The document discusses the role of the Elastic Load Balancer (ELB) in Apache Stratos PaaS. It describes how the ELB uses components like Synapse, Axis2, and Tribes to distribute incoming traffic across backend nodes and auto-scale capacity. The ELB handles load balancing, failover, auto-scaling, and multi-tenancy. It integrates with Stratos by receiving topology information, load balancing requests to cartridge instances, and auto-scaling the number of instances based on traffic.
Apache Stratos (incubating) Hangout IV - Stratos Controller and CLI InternalsIsuru Perera
Slides used for Apache Stratos (incubating) Fourth Hangout. Hangout video can be found at http://youtu.be/VtF9DVGKbTQ
Website: http://stratos.incubator.apache.org
Mailing List:
Subscribe: dev-subscribe@stratos.incubator.apache.org
Post (after subscription): dev@stratos.incubator.apache.org
Social Media:
Google+: https://plus.google.com/103515557134069849802
Twitter: https://twitter.com/ApacheStratos
Facebook: https://www.facebook.com/apache.stratos
LinkedIn: http://www.linkedin.com/groups?home=&gid=5131436
Apache Stratos (incubating) Hangout IV - Stratos Controller and CLI InternalsIsuru Perera
Slides used for Apache Stratos (incubating) Fourth Hangout. Hangout video can be found at http://youtu.be/VtF9DVGKbTQ
Website: http://stratos.incubator.apache.org
Mailing List:
Subscribe: dev-subscribe@stratos.incubator.apache.org
Post (after subscription): dev@stratos.incubator.apache.org
Social Media:
Google+: https://plus.google.com/103515557134069849802
Twitter: https://twitter.com/ApacheStratos
Facebook: https://www.facebook.com/apache.stratos
LinkedIn: http://www.linkedin.com/groups?home=&gid=5131436
Tales from the four-comma club: Managing Kafka as a service at Salesforce | L...HostedbyConfluent
Apache Kafka is a key part of the Big Data infrastructure at Salesforce, enabling publish/subscribe and data transport in near real-time at enterprise scale handling trillions of messages per day. In this session, hear from the teams at Salesforce that manage Kafka as a service, running over a hundred clusters across on-premise and public cloud environments with over 99.9% availability. Hear about best practices and innovations, including:
* How to manage multi-tenant clusters in a hybrid environment
* High volume data pipelines with Mirus replicating data to Kafka and blob storage
* Kafka Fault Injection Framework built on Trogdor and Kibosh
* Automated recovery without data loss
* Using Envoy as an SNI-routing Kafka gateway
We hope the audience will have practical takeaways for building, deploying, operating, and managing Kafka at scale in the enterprise.
How to Autoscale in Apache Cloudstack using LiquiD AutoScalerBob Bennink
The presentation shows how to use LiquiD AutoScaler for autoscaling in Apache CloudStack. It works with any load-balancer and does not require any coding skills.
Setting the infrastructure up takes minutes and no additional hardware or software is required.
Its benefits are better better responsiveness during high traffic, high available websites, lower costs and lower energy consumption.
It provides IaaS providers with additional functionality to their CloudStack cloud orchestration platforms.
It can be used to monitor websites, web apps and other infrastructure and runs in public and private clouds.
KSQL is an open source streaming SQL engine for Apache Kafka. Come hear how KSQL makes it easy to get started with a wide-range of stream processing applications such as real-time ETL, sessionization, monitoring and alerting, or fraud detection. We'll cover both how to get started with KSQL and some under-the-hood details of how it all works.
What happened when our biggest and most important Kafka cluster went rogue all of a sudden, and while trying to recover it, a single, crucial misconfiguration made things even worse?
At a company like Taboola, where service availability and latency are our top priority, this was a disaster.
With 300K messages/sec and 250TB of messages produced each day to our on-premise Kafka clusters, and mirrored to our central Kafka cluster, we always try to ensure Kafka behaves well under high loads of traffic and unexpected cluster failures. So when our main Kafka cluster went crazy we had a serious issue on our hands.
This session is the story of how we learned the hard way about mitigating cluster failures with the proper configurations in place.
See Webinar Recording at https://resource.alibabacloud.com/webinar/detail.htm?webinarId=11
Get up to speed with the basics of cloud networking, including network setup, VPC, routing, and switching between multiple subnets.
In this presentation, we will discuss various aspects of how networking varies within the cloud, including:
1. Basic networking and how it maps to the cloud
2. Various Cloud networking concepts that allow customers to design their own network within the cloud
3. Run through examples of basic network setup, VPC, Routing, and switching between multiple subnets
4. Network security with security groups and VPN
More Webinars: https://resource.alibabacloud.com/webinar/index.htm
Server Load Balancer: www.alibabacloud.com/product/server-load-balancer
VPC: www.alibabacloud.com/product/vpc
Express Connect: www.alibabacloud.com/product/express-connect
VPN Gateway: www.alibabacloud.com/product/vpn-gateway
Monitoring, the Prometheus Way - Julius Voltz, Prometheus Docker, Inc.
Prometheus is an opinionated metrics collection and monitoring system that is particularly well suited to accommodate modern workloads like containers and micro-services. To achieve these goals, it radically breaks away from existing systems and follows very different design principles. In this talk, Prometheus founder Julius Volz will explain these design principles and how they apply to dockerized applications. This will provide insight useful to newcomers wanting to start on the right foot in the land of container monitoring, but also to veterans wanting to quickly map their existing knowledge to Prometheus concepts. In particular, a demo will show Prometheus in action together with a Docker Swarm cluster.
Hagen Toennies from Gaikai Inc. presented this deck at the 2017 HPC Advisory Council Stanford Conference.
"In this talk we will present how we enable distributed, Unix style programming using Docker and Apache Kafka. We will show how we can take the famous Unix Pipe Pattern and apply it to a Distributed Computing System. We will demonstrate the development of two simple applications with the focus on "Do One Thing and Do It Well." Afterwards we demonstrate how we make these two programs work to together using Apache Kafka. By encapsulating our applications in containers we will also show how that enables us to go from the limited resources of a development machine to cluster of computers in a data center without changing our applications or containers."
Watch the video: http://wp.me/p3RLHQ-goG
Learn more: http://www.hpcadvisorycouncil.com/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Building an Event-oriented Data Platform with Kafka, Eric Sammer confluent
While we frequently talk about how to build interesting products on top of machine and event data, the reality is that collecting, organizing, providing access to, and managing this data is where most people get stuck. Many organizations understand the use cases around their data – fraud detection, quality of service and technical operations, user behavior analysis, for example – but are not necessarily data infrastructure experts. In this session, we’ll follow the flow of data through an end to end system built to handle tens of terabytes an hour of event-oriented data, providing real time streaming, in-memory, SQL, and batch access to this data. We’ll go into detail on how open source systems such as Hadoop, Kafka, Solr, and Impala/Hive are actually stitched together; describe how and where to perform data transformation and aggregation; provide a simple and pragmatic way of managing event metadata; and talk about how applications built on top of this platform get access to data and extend its functionality.
Attendees will leave this session knowing not just which open source projects go into a system such as this, but how they work together, what tradeoffs and decisions need to be addressed, and how to present a single general purpose data platform to multiple applications. This session should be attended by data infrastructure engineers and architects planning, building, or maintaining similar systems.
Using the JMS 2.0 API with Apache Pulsar - Pulsar Virtual Summit Europe 2021StreamNative
For a long time Java Messaging Service has been the API to handle messaging systems in the Java World, and now the messaging ecosystem is moving to the next generation of streaming services like Apache Pulsar.
Why? Because Pulsar is free, Open Source, Cloud Native and it comes with cool new features that are not well supported by traditional JMS vendors.
In this session you will see how to use Pulsar in a JakartaEE Web Application deployed on Apache TomEE via the JMS/EJB API, without installing any additional components to your cluster.
Building High-Throughput, Low-Latency Pipelines in Kafkaconfluent
William Hill is one of the UK’s largest, most well-established gaming companies with a global presence across 9 countries with over 16,000 employees. In recent years the gaming industry and in particular sports betting, has been revolutionised by technology. Customers now demand a wide range of events and markets to bet on both pre-game and in-play 24/7. This has driven out a business need to process more data, provide more updates and offer more markets and prices in real time.
At William Hill, we have invested in a completely new trading platform using Apache Kafka. We process vast quantities of data from a variety of feeds, this data is fed through a variety of odds compilation models, before being piped out to UI apps for use by our trading teams to provide events, markets and pricing data out to various end points across the whole of William Hill. We deal with thousands of sporting events, each with sometimes hundreds of betting markets, each market receiving hundreds of updates. This scales up to vast numbers of messages flowing through our system. We have to process, transform and route that data in real time. Using Apache Kafka, we have built a high throughput, low latency pipeline, based on Cloud hosted Microservices. When we started, we were on a steep learning curve with Kafka, Microservices and associated technologies. This led to fast learnings and fast failings.
In this session, we will tell the story of what we built, what went well, what didn’t go so well and what we learnt. This is a story of how a team of developers learnt (and are still learning) how to use Kafka. We hope that you will be able to take away lessons and learnings of how to build a data processing pipeline with Apache Kafka.
Tales from the four-comma club: Managing Kafka as a service at Salesforce | L...HostedbyConfluent
Apache Kafka is a key part of the Big Data infrastructure at Salesforce, enabling publish/subscribe and data transport in near real-time at enterprise scale handling trillions of messages per day. In this session, hear from the teams at Salesforce that manage Kafka as a service, running over a hundred clusters across on-premise and public cloud environments with over 99.9% availability. Hear about best practices and innovations, including:
* How to manage multi-tenant clusters in a hybrid environment
* High volume data pipelines with Mirus replicating data to Kafka and blob storage
* Kafka Fault Injection Framework built on Trogdor and Kibosh
* Automated recovery without data loss
* Using Envoy as an SNI-routing Kafka gateway
We hope the audience will have practical takeaways for building, deploying, operating, and managing Kafka at scale in the enterprise.
How to Autoscale in Apache Cloudstack using LiquiD AutoScalerBob Bennink
The presentation shows how to use LiquiD AutoScaler for autoscaling in Apache CloudStack. It works with any load-balancer and does not require any coding skills.
Setting the infrastructure up takes minutes and no additional hardware or software is required.
Its benefits are better better responsiveness during high traffic, high available websites, lower costs and lower energy consumption.
It provides IaaS providers with additional functionality to their CloudStack cloud orchestration platforms.
It can be used to monitor websites, web apps and other infrastructure and runs in public and private clouds.
KSQL is an open source streaming SQL engine for Apache Kafka. Come hear how KSQL makes it easy to get started with a wide-range of stream processing applications such as real-time ETL, sessionization, monitoring and alerting, or fraud detection. We'll cover both how to get started with KSQL and some under-the-hood details of how it all works.
What happened when our biggest and most important Kafka cluster went rogue all of a sudden, and while trying to recover it, a single, crucial misconfiguration made things even worse?
At a company like Taboola, where service availability and latency are our top priority, this was a disaster.
With 300K messages/sec and 250TB of messages produced each day to our on-premise Kafka clusters, and mirrored to our central Kafka cluster, we always try to ensure Kafka behaves well under high loads of traffic and unexpected cluster failures. So when our main Kafka cluster went crazy we had a serious issue on our hands.
This session is the story of how we learned the hard way about mitigating cluster failures with the proper configurations in place.
See Webinar Recording at https://resource.alibabacloud.com/webinar/detail.htm?webinarId=11
Get up to speed with the basics of cloud networking, including network setup, VPC, routing, and switching between multiple subnets.
In this presentation, we will discuss various aspects of how networking varies within the cloud, including:
1. Basic networking and how it maps to the cloud
2. Various Cloud networking concepts that allow customers to design their own network within the cloud
3. Run through examples of basic network setup, VPC, Routing, and switching between multiple subnets
4. Network security with security groups and VPN
More Webinars: https://resource.alibabacloud.com/webinar/index.htm
Server Load Balancer: www.alibabacloud.com/product/server-load-balancer
VPC: www.alibabacloud.com/product/vpc
Express Connect: www.alibabacloud.com/product/express-connect
VPN Gateway: www.alibabacloud.com/product/vpn-gateway
Monitoring, the Prometheus Way - Julius Voltz, Prometheus Docker, Inc.
Prometheus is an opinionated metrics collection and monitoring system that is particularly well suited to accommodate modern workloads like containers and micro-services. To achieve these goals, it radically breaks away from existing systems and follows very different design principles. In this talk, Prometheus founder Julius Volz will explain these design principles and how they apply to dockerized applications. This will provide insight useful to newcomers wanting to start on the right foot in the land of container monitoring, but also to veterans wanting to quickly map their existing knowledge to Prometheus concepts. In particular, a demo will show Prometheus in action together with a Docker Swarm cluster.
Hagen Toennies from Gaikai Inc. presented this deck at the 2017 HPC Advisory Council Stanford Conference.
"In this talk we will present how we enable distributed, Unix style programming using Docker and Apache Kafka. We will show how we can take the famous Unix Pipe Pattern and apply it to a Distributed Computing System. We will demonstrate the development of two simple applications with the focus on "Do One Thing and Do It Well." Afterwards we demonstrate how we make these two programs work to together using Apache Kafka. By encapsulating our applications in containers we will also show how that enables us to go from the limited resources of a development machine to cluster of computers in a data center without changing our applications or containers."
Watch the video: http://wp.me/p3RLHQ-goG
Learn more: http://www.hpcadvisorycouncil.com/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Building an Event-oriented Data Platform with Kafka, Eric Sammer confluent
While we frequently talk about how to build interesting products on top of machine and event data, the reality is that collecting, organizing, providing access to, and managing this data is where most people get stuck. Many organizations understand the use cases around their data – fraud detection, quality of service and technical operations, user behavior analysis, for example – but are not necessarily data infrastructure experts. In this session, we’ll follow the flow of data through an end to end system built to handle tens of terabytes an hour of event-oriented data, providing real time streaming, in-memory, SQL, and batch access to this data. We’ll go into detail on how open source systems such as Hadoop, Kafka, Solr, and Impala/Hive are actually stitched together; describe how and where to perform data transformation and aggregation; provide a simple and pragmatic way of managing event metadata; and talk about how applications built on top of this platform get access to data and extend its functionality.
Attendees will leave this session knowing not just which open source projects go into a system such as this, but how they work together, what tradeoffs and decisions need to be addressed, and how to present a single general purpose data platform to multiple applications. This session should be attended by data infrastructure engineers and architects planning, building, or maintaining similar systems.
Using the JMS 2.0 API with Apache Pulsar - Pulsar Virtual Summit Europe 2021StreamNative
For a long time Java Messaging Service has been the API to handle messaging systems in the Java World, and now the messaging ecosystem is moving to the next generation of streaming services like Apache Pulsar.
Why? Because Pulsar is free, Open Source, Cloud Native and it comes with cool new features that are not well supported by traditional JMS vendors.
In this session you will see how to use Pulsar in a JakartaEE Web Application deployed on Apache TomEE via the JMS/EJB API, without installing any additional components to your cluster.
Building High-Throughput, Low-Latency Pipelines in Kafkaconfluent
William Hill is one of the UK’s largest, most well-established gaming companies with a global presence across 9 countries with over 16,000 employees. In recent years the gaming industry and in particular sports betting, has been revolutionised by technology. Customers now demand a wide range of events and markets to bet on both pre-game and in-play 24/7. This has driven out a business need to process more data, provide more updates and offer more markets and prices in real time.
At William Hill, we have invested in a completely new trading platform using Apache Kafka. We process vast quantities of data from a variety of feeds, this data is fed through a variety of odds compilation models, before being piped out to UI apps for use by our trading teams to provide events, markets and pricing data out to various end points across the whole of William Hill. We deal with thousands of sporting events, each with sometimes hundreds of betting markets, each market receiving hundreds of updates. This scales up to vast numbers of messages flowing through our system. We have to process, transform and route that data in real time. Using Apache Kafka, we have built a high throughput, low latency pipeline, based on Cloud hosted Microservices. When we started, we were on a steep learning curve with Kafka, Microservices and associated technologies. This led to fast learnings and fast failings.
In this session, we will tell the story of what we built, what went well, what didn’t go so well and what we learnt. This is a story of how a team of developers learnt (and are still learning) how to use Kafka. We hope that you will be able to take away lessons and learnings of how to build a data processing pipeline with Apache Kafka.
PaaS Design & Architecture: A Deep Dive into Apache StratosWSO2
The design and architecture of Stratos present some unique advantages to the users. The multi-tenancy model, where it allows high multi-tenancy density within a deployment is a key advantage. The ability to control IaaS resources, per could, per region, per zone
paves the way to easily achieve high availability and disaster recover. Multi-factor based auto scaling, dynamic load balancing and cloudbusting are some of the other key noteworthy differentiators in Stratos PaaS. This session will highlight the advantages of using Apache Stratos (Incubating) as your PaaS framework.
Amazon CloudFront Office Hour, “Using Amazon CloudFront with Amazon S3 & AWS ...Amazon Web Services
These slides cover information from the August 9, 2016 Amazon CloudFront office hour, which includes a brief overview on Amazon Cloudfront, key benefits of the service, how to use it with Amazon S3 and AWS ELB, pricing and how to get started.
Highly Available Load Balanced Galera MySql ClusterAmr Fawzy
Describing the major principles of well designed cloud system application including high availability and load balancing as well as implementing highly available load balanced galera mysql cluster
Stop Worrying and Keep Querying, Using Automated Multi-Region Disaster RecoveryDoKC
Stop Worrying and Keep Querying, Using Automated Multi-Region Disaster Recovery - Shivani Gupta, Elotl & Sergey Pronin, Percona
Disaster Recovery(DR) is critical for business continuity in the face of widespread outages taking down entire data centers or cloud provider regions. DR relies on deployment to multiple locations, data replication, monitoring for failure and failover. The process is typically manual involving several moving parts, and, even in the best case, involves some downtime for end-users. A multi-cluster K8s control plane presents the opportunity to automate the DR setup as well as the failure detection and failover. Such automation can dramatically reduce RTO and improve availability for end-users. This talk (and demo) describes one such setup using the open source Percona Operator for PostgreSQL and a multi-cluster K8s orchestrator. The orchestrator will use policy driven placement to replicate the entire workload on multiple clusters (in different regions), detect failure using pluggable logic, and do failover processing by promoting the standby as well as redirecting application traffic
MariaDB Auto-Clustering, Vertical and Horizontal Scaling within Jelastic PaaSJelastic Multi-Cloud PaaS
Availability and performance have a direct business impact for most of the companies nowadays. No one wants to lose money because of occasional downtime or data loss. Thus, to minimize the risk and ensure an extra level of redundancy, clustering and automatic scaling should be used. In this video Ruslan Synytsky presented how Jelastic PaaS implemented auto-clustering of MariaDB by providing the customers with different replication options out-of-box with no need in manual configurations. It is also detailed how to automate vertical and horizontal scaling of databases running in the cloud.
Video recording of the session https://www.youtube.com/watch?v=6MND3feb5zM
AskTom: How to Make and Test Your Application "Oracle RAC Ready"?Markus Michalewicz
Oracle Real Application Clusters (Oracle RAC) is the preferred availability and scalability solution for Oracle Databases, as most applications can benefit from its capabilities without making any changes. This mini session explains the secrets behind Oracle RAC’s horizontal scaling algorithm, Cache Fusion, and how you can test and ensure that your application is “Oracle RAC ready.”
This deck was first presented in OOW19 as an AskTom theater / mini session and will be presented as a full version in other conferences going forward at which time I will provide an updated version of the deck.
Container orchestration from theory to practiceDocker, Inc.
"Join Laura Frank and Stephen Day as they explain and examine technical concepts behind container orchestration systems, like distributed consensus, object models, and node topology. These concepts build the foundation of every modern orchestration system, and each technical explanation will be illustrated using SwarmKit and Kubernetes as a real-world example. Gain a deeper understanding of how orchestration systems work in practice and walk away with more insights into your production applications."
Container Orchestration from Theory to PracticeDocker, Inc.
Join Laura Frank and Stephen Day as they explain and examine technical concepts behind container orchestration systems, like distributed consensus, object models, and node topology. These concepts build the foundation of every modern orchestration system, and each technical explanation will be illustrated using Docker’s SwarmKit as a real-world example. Gain a deeper understanding of how orchestration systems like SwarmKit work in practice and walk away with more insights into your production applications.
MuleSoft Meetup Roma - Runtime Fabric Series (From Zero to Hero) - Sessione 2Alfonso Martino
Questa presentazione tratta le seguenti tematiche:
- Service discovery su Kubernetes (Service, Ingress Controller)
- Ingress Controller setup su EKS
- Ingress Controller Template setup su RTF
- Strategie di segregazione del traffico (interno ed esterno)
- Differenze tra RTF BYOK (Bring Your Own Kubernetes) e Self-managed
Training Slides: Basics 102: Introduction to Tungsten ClusteringContinuent
This 30 minutes training session provides an introduction to how Tungsten Clustering for MySQL / MariaDB / Percona Server works, its basic principles, understanding Tungsten Clustering topologies, failover, rolling maintenance and related tools.
AGENDA
- Review the key benefits offered by Tungsten Clustering
- Examine the Tungsten Clustering architecture
- Tungsten Cluster Topologies for MySQL High Availability and Disaster Recovery
- Composite vs Multi-Site/Multi-Master
- Review automatic and manual failover
- Explore the concepts of a rolling maintenance procedure
- Study key resources to monitor and manage the cluster
Load Balancing traffic in OpenStack neutron sufianfauzani
Load balancing refers to efficiently distributing incoming network traffic across a group of backend servers, also known as a server farm or server pool.
Load balancing enables OpenStack tenants to load-balance their traffic between ports.
Efficient Resource Allocation to Virtual Machine in Cloud Computing Using an ...ijceronline
The focus of the paper is to generate an advance algorithm of resource allocation and load balancing that can deduced and avoid the dead lock while allocating the processes to virtual machine. In VM while processes are allocate they executes in queue , the first process get resources , other remains in waiting state .As rest of VM remains idle . To utilize the resources, we have analyze the algorithm with the help of First-Come, First-Served (FCFS) Scheduling, Shortest-Job-First (SJR) Scheduling, Priority Scheduling, Round Robin (RR) and CloudSIM Simulator.
Similar to The Role of Elastic Load Balancer - Apache Stratos (20)
This is the second session of Deep Dive into Kubernetes. It includes information on optimizing Docker image size, persistent volumes, container security, and different aspects of running Kubernetes on GKE and AWS.
This presentation includes information on Kubernetes Architecture, Container Orchestration, Internal Routing, External Routing, Configuration Management, Credentials Management, Persistent Volumes, Rolling Out Updates, Autoscaling, Package Management, and a Hello World example using Helm.
WSO2 API Manager Reference Architecture for Pivotal Cloud FoundryImesh Gunaratne
This presentation includes an introduction to Pivotal Cloud Foundry (PCF) and How WSO2 API Manager can be deployed on PCF using a PCF Tile, BOSH release and a Service Broker.
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/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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The Role of Elastic Load Balancer - Apache Stratos
1. The Role of
Elastic Load Balancer (ELB)
Imesh Gunaratne
Apache Contributor, Technical Lead - WSO2 Inc
2. Agenda
➔ Introduction to Load Balancing
◆ What is Load Balancing?
◆ Algorithms
◆ Node Configuration Modes
◆ Why is it called Elastic?
◆ Purpose
◆ Features
➔ Component Architecture of Apache Stratos ELB
◆ Synapse Mediation Framework
◆ Apache Axis2 Clustering
◆ Apache Tribes Group Management
◆ Binary Relay Message Builder
◆ Load Balance Endpoint Module
◆ Auto-scaling Module
2
3. Agenda (cont.)
➔ ELB’s role in Apache Stratos PaaS
◆ Apache Stratos Logical Architecture
◆ Workflow
➔ Auto-scaling Process
◆ Configuration
◆ Auto-scaling Algorithm
◆ Plugging in Custom Algorithms
➔ How to Avoid Single Point of Failure of ELB
3
5. What is Load Balancing in general?
Load balancing is a computer networking method for
distributing workloads across multiple computers or a computer
cluster, network links, central processing units, disk drives, or
other resources
- Wikipedia
http://en.wikipedia.org/wiki/Load_balancer 5
6. Load balancing is used to distribute the incoming traffic
amongst a set of backend worker nodes which are statically
configured or dynamically discovered.
http://docs.wso2.org/wiki/display/ELB203/Load+Balacing+Basics
LB
W1
W2
Wn
Clients
Worker nodes
Incoming Traffic
6
What is Load Balancing in
Middleware?
7. Load Balancing Clusters
A cluster is a set of nodes that communicate with each
other and work towards a common goal.
7
8. Membership Schemes
Static Dynamic
Configuration Modes
● Only a predefined set of
nodes could exist in a
cluster.
● New nodes cannot be
added at runtime.
● Membership is not
predefined.
● Nodes could discover the
load balancer.
● Nodes could join a cluster by
specifying a cluster name.
Hybrid
● Also called Well-Known
Addressed (WKA).
● A cluster could have set
of well known members.
● Nodes could join a
cluster via a well-known
member.
8
9. Most widely used Load Balancing
Algorithms
➔ Round Robin
◆ Passes each new connection request to the next server in line
➔ Weighted Round Robin
◆ The number of connections that each machine receives over time is
proportionate to a ratio weight you define.
➔ Least Connections
◆ Passes a new connection to the server that has the least number of
current connections.
https://devcentral.f5.com/articles/intro-to-load-balancing-for-developers-
ndash-the-algorithms 9
10. Why it is called Elastic?
Load BalancerAutoscaler
+
Monitor the
incoming traffic
and scales request
handling capacity
(no of nodes)
Elastic Load
Balancer=
Distribute the load of
incoming traffic amongst
a set of worker nodes
10
11. What is the Purpose?
The motivation of load balancing is to
➔ Optimize resource usage
◆ Start and stop resources on demand.
➔ Maximize the throughput
◆ Increase the average rate of successful message delivery.
➔ Minimize the response time
◆ Reduce the time it takes to process a message and send a response
back.
http://en.wikipedia.org/wiki/Load_balancer
11
12. Main Features of a Load Balancer
There are three main features:
➔ Failover Handling
◆ Avoid single point of failure by hosting multiple instances of a given
service.
➔ Auto-scaling
◆ Manage number of instances of an application according to the
incoming traffic.
➔ Multi-tenancy
◆ Manage multiple tenants of applications.
12
17. Binary Relay Message Builder
● Synapse uses Axis2 engine for message processing.
● Axis2 uses Message Formatters & Message Builders for
serializing and building incoming messages into SOAP
format.
● Binary Relay is an Axis2 message builder which pass through
all messages without processing them.
Binary Relay
Message Builder 17
19. Load Balance Endpoint Module
● Tenant Aware Load Balance Endpoint
○ Extends Synapse Dynamic Load Balance Endpoint.
○ Utilizes round robin load balance algorithm.
● Topology Syncher
○ Receives service cluster topology information from Cloud Controller
via the Message Broker.
● Health Checker
○ Re-establishes connection to the Message Broker if it drops.
Load Balance
Endpoint Module 19
20. Load Balance Endpoint Module (cont.)
● Cluster Domain Manager Impl
○ Manages cluster sub domains of cartridge instances.
● Group Mgt Agent Builder
○ Manages Axis2 group management agents of cluster sub domains.
● Registry Manager
○ Receives domain mappings of cartridge instances from ADC manager
via the registry.
Load Balance
Endpoint Module 20
21. Session Affinity
● There are two different ways to manage session information.
● Replicate in cluster is a very costly process.
● Therefore ELB manages session information for the
applications.
Session Information
Replicate in Cluster Handled by LB
Load Balance
Endpoint Module
21
22. Auto-scaling Module
● Autoscale In Mediator
○ Generates a token (request id) per request received and adds it to a
queue.
● Autoscale Out Mediator
○ Removes the token added by the in mediator when a response is
received from the end point.
● Service Requests InFlight Autoscaler (Task)
○ Performs sanity checks to ensure that all clusters meet the minimum
number of nodes.
○ Performs scaling based on the request load & scaling configuration
parameters.
Auto-scaling Module
22
23. ELB’s role in Apache Stratos PaaS
How does it contribute?
23
26. 1. [Client -> ELB] Send request message
2. [ELB] Identify cluster & tenant using message header
3. [ELB] Add request to a list
4. [ELB -> Node] If session exists, send message
5. [ELB] If not store session information
6. [ELB -> Node] Apply algorithm & send message
7. [ELB -> Node] Handle failover
8. [Node -> ELB] Send response
9. [ELB -> Client] Send response and remove request from list
10. [ELB] Scale number of cartridge instances
Load Balancing Workflow
26
27. Load Balancer Configuration
loadbalancer.conf
loadbalancer {
# minimum number of load balancer instances
instances 1;
# whether autoscaling should be enabled or not.
enable_autoscaler true;
# autoscaling decision making task
#autoscaler_task org.wso2.carbon.mediator.autoscale.lbautoscale.task.
ServiceRequestsInFlightAutoscaler;
#please use this whenever url-mapping is used through LB.
#size_of_cache 100;
...
27
28. Load Balancer Configuration (cont.)
loadbalancer {
...
# Endpoint reference of the Autoscaler Service. This should be present,
if you disabled embedded autoscaling.
#autoscaler_service_epr https://host_address:
https_port/services/AutoscalerService/;
# interval between two task executions in milliseconds
autoscaler_task_interval 60000;
# after an instance booted up, task will wait maximum till this much of
time and let the server started up
server_startup_delay 180000; #default will be 60000ms
# session timeout
session_timeout 90000;
# enable failover
fail_over true;
}
28
29. Port Mapping
● Ports of applications deployed in cartridge instances are
mapped to external ports by the load balancer.
● Port mapping is defined in <cartridge>.xml file.
● Example:
<portMapping>
<http port="80" proxyPort="8280"/>
<https port="443" proxyPort="8243"/>
</portMapping>
29
31. Auto-scaling Configuration
loadbalancer.conf
services {
# default parameter values to be used in all services
defaults {
# minimum number of service instances required
min_app_instances 1;
# maximum number of service instances that will be load balanced
max_app_instances 3;
# maximum number of requests served per second by a service instance
max_requests_per_second 5;
# scale up early using AUR, 0 < AUR <= 1 and default is 0.7
alarming_upper_rate 0.7;
# scale down slowly using ALR, 0 < ALR <= 1 and default is 0.2
alarming_lower_rate 0.2;
# scale down slowly using SDF, 0 < SDF <= 1 and default is 0.25
scale_down_factor 0.25;
# no of iterations in-flight avg is calculated to take the decision
rounds_to_average 2;
message_expiry_time 60000;
}
31
33. Custom Auto-scaling Implementation
● You could write your own Java implementation which
implements org.apache.synapse.task.Task and org.apache.
synapse.ManagedLifecycle interfaces.
● Wrap the implementation class to an OSGi bundle and deploy
it in the ELB OSGi container.
● Update autoscaler_task value in loadbalancer.conf.
33
34. How to avoid Single Point of
Failure of ELB
An ELB is prone to single point of failure
34