This document outlines German Sviridov's PhD research on enabling replicated network applications in programmable data planes. It discusses limitations of classic SDN approaches and introduces the concept of programmable data planes that allow for user-defined packet parsing and processing. Sviridov's research developed techniques like LODGE and LOADER to implement logically centralized network applications through state replication across multiple switches, avoiding issues like single point of failure and bandwidth constraints of centralized controllers. His work included formulations for optimal state placement and replication algorithms to minimize traffic overhead while supporting distributed applications.
PLNOG 17 - Marcin Aronowski - Technologie dostępowe dla IoT. Jak się w tym ws...PROIDEA
BLE, BT, Wifi, Z-Wave, Zigbee, EnOcean, 802.15.4, NB-IoT, EC-GSM, LoRa, SigFox. Lista jest długa a cały czas się jeszcze wydłuża. Która technologia będzie najlepsza dla Twojego projektu IoT? Jak się nie pogubić w tej mnogości i szybkości zmian?
Sesja postara się odpowiedzieć na te i kilka innych pytań związanych z łącznością bezprzewodową w świecie Internet of Things.
Enhancing Network Visibility Based On Open Converged Network ApplianceOpen Networking Summit
Dr. Dongheon Lee' and Dr. Junho Suh's presentation from the 2017 Open Networking Summit.
As the mobile traffic carried by cellular networks has been growing rapidly and the networks gets bigger and more complex, network operators have been forced to search for solutions to substantially enhance network visibility. This talk introduces SKT integrated Network Analyzer (TiNA) and Converged Appliance Platform (T-CAP) which help us improving the efficiency of network operation, troubleshooting, and analyzing traffic. TiNA is composed of virtual network packet broker, flow analyzer, high speed packet dump system, connection performance analyzer, and 3D-based network management system. T-CAP is an open architecture of a server-switch type hardware. We will review how to implement those TiNA functions based on open source (e.g., DPDK, Spark Streaming) and T-CAP. Finally, we will also discuss about the use-cases of TiNA and T-CAP for the private cloud & telco network infrastructure.
PLNOG 17 - Marcin Aronowski - Technologie dostępowe dla IoT. Jak się w tym ws...PROIDEA
BLE, BT, Wifi, Z-Wave, Zigbee, EnOcean, 802.15.4, NB-IoT, EC-GSM, LoRa, SigFox. Lista jest długa a cały czas się jeszcze wydłuża. Która technologia będzie najlepsza dla Twojego projektu IoT? Jak się nie pogubić w tej mnogości i szybkości zmian?
Sesja postara się odpowiedzieć na te i kilka innych pytań związanych z łącznością bezprzewodową w świecie Internet of Things.
Enhancing Network Visibility Based On Open Converged Network ApplianceOpen Networking Summit
Dr. Dongheon Lee' and Dr. Junho Suh's presentation from the 2017 Open Networking Summit.
As the mobile traffic carried by cellular networks has been growing rapidly and the networks gets bigger and more complex, network operators have been forced to search for solutions to substantially enhance network visibility. This talk introduces SKT integrated Network Analyzer (TiNA) and Converged Appliance Platform (T-CAP) which help us improving the efficiency of network operation, troubleshooting, and analyzing traffic. TiNA is composed of virtual network packet broker, flow analyzer, high speed packet dump system, connection performance analyzer, and 3D-based network management system. T-CAP is an open architecture of a server-switch type hardware. We will review how to implement those TiNA functions based on open source (e.g., DPDK, Spark Streaming) and T-CAP. Finally, we will also discuss about the use-cases of TiNA and T-CAP for the private cloud & telco network infrastructure.
Introduction to SDN: Software Defined NetworkingAnkita Mahajan
SDN is the next big thing in networking. It focuses on separating the intelligence from the hardware. OpenFlow is one of the ways (currently the open standard followed by all Datacenters) to implement SDN.
The Challenges of SDN/OpenFlow in an Operational and Large-scale NetworkOpen Networking Summits
Jun Bi
Professor & Director
Tsinghua University
Outline
• Intra-AS (campus level) IPv6 source address validation using OpenFlow (with extension)
– Good for introducing new IP services to network
• Planning next step if we run SDN as a common infrastructure for new services and architectures
– Some personal viewpoints and thoughts on design challenges
– Forwarding abstraction for Post-IP architectures
– Control abstraction for scalable NOS and programmable/manageable virtualization platform
– Inter-AS policies negotiation abstraction
ONS2015: http://bit.ly/ons2015sd
ONS Inspire! Webinars: http://bit.ly/oiw-sd
Watch the talk (video) on ONS Content Archives: http://bit.ly/ons-archives-sd
Frank Brockners' presentation from the 2017 Open Networking Summit.
While troubleshooting or planning, did you ever wish to get full insight into which paths *all* your packets take in your network, understand whether your SLA is really in place, or were you ever asked to prove that your traffic really follows the path you specified by service chaining or traffic engineering? We approach this problem by adding meta-data to *all* packets. In-band OAM adds forwarding path information and other information/stats to every data packet - as opposed to relying on probe packets, which is the traditional method that tools like ping or traceroute use. This session will introduce In-band OAM, explain the technology and outline the reference implementation in FD.io/VPP and OpenDaylight using a example demos.
This session provides an overview of HPE's Software Defined Networking (SDN) feature set and will review the benefits of following SDN apps for network operations and IT security teams: HPE Network Protector, HP Network Optimizer, and HP Visualizer.
Introduction to SDN: Software Defined NetworkingAnkita Mahajan
SDN is the next big thing in networking. It focuses on separating the intelligence from the hardware. OpenFlow is one of the ways (currently the open standard followed by all Datacenters) to implement SDN.
The Challenges of SDN/OpenFlow in an Operational and Large-scale NetworkOpen Networking Summits
Jun Bi
Professor & Director
Tsinghua University
Outline
• Intra-AS (campus level) IPv6 source address validation using OpenFlow (with extension)
– Good for introducing new IP services to network
• Planning next step if we run SDN as a common infrastructure for new services and architectures
– Some personal viewpoints and thoughts on design challenges
– Forwarding abstraction for Post-IP architectures
– Control abstraction for scalable NOS and programmable/manageable virtualization platform
– Inter-AS policies negotiation abstraction
ONS2015: http://bit.ly/ons2015sd
ONS Inspire! Webinars: http://bit.ly/oiw-sd
Watch the talk (video) on ONS Content Archives: http://bit.ly/ons-archives-sd
Frank Brockners' presentation from the 2017 Open Networking Summit.
While troubleshooting or planning, did you ever wish to get full insight into which paths *all* your packets take in your network, understand whether your SLA is really in place, or were you ever asked to prove that your traffic really follows the path you specified by service chaining or traffic engineering? We approach this problem by adding meta-data to *all* packets. In-band OAM adds forwarding path information and other information/stats to every data packet - as opposed to relying on probe packets, which is the traditional method that tools like ping or traceroute use. This session will introduce In-band OAM, explain the technology and outline the reference implementation in FD.io/VPP and OpenDaylight using a example demos.
This session provides an overview of HPE's Software Defined Networking (SDN) feature set and will review the benefits of following SDN apps for network operations and IT security teams: HPE Network Protector, HP Network Optimizer, and HP Visualizer.
Multicloud as the Next Generation of Cloud Infrastructure Brad Eckert
So, what are data center networks really built for? Short answer "applications".
Whether it is a public cloud provider, private enterprise, FSI or telco cloud - the nature of applications across each data center type impose a different set of demands on the underlying network infrastructure. A next-generation architecture is one that is versatile yet modular enough to address these different application needs, whether these are HPC and Big Data, legacy or real-time content. A common architecture goal is for a unified and consolidated network design that can leverage standardized technology attributes and can integrate a versatile workload environment be it high-performance bare metal servers to a microservices enabled container environment. This tutorial is aimed at an in-depth structured understanding of data center business and technical requirements and how EVPN-VXLAN constructs serve as a swiss-knife approach to achieve the same. Practical case study examples that translate theoretical concepts into building blocks for designing and automating multi-tenant data center deployments. Explore how a unified technology solution can help build a network that grows with increasing east-west traffic, seamlessly connects with the backbone for north-south communication while leveraging familiar protocol concepts to achieve security insertion. We will also go over operator issues with traffic optimization, multicast and BUM traffic handling and other common pitfalls. A final step would be to define requirements for a cohesive solution using a centralized controller that enables a data center network operator to leverage the same degree of agility and visibility for both the physical network and the application infrastructure to truly build a software-defined data center.
In the latest ONF and SDxCentral webianr, two of the researchers from the iSDX project, Nick Feamster, professor of computer science at Princeton University, and Arpit Gupta, leading researcher on iSDX at Princeton University. ONF's Technical Program Manager Rick Bauer will also present protion of the webinar, which will look at:
- why the industry needs iSDX
- how iSDX compares to other initiatives in the SDN ecosystem today
- a short demo of iSDX
- iSDX in practice – a customer viewpoint
http://bit.ly/1TAhUlA
Packet Optical SDN Field Trial for Multi-Layer Network OptimizationADVA
Jim Theodoras’ presentation, broadcast live from 2016 Internet2 Global Summit in Chicago, covered the work that ADVA Optical Networking and Juniper Networks have accomplished towards interoperability of SDN controllers and outlined the advantages of joint SDN management of router and transport resources.
Radisys/Wind River: The Telcom Cloud - Deployment Strategies: SDN/NFV and Vir...Radisys Corporation
Radisys and Wind River present on the evolution to the Telecom Cloud and how cloud technology and network virtualization will provide both big opportunities and challenges for operators. Important details and insights are shared on Network Function Virtualization (NFV), Software Defined Network (SDN) and Virtualization.
Business Models for Dynamically Provisioned Optical NetworksTal Lavian Ph.D.
Low latency, high bandwidth services (>1Gb/s) are emerging requirements for business, medical, education, government and industry
New applications development and business models could be stimulated by affordable and easily accessible high bandwidth in both local and wide area networks
High bandwidth connections are typically full period today but full period 7x24 bandwidth is not always needed.
Technologies are now available that suggest plausible new business model options to offer time slots for high bandwidth services
Dynamic provisioning of lambda and sub-lambda time slots
Periodically scheduled (N time slots per day, per week) or ad hoc
Next Generation Optical Networking: Software-Defined Optical NetworkingADVA
Check out Stephan Rettenberger’s presentation from the Next Generation Optical Networking Conference in Monaco. It's all about Software Defined Optical Networking.
DWDM-RAM: DARPA-Sponsored Research for Data Intensive Service-on-Demand Advan...Tal Lavian Ph.D.
The DWDM-RAM architecture identifies two distinct planes over the dynamic
underlying optical network:
the Data Grid Plane that speaks for the diverse requirements of a data-intensive application by providing generic data-intensive interfaces and services and
2) the Network Grid Plane that marshals the raw bandwidth of the underlying optical
network into network services, within the OGSI framework, and that matches the complex requirements specified by the Data Grid Plane.
At the application middleware layer, the Data Transfer Service (DTS) presents an interface between the system and an application. It receives high-level client requests, policy-and-access filtered, to transfer specific named blocks of data with specific advance scheduling constraints.
The network resource middleware layer consists of three services: the Data Handler Service (DHS), the Network Resource Service (NRS) and the Dynamic Lambda Grid Service (DLGS). Services of this layer initiate and control sharing of resources.
cncf overview and building edge computing using kubernetesKrishna-Kumar
Open Source India Conference 2018 Presentation to the general audience - not a deep technical talk. Narrated like a story for make it interesting......
Learn how you can streamline your migration to Cisco Intelligent WAN (IWAN) with lab-tested deployment best practices from Verizon Managed Services. Profit from the real-world expertise and valuable insights of this leading WAN solutions provider.
Miss the webcast? Register to view replay here: http://cs.co/9008BPw6A
DWDM-RAM: DARPA-Sponsored Research for Data Intensive Service-on-Demand Advan...Tal Lavian Ph.D.
DWDM-RAM
An architecture for data intensive Grids enabled by next generation dynamic optical networks, incorporating
new methods for lightpath provisioning. DWDM-RAM is designed to meet the networking challenges of
extremely large scale Grid applications. Traditional network infrastructure cannot meet these demands,
especially, requirements for intensive data flows.
The international keynote speech of Prof. Augusto Neto at the II MOBISLICE workshop, co-located in the IEEE MFV/SDN 2019: “NECOS Project: Vision Towards Deeper Cloud Network Slicing”.
Edge Device Multi-unicasting for Video StreamingTal Lavian Ph.D.
Multicast data stream from a server to multiple clients at the application level.
Overlay network structure must be constructed at the application layer to connect participating end systems
Mechanisms for adapting the overlay structure are necessary to provide and maintain adequate level of QoS of the application
Yoid – generic structure for overaly networks for content distribution
Overcast – single-source multicast
End System Multicast – small-scale multicast for teleconference
ALMI – an ALM infrastructure for multi-sender multicast that scales to a large number of groups with small number of members
Edge devices form overlay structure
Edge devices can replicate and multi-unicast to multiple clients
Overcome bottleneck problem over access link
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
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.
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-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.
Enhancing Performance with Globus and the Science DMZGlobus
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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:
- How to remove silos in DevSecOps
- 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
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
German Sviridov - PhD defense
1. Traffic Optimization in Data Center and
Software-Defined Programmable Networks
GERMAN SVIRIDOV
Doctoral Program in Electrical, Electronics and Communications
Engineering
(XXXIII cycle)
Supervisors:
Prof. Paolo Giaccone
Prof. Andrea Bianco
2. 2
Stateful Software-Defined
Networking: enabling replicated
network applications in
programmable data planes
Flow scheduling in data center
networks: optimizing flow
performance by minimizing flow
completion time
Cloud gaming and game engines:
Quality of Experience improvement in
cloud gaming
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV
Blockchains for vehicular
applications
2
3. 3
Stateful Software-Defined
Networking: enabling replicated
network applications in
programmable data planes
Flow scheduling in data center
networks: optimizing flow
performance by minimizing flow
completion time
Cloud gaming and game engines:
Quality of Experience improvement in
cloud gaming
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV
Blockchains for vehicular
applications
3
4. Enabling replicated network applications in
programmable data planes
03/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 4
5. Classic Software Defined Networks (SDN)
Control
Plane
Data
plane
Physical separation of control and data plane
A single entity (controller) for the control plane
Data plane operations are now centrally managed
Key enabler for novel applications
03/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 5
6. Issues with classic SDN
Control
Plane
Data
plane
• Physical latency between switches
and the controller
• Software processing latency
Added latency
• Per flow processing only
• Support of legacy protocols only
Inflexible
commercial
hardware
• Local in-switch processing
• Simplified switch-local control logic
for latency-sensitive application
Need to take a
step back
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 6
7. Programmable data planes
• User-defined packet parsing
• Support for virtually any data protocol
• Modification to any packet field at line rate
Programmable
parsers
• Programmable match-action rules
• Access to persistent states 𝒔𝒊
• Packet recirculation/cloning
Programmable
packet
processing
𝑠𝑖
03/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 7
8. Enabling new applications and
accelerating old ones
𝑠𝑖
Financial applications
Machine learning
MQTT brokers
DBMS Acceleration
Content caching
Overlay protocols
Load balancers
Packet scheduling
Network telemetry
Firewall/IDS
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 8
9. Limits of data plane-assisted network
applications
𝑠1
𝑠2
𝑠3
𝑠4
•Network applications are local to a single switch
•Independent behavior in the network
Locality constraints
•DDoS detection
•Distributed application/link-aware load balancers
•Network-wide rate limiters
•...
Precluded applications
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 9
10. Limits of data plane-assisted network
applications
𝑠1
𝑠2
𝑠3
𝑠4
•Network applications are local to a single switch
•Independent behavior in the network
Locality constraints
•DDoS detection
•Distributed application/link-aware load balancers
•Network-wide rate limiters
•...
Precluded applications
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 10
How to implement logically centralized network-
wide network applications?
11. Logically centralized network applications
𝑠1
𝑠2
𝑠3
𝑠4
Unique network application
Centralized
state with
global view
of the
network
Traffic
reroute
Data
overhead
Bandwidth
constraints
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 11
12. 𝑠1
𝑠2
𝑠3
𝑠4
𝑠2
1
𝑠3
1
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV
State replication in programmable data
planes
12
Replicated network application
Centralized
state with
global view
of the
network
No traffic
reroute
No data
overhead
Sync
overhead
13. 𝑠1
𝑠3
𝑠4
𝑠2
1
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV
State replication in programmable data
planes
13
Replicated network application
Centralized
state with
global view
of the
network
No traffic
reroute
No data
overhead
Sync
overhead
14. A recipe for state replication in
programmable data planes
LODGE
Suitable
replication
algorithm
Need to convey
state-updates to
other replicas
Need to define a
suitable
transport
protocol
Need to be able
to generate new
packets
𝑠2
𝑠3
𝑠2
1
𝑠3
1
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 14
15. A recipe for state replication in
programmable data planes
LODGE
Eventual
consistency
Need to convey
state-updates to
other replicas
Need to define a
suitable
transport
protocol
Need to be able
to generate new
packets
𝑠2
𝑠3
𝑠2
1
𝑠3
1
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 15
16. A recipe for state replication in
programmable data planes
LODGE
Eventual
consistency
Controller-
constructed
distribution tree
Need to define a
suitable
transport
protocol
Need to be able
to generate new
packets
𝑠2
𝑠3
𝑠2
1
𝑠3
1
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 16
17. A recipe for state replication in
programmable data planes
LODGE
Eventual
consistency
Controller-
constructed
distribution tree
Tailored
P4/OPP-enabled
transport
protocol
Need to be able
to generate new
packets
𝑠2
𝑠3
𝑠2
1
𝑠3
1
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 17
18. A recipe for state replication in
programmable data planes
LODGE
Eventual
consistency
Controller-
constructed
distribution tree
Tailored
P4/OPP-enabled
transport
protocol
Traffic-triggered
packet cloning
and
recirculation
𝑠2
𝑠3
𝑠2
1
𝑠3
1
D
S
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 18
19. A recipe for state replication in
programmable data planes
LODGE
Eventual
consistency
Controller-
constructed
distribution tree
Tailored
P4/OPP-enabled
transport
protocol
Traffic-triggered
packet cloning
and
recirculation
𝑠2
𝑠3
𝑠2
1
𝑠3
1
D
S
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 19
20. LODGE-Enabled Applications
server cluster 1
server cluster 3
server cluster 2
server cluster 4
ASN1 ASN2
ASN3
ASN4
R2
R1
R3
R4
SW1
SW2
SW3
SW4
Realistic scenario
Emulated
testbed
LODGE
implementation
•WAN Autonomous System
(AS) with 4 neighbours
•Incoming traffic monitoring
and reaction
•V1 Model with BMV
•Emulated network
•P4 14/16
•Open Packet Processor (OPP)
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 20
21. LODGE-Enabled Applications
• Distributed DDoS from ASN#
• LODGE logic embedded at
SW1 and SW3
• Simultaneous detection of the
DDoS at all switches without
traffic rerouting
• Distributed rate limiting
• LODGE application embedded
at border routers
• Simultaneous drop of the rate
for both flows
02/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 21
server cluster 1
server cluster 3
server cluster 2
server cluster 4
ASN1 ASN2
ASN3
ASN4
R2
R1
R3
R4
SW1
SW2
SW3
SW4
22. LODGE-Enabled Applications
• Distributed DDoS from ASN#
• LODGE logic embedded at
SW1 and SW3
• Simultaneous detection of the
DDoS at all switches without
traffic rerouting
02/03/2021 PHD DEFENSE – GERMAN SVIRIDOV
Why SW1 & SW3 or R# in
particular?
22
server cluster 1
server cluster 3
server cluster 2
server cluster 4
ASN1 ASN2
ASN3
ASN4
R2
R1
R3
R4
SW1
SW2
SW3
SW4
• Distributed rate limiting
• LODGE application embedded
at border routers
• Simultaneous drop of the rate
for both flows
23. Where to place the state replicas?
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 23
24. State replicas placement: data vs
synchronization traffic
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 24
Data traffic
Sync traffic
𝑠1
1
and 𝑠1
2
: replicas of the same state 𝑠1
X2
X1
s s
s s
- More data traffic overhead
- Less sync traffic overhead
- Less data traffic overhead
- More sync traffic overhead
Number of state replicas
𝑠1
1
𝑠1
2
𝑠1
25. State replicas placement: data vs
synchronization traffic
02/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 25
Number of state replicas • Data vs synchronization traffic trade-off?
• How many replicas to use?
• Where to place the replicas?
Data traffic
Sync traffic
𝑠1
1
and 𝑠1
2
: replicas of the same state 𝑠1
X2
X1
s s
s s
𝑠1
1
𝑠1
2
𝑠1
- More data traffic overhead
- Less sync traffic overhead
- Less data traffic overhead
- More sync traffic overhead
26. Optimal state replication in stateful data
planes
02/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 26
MILP formulation for optimal state replication
Input
• Network topology
• Traffic matrix
• Network applications
• Per-flow application
requirements
• Amount of sync traffic
𝜆𝑠
Constrained by
• Bandwidth availability
• Switch resources
availability
• Traffic matrix
Objective
• Minimize maximum
congestion
• Minimize total traffic
Output
• Number of replicas
• Replicas placement
• Data traffic routing
• Sync traffic routing
27. Optimal state replication in stateful data
planes
Difficult to apply to big topologies!
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 27
MILP formulation for optimal state replication
Input
• Network topology
• Traffic matrix
• Network applications
• Per-flow application
requirements
• Amount of sync traffic
𝜆𝑠
Constrained by
• Bandwidth availability
• Switch resources
availability
• Traffic matrix
Objective
• Minimize maximum
congestion
• Minimize total traffic
Output
• Number of replicas
• Replicas placement
• Data traffic routing
• Sync traffic routing
28. Optimal state replication –
approximated solution
28
Algorithm for replica placement
• Based on betweenness centrality
• Solution perturbation
Optimal number of replicas
• Analytical expression
• Asymptotic optimal for Manhattan topology
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 28
29. How to put everything together?
Network application
abstracion
Application
decompositi
on
Consistency
constraints
LODGE dataplane
implementation
P4 OPP
Optimal state
placement
Number of
states
State
position
Traffic routing
Data traffic
Synchroniza-
tion traffic
How to define distributed
network applications?
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 29
30. LOADER: An abstraction for replicated network
applications
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 30
31. 31
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 31
The goals of LOADER
Define an abstraction model for replicated
network applications
Enable efficient translation to basic network
primitives
Combine efforts of optimal state replication
and LODGE into a unified framework
32. 32
Programming
abstraction for
network applications:
•Exposure of
replicated states to
the programmer
•Management of state
inconsistency
•Definition of 𝜆𝑠
Compiler
•Automatic policy
decomposition and
translation to target-
specific language
Embedder
•Automatic state
embedding
•Based on heuristic for
optimal state
replication
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 32
33. 33
Programming
abstraction for
network applications:
•Exposure of
replicated states to
the programmer
•Management of state
inconsistency
•Definition of 𝜆𝑠
Compiler
•Automatic policy
decomposition and
translation to target-
specific language
Embedder
•Automatic state
embedding
•Based on heuristic for
optimal state
replication
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 33
34. 34
Programming
abstraction for
network applications:
•Exposure of
replicated states to
the programmer
•Management of state
inconsistency
•Definition of 𝜆𝑠
Compiler
•Automatic policy
decomposition and
translation to target-
specific language
Embedder
•Automatic state
placement
•Based on heuristic for
optimal state
replication
𝑠1
𝑠2
𝑠3
𝑠4
𝑠2
1
𝑠3
1
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 34
35. 35
Programming
abstraction for
network applications:
•Exposure of
replicated states to
the programmer
•Management of state
inconsistency
•Definition of 𝜆𝑠
Compiler
•Automatic policy
decomposition and
translation to target-
specific language
Embedder
•Automatic state
placement
•Based on heuristic for
optimal state
replication
𝑠1
𝑠2
𝑠3
𝑠4
𝑠2
1
𝑠3
1
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 35
36. LOADER
Network application
abstracion
Application
decompositi
on
Consistency
constraints
LODGE dataplane
implementation
P4 OPP
Optimal state
placement
Number of
states
State
position
Traffic routing
Data traffic
Synchroniza-
tion traffic
[1] Sviridov, German; Bonola, Marco; Tulumello, Angelo; Giaccone, Paolo; Bianco, Andrea; Bianchi, Giuseppe, LODGE: LOcal Decisions on
Global statEs in programmable data planes, in: IEEE NetSoft, 2018
[2] Muqaddas, Abubakar Sidique; Sviridov, German; Giaccone, Paolo; Bianco, Andrea, Optimal state replication in stateful dataplanes, in:
IEEE JSAC, 2020
[3] Sviridov, German; Bonola, Marco; Tulumello, Angelo; Giaccone, Paolo; Bianco, Andrea; Bianchi, Giuseppe, LOcAl DEcisions on Replicated
States (LOADER) in programmable data planes: programming abstraction and experimental evaluation, in: Computer Networks 2021
[1] [2]
[3]
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 36
37. 37
Stateful Software-Defined
Networking: enabling replicated
network applications in
programmable data planes
Flow scheduling in data center
networks: optimizing flow
performance by minimizing flow
completion time
Cloud gaming and game engines:
Quality of Experience improvement in
cloud gaming
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV
Blockchains for vehicular
applications
37
38. Automating game QoE assessment in cloud gaming
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 38
39. The next generation of gaming:
Cloud Gaming
• Games run on a remote server
• Video stream of the game is sent to the users
Remote game
rendering
• Users buy the game and pay the subscription fee
• Play directly on a TV or phone
No need for
hardware
03/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 39
40. The next generation of gaming:
Cloud Gaming
• Games run on a remote server
• Video stream of the game is sent to the users
Remote game
rendering
• Users buy the game and pay the subscription fee
• Play directly on a TV or phone
No need for
hardware
03/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 40
Video streaming
(High BW, High Delay)
Online gaming
(Low BW, Low Delay)
Input lag
(High BW, Low Delay)
41. Not all games are made equal
Different impact
of latency
Different genres
Multiple stages
•Action/FPS
•Strategy, card,
interactive fiction
•Action stages
•Exploration/narration
stages
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 41
42. Not all games are made equal
Different impact
of latency
Different genres
Multiple stages
•Accuracy decrease in
FPS
•Smaller crop yield in
farming simulator?
•Action/FPS
•Strategy, card,
interactive fiction
•Action stages
•Exploration/narration
stages
Different network requirements
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 42
43. Not all games are made equal
Different impact
of latency
Different genres
Multiple stages
•Accuracy decrease in
FPS
•Smaller crop yield in
farming simulator?
•Action/FPS
•Strategy, card,
interactive fiction
•Action stages
•Exploration/narration
stages
Different network requirements
Need for fine-grained game QoE
assessment
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 43
44. Game Quality of Experience (QoE) assessment in
practice
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 44
45. Game
- 25 new game releases
per day on Steam
- Impossible to keep up
with new releases
WAN DC
379 565
1771
2964
4207
7049
9050
0
2000
4000
6000
8000
10000
2012 2013 2014 2015 2016 2017 2018
Number of games released on steam
Human subjects
- Difficult and
expensive to find
enough subjects
- No diversity
guarantee
Controlled environment
- Need to bring subjects to
the lab
- Difficult to achieve in the
wild
Outcome
- Noisy experimental data
- Coarse grained
information
- No repeatability
- Outdated information
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 45
46. Game
- 25 new game releases
per day on Steam
- Impossible to keep up
with new releases
WAN DC
379 565
1771
2964
4207
7049
9050
0
2000
4000
6000
8000
10000
2012 2013 2014 2015 2016 2017 2018
Number of games released on steam
Guilty
Human subjects
- Difficult and
expensive to find
enough subjects
- No diversity
guarantee
Controlled environment
- Need to bring subjects to
the lab
- Difficult to achieve in the
wild
Outcome
- Noisy experimental data
- Coarse grained
information
- No repeatability
- Outdated information
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 46
47. WAN DC
379 565
1771
2964
4207
7049
9050
0
2000
4000
6000
8000
10000
2012 2013 2014 2015 2016 2017 2018
Number of games released on steam
Game
- 25 new game releases
per day on Steam
- Impossible to keep up
with new releases
Bots
- Diversity and
experience can be
programmed
- Still expensive
Controlled environment
- Can be easily simulated
- Fine tuning is easily
achieved
Outcome
+ Deterministic results
+ Fine grained information
+ Repeatability
- Outdated information
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 47
48. WAN DC
379 565
1771
2964
4207
7049
9050
0
2000
4000
6000
8000
10000
2012 2013 2014 2015 2016 2017 2018
Number of games released on steam
Game
- 25 new game releases
per day on Steam
- Impossible to keep up
with new releases?
Bots
- Diversity and
experience can be
programmed
- Still expensive?
Controlled environment
- Can be easily simulated
- Fine tuning is easily
achieved
Outcome
- Deterministic results
- Fine grained information
- Repeatability
- Outdated information
AI?
02/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 48
49. Removing humans from the loop:
Putting the AI to work for QoE assessment
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 49
50. Deep reinforcement learning (DRL) for
video games
Learning to play from raw video pixels
Trial and error approach
No supervision Good adaptability
Active exploration
Reward shaping
Tunable behavior
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 50
51. Learning to play Atari
• Deep Q Networks
• Simple, yet powerful model for DRL
One
algorithm
• Seaquest, Beam Rider and Breakout
• Taken from the Atari game catalogue
Three
games
• (Super) human level achieved in less
than 5 hours of training
• Ready to be deployed in the testbed
Three AI
agents
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 51
52. Measuring the gaming performance
• Per-frame lag 𝑙
• Per-frame lag probability
𝑝𝑙𝑎𝑔
• Per-keystroke drop
probability 𝑝𝑑𝑟𝑜𝑝
Emulated
network
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 52
53. Measuring the gaming performance
• Per-frame lag 𝑙
• Per-frame lag probability
𝑝𝑙𝑎𝑔
• Per-keystroke drop
probability 𝑝𝑑𝑟𝑜𝑝
Emulated
network
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 53
54. Maximizing average game score
Different network requirements
Need for fine-grained game QoE
assessment
Without traffic
prioritization
With traffic
prioritization
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 54
55. Moving to more complex games
Moving to more complex games
55
Classic Doom
Similar algorithm for training
Different
maps
Different
game modes
Network
parameter
perturbation
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 55
56. Automating game QoE assessment
Cloud gaming
scenario
Emulated network
with perturbation
Artificial players trained with DRL
Atari Doom
[4] Sviridov, German; Beliar, Cedric; Bianco, Andrea; Giaccone, Paolo; Rossi, Dario, Removing human players from the loop: AI-assisted
assessment of Gaming QoE, in: IEEE Infocom NI Workshop, 2020
[5] Sviridov, German; Beliar, Cedric; Simon, Gwendal; Bianco, Andrea; Giaccone, Paolo; Rossi, Dario, Leveraging AI players for QoE estimation
in cloud gaming, in IEEE Infocom Demo Session, 2020
[5]
[4-5]
Per-game QoS mechanism which
maximizes average game QoE
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 56
57. Past works
57
02/03/2021 PHD DEFENSE – GERMAN SVIRIDOV
•Is centralized flow scheduling feasible in practice?
•Can we reach close to state of the art performance
while employing commodity switches for flow
scheduling?
Flow scheduling
in data center
networks
•Blockchain as an enabler for mobility verification
•How to manage trust for sensitive data?
•Which scale private blockchains can reach?
Blockchains for
vehicular
applications
[6-7]
[8]
[6] Sviridov, German; Giaccone, Paolo; Bianco, Andrea, Low-Complexity Flow Scheduling for Commodity Switches in Data Center Networks, in: IEEE Globecom,
2019
[7] Sviridov, German; Giaccone, Paolo; Bianco, Andrea, To Sync or Not to Sync: Why Asynchronous Traffic Control Is Good Enough for Your Data Center, in: IEEE
Globecom, 2018
[8] Chiasserini, Carla Fabiana; Giaccone, Paolo; Malnati, Giovanni; Macagno, Michele; Sviridov, German, Blockchain-based mobility verification of connected cars,
in IEEE CCNC, 2020
57
58. Current work – (Cloud) Gaming
58
•Games are made of different stages
•Each stage requires different QoS
•Can we achieve fine-grained QoS control?
Game stage
classification
•Training time of a given game is typically
unknown
•Can we understand the training difficulty before
even training?
Estimating
training
complexity
•Game engines are monolithic
•Opposite of the cloud-based applications
•Can we make cloud gaming truly cloud-based?
Distributed
gaming
engines
Graphics
Front-end
A.I. Physics
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 58
59. References
59
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV
[1] Sviridov, German; Bonola, Marco; Tulumello, Angelo; Giaccone, Paolo; Bianco, Andrea; Bianchi, Giuseppe, LODGE: LOcal Decisions on Global statEs in
programmable data planes, in: IEEE NetSoft, 2018
[2] Muqaddas, Abubakar Sidique; Sviridov, German; Giaccone, Paolo; Bianco, Andrea, Optimal state replication in stateful dataplanes, in: IEEE JSAC, 2020
[3] Sviridov, German; Bonola, Marco; Tulumello, Angelo; Giaccone, Paolo; Bianco, Andrea; Bianchi, Giuseppe, LOcAl DEcisions on Replicated States
(LOADER) in programmable data planes: programming abstraction and experimental evaluation, in: Computer Networks 2021
[4] Sviridov, German; Beliar, Cedric; Bianco, Andrea; Giaccone, Paolo; Rossi, Dario, Removing human players from the loop: AI-assisted assessment of
Gaming QoE, in: IEEE Infocom NI Workshop, 2020
[5] Sviridov, German; Beliar, Cedric; Simon, Gwendal; Bianco, Andrea; Giaccone, Paolo; Rossi, Dario, Leveraging AI players for QoE estimation in cloud
gaming, in IEEE Infocom Demo Session, 2020
[6] Sviridov, German; Giaccone, Paolo; Bianco, Andrea, Low-Complexity Flow Scheduling for Commodity Switches in Data Center Networks, in: IEEE
Globecom, 2019
[7] Sviridov, German; Giaccone, Paolo; Bianco, Andrea, To Sync or Not to Sync: Why Asynchronous Traffic Control Is Good Enough for Your Data Center, in:
IEEE Globecom, 2018
[8] Chiasserini, Carla Fabiana; Giaccone, Paolo; Malnati, Giovanni; Macagno, Michele; Sviridov, German, Blockchain-based mobility verification of
connected cars, in IEEE CCNC, 2020
59
61. Centralized explicit rate assignment
Taking inspiration from Faspass:
◦ Centralized per packet DC-wide scheduling
Per-flow rate control:
◦ Given the actual number of flows...
◦ ...allocate a rate to transmit data for each pair of
source/destination servers
◦ Rate re-computation occurs whenever a flow starts/ends
Two step algorithms
1. Rate assignment
2. Routing
Grant:
SRCi -> DSTj
Through Sk
With rate Rij
Centralized Controller
At each
new/ended
flow
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 62
62. Example of ASY algorithms
Rate assignment algorithm
◦ rij = number of flows from server i to server j
◦ Normalize the matrix between pairs of servers in order
to be double-stochastic
◦ Avoid to overload servers
◦ Possible parallel implementation
Routing
◦ Exploit the available parallel paths to reduce output
contentions
◦ Randomly assign a different path for each flow (e.g.,
ECMP)
r11 r12 r13 . . . r1N
r21 . . . .
r31 . . . .
. . . . .
. . . .
rN1 rN2 . . . rNN
Normalize
Normalize
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 63
63. SYN vs ASY - schedulers
Scenario
◦ Realistic protocol stack
◦ incast + fixed packet size
SYN schedulers
◦ MMF: Max-Min Fair
◦ OCF: Oldest Cell First
◦ SRJF: Shortest Remaining Job First
Average Flow Completion Time (AFCT)
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 64
64. Minimizing FCT in DCN
Optimal flow scheduling in DCN requires knowledge about
individual flow length.
◦ Difficult (impossible) to know in advance
◦ Fully information-agnostic schedulers penalize long flows
◦ Knowledge about flow length distribution may help in improving the
scheduling
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 65
65. Minimizing FCT
with average flow
length distribution
Flow prioritization flows without knowledge about
their length.
◦ Use of average flow length distribution
◦ Fine grained scheduling at host
◦ Coarse scheduling at switches
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 66
66. Minimizing FCT with average flow length
distribution
Mice Medium Elephant
Average
Realistic packet-level network simulator
◦ Real implementation of TCP and DCTCP
◦ Realistic flow length distributions
◦ Mixed and uniform flow length scenarios
◦ Robustness analysis to CDF underestimation
01/03/2021 PHD DEFENSE – GERMAN SVIRIDOV 67