Building a Video Streaming Testing Framework with Seastar

•

0 likes•795 views

At Broadpeak, we initiated an effort to use an in-house benchmarking tool built upon Seastar. Our benchmarking tool emulates thousands of HLS or DASH video clients benefiting from the asynchronous programming model of Seastar with capabilities similar to JMeter but with complete support for HTTP/2, as well as high performance thanks to an extensive use of the native stack based on TCP. We tested up to 170 Gbps HTTP/1.1 traffic and 100+ Gbps HTTP/2 on a single dual-socket server. This shows that Seastar is well suited for applications beyond servers. During the presentation, we will highlight some limitations of Seastar we identified for such kind of client applications and the fixes we made.

Technology

Building a video streaming
testing framework with Seastar
Nicolas Le Scouarnec

Nicolas Le Scouarnec
Senior R&D Engineer, Broadpeak
Nicolas Le Scouarnec is an R&D engineer at Broadpeak, a provider of video streaming
solutions for ISPs and content providers. He currently focuses on performance evaluation of
video delivery systems. Previously, he worked on optimizing networked systems (Krononat,
Cuckoo++), or machine learning applications (Faiss-QuickerADC).
2

Benchmark/Load test Broadpeak’s Video CDN solutions
Our Goal
...
Android, Browsers, …
(Video players)
HTTP/1.1, HTTPS/1.1, HTTP2, …
Caching server
(High-performance)

Our Goal
Benchmark Efficiently
Reuse COTS servers
■ failover server
■ multi-purpose test server
Scale
■ 10s to 100+ Gbps Networking
■ 10 000s concurrent clients
Benchmark

Current Solutions
Apache JMeter
(10-20 Gbps)
Commercial Hardware
Appliances (20-100+ Gbps)
DASH / HLS over HTTP
Client Emulation
• Open, can be extended
• One simulated client <-> One thread
• Can’t pile 10 servers to test 1
• High performance
• Cannot be “moved/replicated”
• Precludes on-customer premises load tests
• Delay streaming protocol evolutions

What we did
Pick Seastar as a basis
■ IO Intensiveness (good fit for DPDK stack)
■ High-concurrency / asynchronous
Pick Chromium (widely used browser)
■ Full HTTP protocol support (HTTPS/1.1, HTTP2, HTTP/3), compression,
pooling…
■ Highly-realistic client emulation
HLS/DASH Player Emulation
■ Leverage Seastar / One Thread per Core
■ Coroutines, Ragel
■ Support latest evolutions (low latency, DASH templates)

Why Seastar than DPDK-based X?
Full TCP stack
■ With minor fixes runs well in our benchmarks
■ “Socket”-like abstraction
Asynchronous model good fit for “video” application
■ Scheduling of video parts download
■ Scheduling of periodic manifest updates
■ Simulating buffer draining, timeouts, …
■ Constant-rate scheduling for avoiding coordinated omission

Improving Seastar for client
applications
Network IO/Intensive (Benchmarking)
■ Seastar periodically yields the CPU to the kernel
■ Get rid of context switches for pure-networking benchmark app
Seastar Native Network Stack limits
■ Single NIC
■ Designed first for server applications ( Client -> Seastar Server)
● Network Connection to Core mapping done through RSS
● Good fit for Seastar-received connections
■ New “multi-NIC” packet steering for Seastar-initiated connections & beyond

Exposing each core on the network
Physical NIC
RxQ
RxQ
RxQ
@IP
@MAC
@IP
@MAC
@IP
@MAC
RxQ
@IP
@MAC
RxQ@MAC
RxQ
@IP
@MAC
vNIC
vNIC
vNIC
vNIC
CPU0
CPU1
CPU5
CPU2
CPU3
CPU4
vNIC
vNIC
@IP
Physical NIC
CPU0
CPU1
CPU7
RxQ0
RxQ1
RxQ7
CPU2RxQ2
CPU3RxQ3
CPU6RxQ6
@IP
RSSHashing
@MAC
...
Seastar’s vanilla native networking stack Broadpeak S2C networking stack
...

Physical NIC
Supporting multiple NICs
RxQ
RxQ
RxQ
RxQ
RxQ
RxQ
vNIC
vNIC
vNIC
vNIC
CPU0
CPU1
CPU7
CPU2
CPU3
CPU6
vNIC
vNIC
...
@IP
@MAC
@IP
@MAC
@IP
@MAC
@IP
@MAC
@MAC
@IP
@MAC
@IP

Going back to server applications
TOR
Switch Physical NIC
CPU0
CPU1
CPU7
RxQ
RxQ
RxQ
CPU2RxQ
CPU3RxQ
CPU6RxQ
@IP
ECMPHashing
With a single IP (server applications)
@MAC
@MAC
@MAC
@MAC
@MAC
@MAC
vNIC
vNIC
vNIC
vNIC
vNIC
vNIC
...

Conclusion
Seastar for an HTTP benchmarking tool
▪ Jmeter-like capabilities (high flexibility)
▪ But with high-performance levels
▪ Currently in use at Broadpeak for testing and benchmarking
Minor changes to core Seastar
▪ How to use multiple NICs and handle “reverse” traffic ?
▪ Implementation of some of Krononat principles (USENIX ATC 2018)

Thank You
@broadpeak
broadpeak / Nicolas Le Scouarnec
Nicolas Le Scouarnec
Nicolas.LeScouarnec@broadpeak.tv

We have been offering many internet services and smart phone applications for over 20 years in Japan, and Cassandra has been used by our services since 2010. In this presentation, I will explain some issues and solutions about Cassandra, and our next generation infrastructure for Cassandra. About the Speaker Satoshi Konno Technical Manager, Yahoo Japan Corporation Satoshi Konno is a software engineer with 20 years of experience. He has worked in Yahoo Japan as a programmer for 10 years and in their NoSQL team for the past 4 years and he is currently in a computer science doctoral course studying distributed computing.

Scylla’s Journey Towards Being an Elastic Cloud Native Database

ScyllaDB

Scaling Redis Cluster Deployments for Genome Analysis (featuring LSU) - Terry...

Redis Labs

Connecting kafka message systems with scylla

Maheedhar Gunturu

Maheedhar Gunturu presented on connecting Kafka message systems with Scylla. He discussed the benefits of message queues like Kafka including centralized infrastructure, buffering capabilities, and streaming data transformations. He then explained Kafka Connect which provides a standardized framework for building connectors with distributed and scalable connectors. Scylla and Cassandra connectors are available today with a Scylla shard aware connector being developed.

Kafka to the Maxka - (Kafka Performance Tuning)

DataWorks Summit

Kafka is becoming an ever more popular choice for users to help enable fast data and Streaming. Kafka provides a wide landscape of configuration to allow you to tweak its performance profile. Understanding the internals of Kafka is critical for picking your ideal configuration. Depending on your use case and data needs, different settings will perform very differently. Lets walk through performance essentials of Kafka. Let's talk about how your Consumer configuration, can speed up or slow down the flow of messages to Brokers. Lets talk about message keys, their implications and their impact on partition performance. Lets talk about how to figure out how many partitions and how many Brokers you should have. Let's discuss consumers and what effects their performance. How do you combine all of these choices and develop the best strategy moving forward? How do you test performance of Kafka? I will attempt a live demo with the help of Zeppelin to show in real time how to tune for performance.

Sizing Your Scylla Cluster

ScyllaDB

This document discusses ScyllaDB's process for sizing a Scylla cluster. It begins by outlining the importance of understanding business, application, and infrastructure requirements. Then it walks through building a sample system based on provided workload details. It shows how the sample system could be configured on different cloud platforms like AWS, Azure, and GCP. Finally, it highlights Scylla's sizing sheet tool for helping to determine hardware needs based on workload characteristics and performance goals.

War Stories: DIY Kafka

confluent

(Nina Hanzlikova, Zalando) Kafka Summit SF 2018 My team at Zalando fell in love with KStreams and their programming model straight out of the gate. However, as a small team of developers, building out and supporting our infrastructure while still trying to deliver solutions for our business has not always resulted in a smooth journey. Can a small team of a couple of developers run their own Kafka infrastructure confidently and still spend most of their time developing code? In this talk, we will dive into some of the problems we experienced while running Kafka brokers and Kafka Streams applications, as well as the consultations we had with other teams around this matter. We will outline some of the pragmatic decisions we made regarding backups, monitoring and operations to minimize our time spent administering our Kafka brokers and various stream applications.

Cassandra Summit 2014: Active-Active Cassandra Behind the Scenes

DataStax Academy

Presenter: Roopa Tangirala, Senior Cloud Data Architect at Netflix High availability is an important requirement for any online business and trying to architect around failures and expecting infrastructure to fail, and even then be highly available, is the key to success. One such effort here at Netflix was the Active-Active implementation where we provided region resiliency. This presentation will discuss the brief overview of the active-active implementation and how it leveraged Cassandra’s architecture in the backend to achieve its goal. It will cover our journey through A-A from Cassandra’s perspective, the data validation we did to prove the backend would work without impacting customer experience. The various problems we faced, like long repair times and gc_grace settings, plus lessons learned and what would we do differently next time around, will also be discussed.

The document introduces the Scylla Operator for Kubernetes, which provides a management layer for Scylla on Kubernetes. It addresses some limitations of using StatefulSets alone to run Scylla, such as safe scale down operations and tracking member identity. The operator implements the controller pattern with custom resources to deploy and manage Scylla clusters on Kubernetes. It handles tasks like cluster creation and scale up/down while addressing issues like local storage failures.

Reactive Streams, linking Reactive Application to Spark Streaming by Luc Bour...

Spark Summit

This document summarizes a presentation about linking reactive applications to Spark Streaming using Reactive Streams. It discusses back pressure in Spark Streaming, how Spark 1.5 introduced dynamic rate limiting to support back pressure, and how the rate is estimated using a PID controller. It also describes reactive applications as being responsive, resilient, elastic, and message-driven. Reactive Streams is presented as a specification that allows connecting systems using a back pressure interface in the JVM. Finally, it demonstrates how end-to-end back pressure can be achieved between a reactive application, Spark Streaming, and a Reactive Streams receiver.

Redpanda and ClickHouse

Altinity Ltd

Getting Started with Apache Cassandra and Apache Zeppelin (DuyHai DOAN, DataS...

DataStax

If you are interested in Big Data, you surely already know Apache Spark or Apache Cassandra, but do you know Apache Zeppelin ? Do you know that it is possible to draw out beautiful graph using an user-friendly interface out of your Spark RDD and Cassandra queries ? In this session, I will introduce Zeppelin by live coding example and highlight its modular architecture which allows you to plug-in any interpreter for the back-end of your choice. Then we'll dig into the Apache Cassandra interpreter and show how to use it as a default front-end to display your Cassandra data About the Speaker DuyHai DOAN Apache Cassandra Evangelist, DataStax DuyHai DOAN is an Apache Cassandra Evangelist at DataStax. He spends his time between technical presentations/meetups on Cassandra, coding on open source projects like Achilles or Apache Zeppelin to support the community and helping all companies using Cassandra to make their project successful. Previously he was working as a freelance Java/Cassandra consultant.

Redis Networking Nerd Down: For Lovers of Packets and Jumbo Frames- John Bull...

Redis Labs

Packets per second (PPS) is an often overlooked value within an environment. Most network concerns are around throughput and interface speed, but what happens when this value becomes the bottleneck due to large-scale hosting providers (AWS, Azure, etc.) with rigid standards? This talk covers what a Redis packet looks like and how the out-of-the-box configuration can drastically affect packet per second overhead. From here we’ll deep dive into specific configuration values which help lower PPS numbers, as well as different Redis master/slave relationships that can be utilized to keep PPS below inflexible network thresholds.

Aerospike: Maximizing Performance

Aerospike, Inc.

The document discusses improving performance in Aerospike systems. It analyzes performance at the client level, network level, and Aerospike node level. Some key factors that can impact performance are CPU usage, number of network connections, bandwidth, transactions per second, and storage I/O. The document provides commands to monitor these factors and suggests potential remedies such as adding nodes, SSDs, faster network equipment, or load balancing.

A glimpse of cassandra 4.0 features netflix

Vinay Kumar Chella

These slides are from the recent meetup @ Uber - Apache Cassandra at Uber and Netflix on new features in 4.0. Abstract: A glimpse of Cassandra 4.0 features: There are a lot of exciting features coming in 4.0, but this talk covers some of the features that we at Netflix are particularly excited about and looking forward to. In this talk, we present an overview of just some of the many improvements shipping soon in 4.0.

Scylla Summit 2018: Scylla Feature Talks - Scylla Streaming and Repair Updates

ScyllaDB

Streaming is an internal operation that moves data from node to node over a network which. It is the foundation of various Scylla cluster operations, e.g., add node, decommission node and rebuild node. Repair is another important operation that detects the mismatch between multiple replicas on different nodes and synchronize the replicas. In this talk we will cover recent changes and performance improvements to streaming and repair. We will introduce the new Scylla streaming and the brand new row level repair that will be released in the upcoming scylla releases.

Parallelization of Structured Streaming Jobs Using Delta Lake

Databricks

Nick Fisk - low latency Ceph

ShapeBlue

This document discusses optimizing Ceph latency through hardware design. It finds that CPU frequency has a significant impact on latency, with higher frequencies resulting in lower latencies. Testing shows 4KB write latency of 2.4ms at 900MHz but 694us at higher frequencies. The document also discusses how CPU power states that wake slowly, like C6 at 85us, can negatively impact latency. Overall it advocates designing hardware with fast CPUs and avoiding slower cores or dual sockets to minimize latency in Ceph deployments.

hbaseconasia2017: Building online HBase cluster of Zhihu based on Kubernetes

HBaseCon

Zhiyong Bai As a high performance and scalable key value database, Zhihu use HBase to provide online data store system along with Mysql and Redis. Zhihu’s platform team had accumulated some experience in technology of container, and this time, based on Kubernetes, we build flexible platform of online HBase system, create multiple logic isolated HBase clusters on the shared physical cluster with fast rapid，and provide customized service for different business needs. Combined with Consul and DNS server, we implement high available access of HBase using client mainly written with Python. This presentation is mainly shared the architecture of online HBase platform in Zhihu and some practical experience in production environment. hbaseconasia2017 hbasecon hbase

25 snowflake

剑飞陈

Ceph, the future of Storage - Sage Weil

Ceph Community

Comparing Apache Cassandra 4.0, 3.0, and ScyllaDB

ScyllaDB

How does Cassandra 4.0’s performance compare to Cassandra 3.x’s? What’s been fixed in 4.0 and what remains unchanged? Should you upgrade or consider other options? Join us for a webinar where we’ll answer these questions and more, based on our extensive benchmarks comparing Cassandra 4.0 against Cassandra 3.11. We’ll also share how the new release of Cassandra stacks up against Scylla Open Source. You’ll learn the rationale and results for our head-to-head comparisons, including: - Throughput under various loads - Comparison of long-tail (p95, p99) latencies - Improvements to operations such as compactions If you are considering upgrading your existing infrastructure from Cassandra 3.11, or if you are considering a new wide column database for a greenfield deployment, this is a session you won’t want to miss!

Scylla Summit 2018: Meshify - A Case Study, or Petshop Seamonsters

ScyllaDB

Introduce_non-volatile_generic_object_programming_model_for_In-Memory_Computing

YanpingWang

This document introduces Apache Mnemonic, an open source project that provides a non-volatile programming model for Java applications to improve performance of in-memory computing frameworks like Spark. It describes how Mnemonic allows data to be stored and processed directly in persistent memory rather than being serialized to disk. Experimental results show Mnemonic can significantly reduce Spark MLlib Kmeans execution time by avoiding object serialization and spilling to disk.

DataEngConf SF16 - BYOMQ: Why We [re]Built IronMQ

Hakka Labs

The document discusses reasons for reimplementing messaging queue functionality instead of using an existing solution like RabbitMQ or Kafka. It notes that reimplementing provides full understanding and control, allowing for quick fixes and easy addition of features without needing a daemon. However, it also acknowledges that reimplementing takes a long time to reach stability. The document then describes the specific requirements for the reimplemented messaging queue and provides a brief overview of its design and performance.

DatEngConf SF16 - Apache Kudu: Fast Analytics on Fast Data

Hakka Labs

This document discusses Apache Kudu, an open source columnar storage system for analytics workloads on Hadoop. Kudu is designed to enable both fast analytics queries as well as real-time updates on fast changing data. It aims to fill gaps in the current Hadoop storage landscape by supporting simultaneous high throughput scans, low latency reads/writes, and ACID transactions. An example use case described is for real-time fraud detection on streaming financial data.

HTTP Analytics for 6M requests per second using ClickHouse, by Alexander Boc...

Altinity Ltd

This document summarizes Cloudflare's use of ClickHouse to analyze over 6 million HTTP requests per second. Some key points: - Cloudflare previously used PostgreSQL, Citus, and Flink but these did not scale sufficiently. - ClickHouse was chosen as it is fast, scalable, fault tolerant, and Cloudflare had existing expertise in it. - Cloudflare designed ClickHouse schemas to aggregate HTTP data into totals, breakdowns by category, and unique counts into two tables using different engines. - Tuning ClickHouse index granularity improved query latency by 50% and throughput by 3x. - The new ClickHouse pipeline is more scalable, fault tolerant

Building Value Within the Heavy Vehicle Industry Using Big Data and Streaming...

DataWorks Summit

More than 300,000 globally connected Scania vehicles, trucks and buses, are continuously submitting their GPS positions. In this presentation we will show how Scania analyses these positions to obtain valuable information about the operation of the vehicles. The algorithms have been developed in a research project, FUMA, that is run as a joint venture between Fraunhofer Chalmers Centre and Scania. In the project we build a continuous delivery pipeline that enables us to iteratively improve our code, our algorithms and the data deliverables. The pipeline runs on a Hortonworks platform using Apache Spark Streaming. In the build pipeline we use Jenkins, Nexus and Ansible to test, deploy and run Apache Spark Streaming jobs and the results are pushed to Apache Kafka. We will highlight and present some of the steps we have taken in order to put a streaming big data application in production at a manufacturing company. We think that people with a general awareness of the challenges with big data, the possibilities of the streaming paradigm and the need for continuous delivery will find this talk very intriguing. In this presentation you will learn how we develop and run the code and how we ensure that we are creating value for Scania.

WebRTC Webinar & Q&A - Sumilcast Standards & Implementation

Amir Zmora

Optimising Service Deployment and Infrastructure Resource Configuration

RECAP Project

What's hot

Scylla on Kubernetes: Introducing the Scylla Operator

ScyllaDB

Reactive Streams, linking Reactive Application to Spark Streaming by Luc Bour...

Spark Summit

Redpanda and ClickHouse

Altinity Ltd

Getting Started with Apache Cassandra and Apache Zeppelin (DuyHai DOAN, DataS...

DataStax

Redis Networking Nerd Down: For Lovers of Packets and Jumbo Frames- John Bull...

Redis Labs

Aerospike: Maximizing Performance

Aerospike, Inc.

A glimpse of cassandra 4.0 features netflix

Vinay Kumar Chella

Scylla Summit 2018: Scylla Feature Talks - Scylla Streaming and Repair Updates

ScyllaDB

Parallelization of Structured Streaming Jobs Using Delta Lake

Databricks

Nick Fisk - low latency Ceph

ShapeBlue

hbaseconasia2017: Building online HBase cluster of Zhihu based on Kubernetes

HBaseCon

25 snowflake

剑飞陈

Ceph, the future of Storage - Sage Weil

Ceph Community

Comparing Apache Cassandra 4.0, 3.0, and ScyllaDB

ScyllaDB

Scylla Summit 2018: Meshify - A Case Study, or Petshop Seamonsters

ScyllaDB

Introduce_non-volatile_generic_object_programming_model_for_In-Memory_Computing

YanpingWang

DataEngConf SF16 - BYOMQ: Why We [re]Built IronMQ

Hakka Labs

DatEngConf SF16 - Apache Kudu: Fast Analytics on Fast Data

Hakka Labs

HTTP Analytics for 6M requests per second using ClickHouse, by Alexander Boc...

Altinity Ltd

Building Value Within the Heavy Vehicle Industry Using Big Data and Streaming...

DataWorks Summit

What's hot (20)

Scylla on Kubernetes: Introducing the Scylla Operator

Reactive Streams, linking Reactive Application to Spark Streaming by Luc Bour...

Redpanda and ClickHouse

Getting Started with Apache Cassandra and Apache Zeppelin (DuyHai DOAN, DataS...

Redis Networking Nerd Down: For Lovers of Packets and Jumbo Frames- John Bull...

Aerospike: Maximizing Performance

A glimpse of cassandra 4.0 features netflix

Scylla Summit 2018: Scylla Feature Talks - Scylla Streaming and Repair Updates

Parallelization of Structured Streaming Jobs Using Delta Lake

Nick Fisk - low latency Ceph

hbaseconasia2017: Building online HBase cluster of Zhihu based on Kubernetes

25 snowflake

Ceph, the future of Storage - Sage Weil

Comparing Apache Cassandra 4.0, 3.0, and ScyllaDB

Scylla Summit 2018: Meshify - A Case Study, or Petshop Seamonsters

Introduce_non-volatile_generic_object_programming_model_for_In-Memory_Computing

DataEngConf SF16 - BYOMQ: Why We [re]Built IronMQ

DatEngConf SF16 - Apache Kudu: Fast Analytics on Fast Data

HTTP Analytics for 6M requests per second using ClickHouse, by Alexander Boc...

Building Value Within the Heavy Vehicle Industry Using Big Data and Streaming...

Real-Time or Analytics Workloads... Why Not Both?

ScyllaDB

ScyllaDB’s Workload Prioritization provides resource optimization and performance isolation across workloads with different performance needs, such as Analytics and Real-time. In this session, you will learn how Workload Prioritization works, how you can use it to run different types of workloads together under a single ScyllaDB cluster, and how to fine-tune priorities and resource allocation based on your specific requirements.

Real-Time Persisted Events at Supercell

ScyllaDB

ScyllaDB Leaps Forward with Dor Laor, CEO of ScyllaDB

ScyllaDB

An All-Around Benchmark of the DBaaS Market

ScyllaDB

The entire database market is moving towards Database-as-a-Service (DBaaS), resulting in a heterogeneous DBaaS landscape shaped by database vendors, cloud providers, and DBaaS brokers. This DBaaS landscape is rapidly evolving and the DBaaS products differ in their features but also their price and performance capabilities. In consequence, selecting the optimal DBaaS provider for the customer needs becomes a challenge, especially for performance-critical applications. To enable an on-demand comparison of the DBaaS landscape we present the benchANT DBaaS Navigator, an open DBaaS comparison platform for management and deployment features, costs, and performance. The DBaaS Navigator is an open data platform that enables the comparison of over 20 DBaaS providers for the relational and NoSQL databases. This talk will provide a brief overview of the benchmarked categories with a focus on the technical categories such as price/performance for NoSQL DBaaS and how ScyllaDB Cloud is performing.

Discover the Unseen: Tailored Recommendation of Unwatched Content

ScyllaDB

The session shares how JioCinema approaches ""watch discounting."" This capability ensures that if a user watched a certain amount of a show/movie, the platform no longer recommends that particular content to the user. Flawless operation of this feature promotes the discover of new content, improving the overall user experience. JioCinema is an Indian over-the-top media streaming service owned by Viacom18.

So You've Lost Quorum: Lessons From Accidental Downtime

ScyllaDB

The best thing about databases is that they always work as intended, and never suffer any downtime. You'll never see a system go offline because of a database outage. In this talk, Bo Ingram -- staff engineer at Discord and author of ScyllaDB in Action --- dives into an outage with one of their ScyllaDB clusters, showing how a stressed ScyllaDB cluster looks and behaves during an incident. You'll learn about how to diagnose issues in your clusters, see how external failure modes manifest in ScyllaDB, and how you can avoid making a fault too big to tolerate.

ScyllaDB Tablets: Rethinking Replication

ScyllaDB

ScyllaDB is making a major architecture shift. We’re moving from vNode replication to tablets – fragments of tables that are distributed independently, enabling dynamic data distribution and extreme elasticity. In this keynote, ScyllaDB co-founder and CTO Avi Kivity explains the reason for this shift, provides a look at the implementation and roadmap, and shares how this shift benefits ScyllaDB users.

Getting the Most Out of ScyllaDB Monitoring: ShareChat's Tips

ScyllaDB

ScyllaDB monitoring provides a lot of useful information. But sometimes it’s not easy to find the root of the problem if something is wrong or even estimate the remaining capacity by the load on the cluster. This talk shares our team's practical tips on: 1) How to find the root of the problem by metrics if ScyllaDB is slow 2) How to interpret the load and plan capacity for the future 3) Compaction strategies and how to choose the right one 4) Important metrics which aren’t available in the default monitoring setup.

More from ScyllaDB (20)

Radically Outperforming DynamoDB @ Digital Turbine with SADA and Google Cloud

ScyllaDB Cloud: Faster and More Flexible

The Strategy Behind ReversingLabs’ Massive Key-Value Migration

ScyllaDB Topology on Raft: An Inside Look

CTO Insights: Steering a High-Stakes Database Migration

MongoDB vs ScyllaDB: Tractian’s Experience with Real-Time ML

Inside Expedia's Migration to ScyllaDB for Change Data Capture

Terraform Best Practices for Infrastructure Scaling

Elasticity vs. State? Exploring Kafka Streams Cassandra State Store

DynamoDB to ScyllaDB: Technical Comparison and the Path to Success

ScyllaDB Real-Time Event Processing with CDC

MongoDB to ScyllaDB: Technical Comparison and the Path to Success

Real-Time or Analytics Workloads... Why Not Both?

Real-Time Persisted Events at Supercell

ScyllaDB Leaps Forward with Dor Laor, CEO of ScyllaDB

An All-Around Benchmark of the DBaaS Market

Discover the Unseen: Tailored Recommendation of Unwatched Content

So You've Lost Quorum: Lessons From Accidental Downtime

ScyllaDB Tablets: Rethinking Replication

Getting the Most Out of ScyllaDB Monitoring: ShareChat's Tips

Recently uploaded

Mutation Testing for Task-Oriented Chatbots

Pablo Gómez Abajo

Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots. To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.

Northern Engraving | Modern Metal Trim, Nameplates and Appliance Panels

Northern Engraving

The Microsoft 365 Migration Tutorial For Beginner.pptx

operationspcvita

Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency

ScyllaDB

Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors

DianaGray10

Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more. The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications. We’ll discuss and demo the benefits of UiPath Apps and connectors including: Creating a compelling user experience for any software, without the limitations of APIs. Accelerating the app creation process, saving time and effort Enjoying high-performance CRUD (create, read, update, delete) operations, for seamless data management. Speakers: Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP Charlie Greenberg, host

Nordic Marketo Engage User Group_June 13_ 2024.pptx

MichaelKnudsen27

What is an RPA CoE? Session 2 – CoE Roles

DianaGray10

Apps Break Data

Ivo Velitchkov

How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?

Essentials of Automations: Exploring Attributes & Automation Parameters

Safe Software

Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they? Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality. You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.

Day 2 - Intro to UiPath Studio Fundamentals

UiPathCommunity

In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project. 📕 Detailed agenda: Variables and Datatypes Workflow Layouts Arguments Control Flows and Loops Conditional Statements 💻 Extra training through UiPath Academy: Variables, Constants, and Arguments in Studio Control Flow in Studio

Y-Combinator seed pitch deck template PP

c5vrf27qcz

Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians

Neo4j

"NATO Hackathon Winner: AI-Powered Drug Search", Taras Kloba

Fwdays

This is a session that details how PostgreSQL's features and Azure AI Services can be effectively used to significantly enhance the search functionality in any application. In this session, we'll share insights on how we used PostgreSQL to facilitate precise searches across multiple fields in our mobile application. The techniques include using LIKE and ILIKE operators and integrating a trigram-based search to handle potential misspellings, thereby increasing the search accuracy. We'll also discuss how the azure_ai extension on PostgreSQL databases in Azure and Azure AI Services were utilized to create vectors from user input, a feature beneficial when users wish to find specific items based on text prompts. While our application's case study involves a drug search, the techniques and principles shared in this session can be adapted to improve search functionality in a wide range of applications. Join us to learn how PostgreSQL and Azure AI can be harnessed to enhance your application's search capability.

Dandelion Hashtable: beyond billion requests per second on a commodity server

Antonios Katsarakis

This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).

Northern Engraving | Nameplate Manufacturing Process - 2024

Northern Engraving

Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!

GNSS spoofing via SDR (Criptored Talks 2024)

Javier Junquera

In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security. This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing. The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.

Astute Business Solutions | Oracle Cloud Partner |

AstuteBusiness

"Choosing proper type of scaling", Olena Syrota

Fwdays

zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...

Alex Pruden

Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security. Paper Link: https://eprint.iacr.org/2024/257

"Scaling RAG Applications to serve millions of users", Kevin Goedecke

Fwdays

Recently uploaded (20)

Mutation Testing for Task-Oriented Chatbots

Northern Engraving | Modern Metal Trim, Nameplates and Appliance Panels

The Microsoft 365 Migration Tutorial For Beginner.pptx

Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency

Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors

Nordic Marketo Engage User Group_June 13_ 2024.pptx

What is an RPA CoE? Session 2 – CoE Roles

Apps Break Data

Essentials of Automations: Exploring Attributes & Automation Parameters

Day 2 - Intro to UiPath Studio Fundamentals

Y-Combinator seed pitch deck template PP

Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians

"NATO Hackathon Winner: AI-Powered Drug Search", Taras Kloba

Dandelion Hashtable: beyond billion requests per second on a commodity server

Northern Engraving | Nameplate Manufacturing Process - 2024

GNSS spoofing via SDR (Criptored Talks 2024)

Astute Business Solutions | Oracle Cloud Partner |

"Choosing proper type of scaling", Olena Syrota

zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...

"Scaling RAG Applications to serve millions of users", Kevin Goedecke

Building a Video Streaming Testing Framework with Seastar

1. Building a video streaming testing framework with Seastar Nicolas Le Scouarnec

2. Nicolas Le Scouarnec Senior R&D Engineer, Broadpeak Nicolas Le Scouarnec is an R&D engineer at Broadpeak, a provider of video streaming solutions for ISPs and content providers. He currently focuses on performance evaluation of video delivery systems. Previously, he worked on optimizing networked systems (Krononat, Cuckoo++), or machine learning applications (Faiss-QuickerADC). 2

3. Benchmark/Load test Broadpeak’s Video CDN solutions Our Goal ... Android, Browsers, … (Video players) HTTP/1.1, HTTPS/1.1, HTTP2, … Caching server (High-performance)

4. Our Goal Benchmark Efficiently Reuse COTS servers ■ failover server ■ multi-purpose test server Scale ■ 10s to 100+ Gbps Networking ■ 10 000s concurrent clients Benchmark

5. Current Solutions Apache JMeter (10-20 Gbps) Commercial Hardware Appliances (20-100+ Gbps) DASH / HLS over HTTP Client Emulation • Open, can be extended • One simulated client <-> One thread • Can’t pile 10 servers to test 1 • High performance • Cannot be “moved/replicated” • Precludes on-customer premises load tests • Delay streaming protocol evolutions

6. What we did Pick Seastar as a basis ■ IO Intensiveness (good fit for DPDK stack) ■ High-concurrency / asynchronous Pick Chromium (widely used browser) ■ Full HTTP protocol support (HTTPS/1.1, HTTP2, HTTP/3), compression, pooling… ■ Highly-realistic client emulation HLS/DASH Player Emulation ■ Leverage Seastar / One Thread per Core ■ Coroutines, Ragel ■ Support latest evolutions (low latency, DASH templates)

7. Why Seastar than DPDK-based X? Full TCP stack ■ With minor fixes runs well in our benchmarks ■ “Socket”-like abstraction Asynchronous model good fit for “video” application ■ Scheduling of video parts download ■ Scheduling of periodic manifest updates ■ Simulating buffer draining, timeouts, … ■ Constant-rate scheduling for avoiding coordinated omission

8. Improving Seastar for client applications Network IO/Intensive (Benchmarking) ■ Seastar periodically yields the CPU to the kernel ■ Get rid of context switches for pure-networking benchmark app Seastar Native Network Stack limits ■ Single NIC ■ Designed first for server applications ( Client -> Seastar Server) ● Network Connection to Core mapping done through RSS ● Good fit for Seastar-received connections ■ New “multi-NIC” packet steering for Seastar-initiated connections & beyond

9. Exposing each core on the network Physical NIC RxQ RxQ RxQ @IP @MAC @IP @MAC @IP @MAC RxQ @IP @MAC RxQ@MAC RxQ @IP @MAC vNIC vNIC vNIC vNIC CPU0 CPU1 CPU5 CPU2 CPU3 CPU4 vNIC vNIC @IP Physical NIC CPU0 CPU1 CPU7 RxQ0 RxQ1 RxQ7 CPU2RxQ2 CPU3RxQ3 CPU6RxQ6 @IP RSSHashing @MAC ... Seastar’s vanilla native networking stack Broadpeak S2C networking stack ...

10. Physical NIC Supporting multiple NICs RxQ RxQ RxQ RxQ RxQ RxQ vNIC vNIC vNIC vNIC CPU0 CPU1 CPU7 CPU2 CPU3 CPU6 vNIC vNIC ... @IP @MAC @IP @MAC @IP @MAC @IP @MAC @MAC @IP @MAC @IP

11. Going back to server applications TOR Switch Physical NIC CPU0 CPU1 CPU7 RxQ RxQ RxQ CPU2RxQ CPU3RxQ CPU6RxQ @IP ECMPHashing With a single IP (server applications) @MAC @MAC @MAC @MAC @MAC @MAC vNIC vNIC vNIC vNIC vNIC vNIC ...

12. Conclusion Seastar for an HTTP benchmarking tool ▪ Jmeter-like capabilities (high flexibility) ▪ But with high-performance levels ▪ Currently in use at Broadpeak for testing and benchmarking Minor changes to core Seastar ▪ How to use multiple NICs and handle “reverse” traffic ? ▪ Implementation of some of Krononat principles (USENIX ATC 2018)

13. Thank You @broadpeak broadpeak / Nicolas Le Scouarnec Nicolas Le Scouarnec Nicolas.LeScouarnec@broadpeak.tv

Building a Video Streaming Testing Framework with Seastar

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Building a Video Streaming Testing Framework with Seastar

Similar to Building a Video Streaming Testing Framework with Seastar (20)

More from ScyllaDB

More from ScyllaDB (20)

Recently uploaded

Recently uploaded (20)

Building a Video Streaming Testing Framework with Seastar