Name: Optimizing application performance and compute cost for AdTech
Start: 2022-07-28T16:15:00Z
End: 2022-07-28T16:15:18.000Z
Location: BrightTALK
Rating: 0

An Intel Company. Granulate's autonomous workload optimization solution continuously optimizes your service’s runtime resource management, enabling immediate application performance improvements and reducing infrastructure costs by up to 45% - with no code changes required.

Salt Security, one of the biggest API security providers in the world, is running thousands of cores in production and is scaling rapidly. They manage vast amounts of rising traffic and loads to their applications, while needing to ensure the highest SLA to protect its customers from security threats. 

Salt’s DevOps team was seeking an automated optimization solution to help them improve the performance of their Spark on Amazon EKS workloads. They turned to Granulate due to its  fully automated solution that requires no code changes. After proving fast time to result, Granulate was deployed across their workloads and was able to reduce CPU utilization by 35% and drop 15% of compute time on their Spark worker jobs.

Salt translated these performance improvements into a 40% compute cost reduction on their EKS workloads.

Join AWS, Salt Security and Granulate to learn about:
- Scale and cost challenges when it comes to Kubernetes
- Amazon EKS under the hood and main benefits 
- Automated performance and cost optimization solutions for K8s environments

Kubernetes Optimization: Salt Security reduces EKS costs by 40% with Granulate

Microservices and containers have been paramount to building highly distributed and scalable applications. Amazon ECS and EKS have been playing a critical role in shaping the adoption of these modern environments. 

However, these complex architectures also present new challenges, especially for DevOps and Engineering teams - in terms of efficient utilization of resources as well as troubleshooting errors.

Join AWS, Epsagon, and Granulate in a discussion on managing compute in containerized environments, understanding these high-scale environments using observability, and creating the right cost and performance optimization strategies.

On the agenda:
-       An overview of EKS/ECS workloads
-       Overcoming containerized environments complexities via Observability
-       Leveraging autonomous, continuous optimization to improve performance and reduce costs for containerized workloads.

Monitoring and optimizing AWS container-based environments (ECS/EKS)

After understanding that moving to the cloud will not necessarily save companies money, especially if they've just used a lift and shift approach, cloud cost management has become an increasingly difficult problem to solve. 

In this session, Corey Quinn, Chief Cloud Economist at The Duckbill Group, Bailey Caldwell, former VP of Solution Engineering at Flexera, and Asaf Ezra, Co-founder, and CEO at Granulate review old-generation solutions and practices that companies are leveraging in their optimization strategies (rightsizing, discount purchasing, etc.) and explain why these methods are not enough and have been saturated. Then, we'll explore the ongoing shift in the market from a reactive to an active optimization approach and deep dive into the progressive and new methods for reducing cloud compute costs you should be exploring and using in your organization, focusing on real-time, autonomous, continuous optimization.

Progressive methods for reducing cloud compute costs

Companies and their tech leaders are constantly striving to deliver a better customer experience and gain a competitive edge in the market. The performance metrics of applications (SLA, latency, and job completion time to name just a few) have a major impact on those goals together with business revenue and growth.

The challenge with performance improvement efforts presents itself in terms of high cost and engineering efforts, distracting teams and companies from their core business: their product.

AWS Graviton2 processor-based instances were created to offer AWS customers better price-performance benefits at minimal effort. Combined with Granulate’s continuous optimization solution, teams unlock even more performance at less cost and without making any changes to their code.

In this live session, Yuval Kenigsbuch - Senior Backend Engineer at SundaySky, Michael Lanthier - Partner Solution Architect at AWS, and Bar Yochai Shaya - Director of Solution Engineering at Granulate, review SundaySky’s optimization journey.

Join us to learn about:
- The considerations and best practices for using and migrating to Graviton 
- Internal methods and processes you can implement to improve performance and reduce infrastructure costs 
- What is autonomous, continuous optimization, and how it actually works
- The optimization whole: leveraging AWS Graviton2 processor-based instances and Granulate

Who should join? Cloud, Technology, Infrastructure, Platform, Architecture, DevOps, SRE, Engineering at a Senior/decision-maker level (Manager and above) interested in performance and cost optimization.

Improve performance at less cost with AWS Graviton and Continuous Optimization

With demand fluctuations of 3-6 million requests per minute, a rapidly growing cloud infrastructure, and a highly dynamic environment on AWS, MoEngage was dedicated to finding a solution that would help it maintain its competitive SLA and reduce its increasing compute costs. 

Join Balaji Rajan - Director of Engineering at MoEngage, Aharon Twizer - Principal Partner Solution Architect at AWS, and Omer Mesika - Solution Engineer at Granulate, as they discuss a real-life use-case of how MoEngage reduced its compute cost by 40% using Granulate's real-time continuous optimization solution. In this session, we'll cover scaling challenges and review MoEngage's optimization journey, including internal process tuning and efficiency initiatives, and available AWS solutions for cost and performance optimization.

How MoEngage Maintains a 99.9% SLA at Scale While Reducing Costs on AWS

More often than not, developers need to investigate performance bottlenecks in their production applications and identify the cause of the issue. To do so, one prominent approach is continuous profiling, the process of collecting application performance data in a production environment and making that data available to developers for deeper analysis. 

Join us to hear firsthand from Or Zeira, Senior Software Engineer at Bigabid, how they started using continuous profiling in their production environment, what performance issues were discovered, and what was the performance outcome.

In this webinar, you’ll learn:
- What production profiling is, and the benefits of using it 
- Introduction to gProfiler: a new profiling tool by Granulate
- How Bigabid investigated performance to map code-level bottlenecks
- Tips and tricks to get started with profiling

* Can't attend? Register anyway and we'll send you the recording!

How Bigabid Eliminated Production Performance Issues Using Profiling

Join Asaf Ezra, Co-founder and CEO at Granulate, Corey Quinn, Chief Cloud Economist at The Duckbill Group, and Bailey Caldwell, former VP Solutions Engineering at Flexera as they discuss the new approaches to cloud cost reduction.

New Approaches to Cloud Cost Reduction

Join Amir Arama, VP Cloud Operations at Perion and Tom Amsterdam, VP Product at Granulate discussing how Perion achieved 52% cost reduction using Granulate. Perion turned to Granulate to achieve better operational efficiency, reduce infrastructure costs and improve application performance. Using Granulate's solution, Perion saw 52% reduced compute cost, 60% reduced CPU utilization and 10% reduced response time, with 0 R&D efforts.

How Perion Cut Compute Cost by 52%

Join Ron Gruner, Granulate’s VP R&D, and Ron Sobol, R&D Team Lead, for an intimate session on how your organization can optimize big data workloads to reduce infrastructure costs by 60% and improve performance using Granulate’s Real-Time continues optimization solution. We will cover the main practices of big data, the building blocks of big data workloads, the challenges that come with scale, and how Granulate’s product enables organizations to tap into unfulfilled performance potential improving metrics, including CPU utilization and job completion time with no R&D efforts.

Optimizing Big Data Workloads

Microservices and containers have been paramount to building highly distributed and scalable applications. Amazon ECS and EKS have been playing a critical role in shaping the adoption of these modern environments. However, these complex architectures also present new challenges, especially for DevOps and Engineering teams - in terms of efficient utilization of resources as well as troubleshooting errors.
Join AWS, Epsagon, and Granulate in a discussion on managing compute in containerized environments, understanding these high-scale environments using observability, and creating the right strategies for cost and performance optimization.

Agenda:
-       An overview of EKS/ECS workloads
-       Overcoming containerized environments complexities via Observability
-       Leveraging real-time continuous optimization to improve performance and achieve automatic cost reduction for containerized workloads.

Understanding AWS Containerized Environments: from Observability to Optimization

When there's a direct correlation between application performance and generating revenue, it's no wonder the AdTech industry is one of the most competitive ones, comprised of strict SLAs and uncompromising performance requirements.

In this session, we review the use case of Perion, a public-traded AdTech company. Perion leverages big data for its products and receives billions of requests to its infrastructure on a daily basis (which also comes with a cost). Being the innovative and data-driven company they are, Perion's dev and engineering teams have already implemented several methods to improve performance and reduce cost: from application modernization (migration from monolith to K8s-run microservices) to using spot and reserved instances. 

But that wasn't enough. Perion reached a point where it exhausted all optimization practices within its workloads running in the cloud as well as its engineering resources. So what do you do next?

Join Amir Arama - VP Cloud Operations at Perion and Tom Amsterdam - VP Product at Granulate as they'll go over:
- The main cost and performance challenges the AdTech industry is facing today
- Common optimization methods and practices in the market
- Progressive solutions to improve performance and reduce cost 
- Granulate's autonomous, continuous workload optimization and how it helped Perion reduce compute costs by 52%

Optimizing application performance and compute cost for AdTech

Cloud Applications

Cloud Cost

Cost Optimization

Application Performance

Cloud Architecture

Microservices

Big Data

Adtech

Performance Management

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Optimizing application performance and compute cost for AdTech

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