congestion
0 Traffic Parameters
0 ATM Service Categories
0 Quality of Service (QoS)
0 Quality of Service (QoS) Parameters
0 Traffic Contract
Jain
0 Traffic Control Functions
0 Usage Parameter Control
0 Packet Discarding
CS575 ATM Technology: Traffic Management 2
3. 12/20/12 16:27 3
Why Traffic Management?
0 The ATM technology is intended to support a wide variety of services and
applications such as voice, video, and data
0 ATM promises to support all these different requirements with a common
network
0 Within such a network all connections may impact on each other
0 ATM must manage traffic fairly and provide effective allocation of network
resources for these different applications
0 It is the task of ATM traffic control to:
- protect the network and the end-systems from congestion in order to
provide specified and guaranteed levels of Quality of Service (QoS)
- use available network resources efficiently
CS575 ATM Technology: Traffic Management
3
4. 12/20/12 16:27 4
Network Congestion
0 Network congestion is a state when the network cannot meet the negotiated
network performance objectives for established connections or for new
connection requests
0 Network congestion can be caused by:
- Unpredictable statistical fluctuation of traffic flows
- Fault conditions within the network
0 ATM layer traffic control is a set of actions taken by the network to avoid
network congestion
0 Traffic control takes measures to adapt to unpredictable fluctuations in
traffic flows and other problems within the network.
0 ATM layer congestion control refers to the set of actions taken by the
network to minimize the intensity, spread, and duration of congestion
CS575 ATM Technology: Traffic Management
4
5. 12/20/12 16:27 5
Effects of Network Congestion
Source: Stallings: Data and Computer Communications p316
CS575 ATM Technology: Traffic Management 5
6. 12/20/12 16:27 6
Traffic Parameters
0 Traffic parameters describe traffic characteristics of a connection
0 For a given connection, traffic parameters are grouped into a source traffic
descriptor
0 Traffic parameters specified in ATM Forum UNI
- Peak Cell Rate (PCR)
= An upper bound on the rate that traffic can be submit on a
connection
= Measured in cells/second
- Sustainable Cell Rate (SCR)
= An upper bound on the average cell rate of a burst traffic of an
ATM connection
= Measured in cells/second
= Calculated over the duration of the connection
CS575 ATM Technology: Traffic Management
6
7. 12/20/12 16:27 7
Traffic Parameters (concluded)
- Maximum Burst Size (MBS)
= The maximum number of cells that can be sent at the peak cell rate
- Minimum Cell Rate (MCR)
= The minimum number of cells that the user considers acceptable
CS575 ATM Technology: Traffic Management
7
8. 12/20/12 16:27 8
ATM Service Categories
0 ATM carries a wide range of heterogeneous traffic mix
0 To ensure network resources are fairly allocated for each traffic type, ATM
services are divided into different service categories
0 Each ATM service category represents a class of ATM connections that have
homogeneous characteristics in terms of traffic pattern, QoS requirements,
and possible use of control mechanisms, making it suitable for a given type
of resource allocation
0 The ATM Forum specifies the following five categories of services:
- CBR: Constant Bit Rate
- rt-VBR: Real-Time Variable Bit Rate
- nrt-VBR: Non-Real-Time Variable Bit Rate
- UBR: Unspecified Bit Rate
- ABR: Available bit Rate
CS575 ATM Technology: Traffic Management
8
9. 12/20/12 16:27 9
ATM Service Categories (continued)
0 All service categories apply to both VCCs and VPCs.
0 ATM services are characterized by the traffic parameters:
0 Constant Bit Rate (CBR)
- Requires a fixed amount of bandwidth continuously available during
the connection lifetime
- The amount of bandwidth is characterized by a Peak Cell Rate (PCR)
value
CS575 ATM Technology: Traffic Management 9
10. 12/20/12 16:27 10
ATM Service Categories (continued)
- The source can transmit cells at the PCR at any time and for any
duration
- Intended to support real-time applications
- Requires tightly constrained delay and delay variation
- Application examples: interactive (real-time) voice, video, and circuit
emulation
0 Real-Time Variable Bit Rate (rt-VBR)
- Intended for real-time applications
- Requires tightly constrained delay and delay variation
- Characterized by a PCR, Sustainable Cell Rate (SCR), and Maximum
Burst Size (MBS)
- The source may transmit bursty traffic, e.g., Motion JPEG or MPEG
compressed video
CS575 ATM Technology: Traffic Management
10
11. 12/20/12 16:27 11
ATM Service Architecture (continued)
- Application Example: native ATM voice with compression, interactive
(real-time) compressed video (videoconferencing), and other types of
multimedia communications
CS575 ATM Technology: Traffic Management 11
12. 12/20/12 16:27 12
ATM Service Categories (continued)
0 Non-Real-Time Varible Bit Rate (nrt-VBR)
- Intended for non-real-time applications
- Source transmits bursty traffic
- Characterized by a PCR, SCR, and MBS
- Requires low Cell Loss Ratio (CLR)
- May support statistical multiplexing of connections
- No delay bounds are associated with this service category
- Application Example: Critical response time transaction processing
such as airline reservations, banking transactions, processing
monitoring
CS575 ATM Technology: Traffic Management 12
13. 12/20/12 16:27 13
ATM Service Categories (continued)
0 Unspecified Bit Rate (UBR)
- Intended for non-real-time, bursty applications
- Does not specify traffic related service guarantees
- No commitment is made about cell transfer delay
- No commitment is made as to cell loss ratio experienced by cells on the
connection
- Best effort service
- Application example: E-mail, LAN traffic, and TCP/IP traffic
0 Available Bit Rate (ABR)
- Intended for bursty traffic whose bandwidth range is known roughly
- End system specifies maximum required bandwidth (PCR) and
minimum usable bandwidth (MCR)
CS575 ATM Technology: Traffic Management
13
14. 12/20/12 16:27 14
ATM Service Categories (continued)
- The cell rate provided by the network can change throughout the
connection
- The user gets what’s available
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ATM Service Categories (continued)
- The goal is to provide rapid access to unused network bandwidth at up to PCR
whenever the network bandwidth is available
- Cell loss ratio is minimal provided that the user adapts to the network’s feedback
controls
- Intended for non-real-time applications
- Application example: file transfer, browsing the Web
- No numeric commitment is made about cell transfer delay
- Flow control mechanism specified
- A rate-based service specified by the ATM Forum
- Flow control model
= A source generates forward Resource Management cells (RM-cells)
= RM-cells are turned around by the destination as backward RM-cells
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ATM Service Categories (concluded)
= Backward RM-cells carry feedback information provided by the
network and/or destination to the source
- The source performs dynamic traffic shaping based on feedback
received from the network
unspecified bit-rate
and
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Quality of Service (QoS)
0 QoS is a set of user-perceivable performance parameters that characterize
the traffic over an ATM connection
0 Defined on an end-to-end basis
0 User requests a QoS class for an ATM connection
0 The requested QoS class is a part of the traffic contract
0 The network commits to meet the requested QoS as long as the user complies
with the traffic contract
0 ATM Forum QoS Classes
QoS Class QoS Parameters Application
0 Unspecified Best Effort, At Risk
1 Specified Circuit Emulation, CBR
2 Specified VBR Video/Audio
3 Specified Connection-Oriented Data
4 Specified Conectionless Data
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Quality of Service (QoS) Parameters
0 QoS parameters describe the level of service for each connection
0 ATM Forum specified six QoS parameters
0 Through the use of network signaling to establish an ATM connection, three
of these may be negotiated between the end-system and the network
- Peak-to-peak Cell Delay Variation (peak-to-peak CDV)
- Maximum Cell Transfer Delay (maxCTD)
- Cell Loss Ratio (CLR)
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Quality of Service (QoS) Parameters (continued)
Cell Transfer Delay Probability Density
Model
Source: ATM Forum Traffic Management Specification Version 4.0
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Quality of Service (QoS) Parameters (continued)
0 Peak-to-peak Cell Delay Variation (peak-to-peak CDV)
- Cell delay variation (CDV) is defined as a measure of cell clumping
- It is how much more closely the cells are spaced than the nominal
interval
- Cells may be sent into the network evenly spaced, a variety of factors
may contribute to cell clamping or gaps in the cell stream
- If the network cannot properly control CDV, distortion can occur for
real-time services such as voice, video, and multimedia applications
- If cells arrive too closely together, cell buffers may overflow
- Subscribers of CBR or VBR services need to specify this parameter
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Quality of Service (QoS) Parameters (continued)
0 Maximum Cell Transfer Delay (maxCTD)
- CTD is the elapsed time between a cell’s exit at the source and its entry
at the destination
- It includes both node processing and internode transmission time
- Subscribers of CBR or VBR services need to specify this parameter
0 Cell Loss Ratio (CLR)
- CLR = (Lost Cells) / (Total Transmitted Cells)
- Cells may be lost due to
= network malfunction
= discarded for noncompliance
= discarded in response to network congestion
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Quality of Service (QoS) Parameters (concluded)
0 Higher values of cell loss is dominated by the effects of queuing strategy and
buffer sizes
0 Delay, delay variation, and cell loss are impacted by buffer size and
buffering strategy
0 The error rate is determined by fiber transmission characteristics
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Traffic Contract
0 Agreement between user and network across UNI regarding:
- The QoS that a network is expected to provide
- The Connection Traffic Descriptor, which includes
= Source Traffic Descriptor Traffic Contract
= Cell Delay Variation Tolerance (CDVT)
= Conformance Definition
- Source Traffic Descriptor
= Defines the characteristics of ATM traffic coming into the network
= Includes several negotiable traffic parameters: PCR, SCR, MBS,
and Burst Tolerance (BT)
= Specifies flow for CLP = 0 and/or CLP = 0 + 1
- Cell Delay Variation Tolerance (CDVT)
= The upper bound on the cell clumping measure is CDVT
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Traffic Contract (continued)
= It is the measure of how much cell clumping is acceptable resulting
from network operations such as cell multiplexing or the insertion
of OAM cells
= CDVT controls the amount of variability acceptable using a leaky
bucket algorithm
- Conformance Definition
= Defines what cell rates and streams will be monitored
= Defines the checking rule used to interpret the traffic parameters
= Defines the network’s definition of a compliant connection, i.e.,
what constitutes obeying the rules
= Conformance is determined by the Usage Parameter control (UPC)
at the ingress to the network
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Traffic Contract (concluded)
0 A separate traffic contract for each Virtual Path Connection (VPC) or
Virtual Channel Connection (VCC)
0 Negotiated at connection time
- Signaling message for SVC
- Circuit provision for PVC
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Traffic Control Functions
0 Connection Admission Control (CAC)
0 Usage Parameter Control (UPC)
0 Selective cell discarding
0 Traffic Shaping
0 Explicit Forward Congestion Indication (EFCI)
0 Cell Loss Priority Control
0 Network Resource Management (NRM)
0 Frame discard
0 ABR Flow Control
0 Others
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Connection Admission Control
0 Responsible for determining whether a connection request is admitted or
denied
0 For each connection request, CAC derives the following information from
the traffic contract
- Values of parameters in the source traffic descriptor
- The requested and acceptable values of each QoS parameter and the
requested QoS class
- The value of the CDVT
- The requested conformance definition
- Based on that information and the network’s definition of a compliant
connection to determine
= Whether the connection can be accepted or not
= The traffic parameters needed by UPC
= Allocation of network resource
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Usage Parameter Control
0 What is UPC
- Commonly known as Traffic Policing
- A network traffic control mechanism
- Required at the public UNI
- Detects and stops user traffic violations
- Ensures QoS for other connections
0 UPC Functions
- Monitors cells submitted at the UNI
- Checks for connection compliance
= Is the user sending data too quickly?
= Is the user obeying the traffic contract?
- Checks validity of VPI/VCI values
= Is the user using the correct VPI/VCI?
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Usage Parameter Control (continued)
0 UPC Action
- For non-conforming cells
= Discard or
= Tag as low priority (overwriting CLP bit to 1)
- For conforming cells
= Transparently pass or
= Traffic shape
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Usage Parameter Control (continued)
Discard
cell
No
Yes
Conform to
SCR/BT
0 CLP = 0
Yes Yes
Valid Conform to
In VPI/VCI PCR/CDV CLP Out
CLP = 0+1
1
Conform to
SCR/BT
No No CLP = 1 Yes
No
Discard Discard
cell cell Discard
cell
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Usage Parameter Control (continued)
Generic Cell Rate Algorithm
0 Used to define conformance with respect to the traffic contract
0 For each cell arrival, GCRA determines whether the cell conforms to the
traffic contract of the connection
0 The UPC function may implement GCRA to enforce conformance
0 Equivalent representations of the GCRA
- Continuous-State Leaky Bucket Algorithm
- Virtual Scheduling algorithm
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Usage Parameter Control (concluded)
Equivalent Versions of GCRA
Arrival of a cell k at time ta(k)
TAT: Theoretical Arrival Time
ta(k): Time of arrival of a cell
X’ = X - (t a(k) - LCT)
Yes
TAT < ta(k)
?
Yes
X’ < 0
No TAT = ta (k) ?
No
Yes X’ = 0
Non
Conforming TAT > ta(k) + L
Cell Yes
? Non
Conforming X’ > L
Cell ?
No No
TAT = TAT + I X = X’ + I
Conforming Cell LCT = ta(k)
Conforming Cell
Virtual Scheduling Algorithm
Continuous-State Leaky Bucket Algorithm
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Selective Cell Discard and EFCI
0 Selective Cell discard
- A congested network may selectively discard cells which meet either or
both the following conditions:
= Cells which belong to a non-compliant ATM connection
= Cells which have CLP = 1
- This is to protect the CLP = 0 flow as much as possible
0 Explicit Forward congestion Indication (EFCI)
- A network element in an impending congested state or a congested state
may set an EFCI in the cell header
- This indication may be examined by the destination end-system
- The end-system may adaptively lower the cell rate of the connection
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Traffic Shaping
0 A mechanism that alters the traffic characteristics of a cell stream on a
connection to achieve better network efficiency or to ensure conformance to
the traffic parameters in the traffic contract
0 Traffic shaping examples:
- Peak cell rate reduction
- Burst length limiting
- Spacing cells in time to reduce CDV
- Cell scheduling policy
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Resource Management
0 Resource Management
- Two critical resources
= Buffer space
= Trunk bandwidth
- One way of simplifying the management of the trunk bandwidth is
through the use of virtual paths
= If every node in a network is interconnected by a VPC, then only
the total available entry-to-exit VPC bandwidth need be considered
in CAC decisions
= A VPC is easier to manage as a larger aggregate than multiple,
individual VCCs
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Packet Discarding
0 The ATM Adaptation Layer (AAL) segments higher layer packets into small
fixed-size cells for transporting over the ATM network
0 A cell discarded by a switch causes the loss of the entire packet and
eventually requires end-to-end error recovery through packet
retransmission
0 A small congestion problem could potentially escalate to a more serious one
0 To prevent congestion escalation, Early Packet Discard (EPD) and Partial
Packet Discard (PPD) can be used to discard cells on a packet basis
0 EPD and PPD are applied for ABR and UBR traffic of AAL-5 connections
0 EPD
- When congestion occurs and buffers are filling, EPD discards all cells
associated with a new packet arriving at a queue
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Packet Discarding (continued)
- The remaining buffer space can then be used for cells belonging to
packets that already have entered the queue
- EPD maximizes the chances for already queued packets to leave the
queue successfully
0 PPD
- If EPD does not remove congestion and cells arriving at a queue have to
be discarded because of buffer overflow PPD is applied
- PPD discards all subsequent cells associated with the same packet
rather than just a few cells within the packet during buffer overflow
- PPD minimizes the number of packets becoming invalid in the queue
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Packet Discarding (concluded)
Source: Newbridge White Paper
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References
W. Stalling, Local and Metropolitan Area Networks, 6th edition, Prentice Hall,
2000, Chapter 11
W. Stalling, Data and Computer Communications, 6th edition, Prentice Hall,
2002, Chapters 11-12
A. Wu, Advanced Local Area Networks, Lectures & Slides, Rivier College,
2001.
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