Emad Benjamin

Chief Technologist, Application Platforms, VMware

Emad Benjamin

Emad has spent the past 25 years in various software engineering positions involving software development of application platforms and distributed systems for various industries such as finance, health, IT, and heavy industry – in various international locations. Emad is currently the Sr. Director and Chief Technologist of Application Platforms with Office of the CTO at VMware, focusing on building hybrid cloud distributed runtimes that are application aware.

Presentations

Virtualizing and Tuning Large Scale Java Platforms

Tuesday, 1:30 PM EST

In this 2 part session (each part 1.5 hours), I will deep dive into the deployment architectures of large scale Java platforms. I will first set the context of the discussion around what problems exist in our industry before I proceed to lay out the concept of platform engineering and its renaissance among the developer/deployment community today. It is astonishing to see that majority of new application platforms being rolled out today miss their SLA, and 90% of these systems require 2X more hardware than what they actually need in order to run. This is an industry suffering from a double whammy, where you spend 2X on hardware and still miss your SLA; clearly something is completely broken. Now prior to delving into these new concepts, such as Microservices, Cloud Native, 3rd Platform, & 12Factor App, it is imperative to first understand the problem at hand before we apply these deployment architectural patterns. I will layout the definition of Platform Engineering as the software engineering science needed in order to understand how to precisely deploy application components onto application runtimes and how in-turn one should appropriately map the application runtimes onto the infrastructure that it needs. With this knowledge you should be able to more effectively decide when to scale-out (by how many instances), and when to scale-up.

We will conclude with covering various GC tuning techniques, and how to best build platform engineered systems; in particular, the focus will be on tuning large scale JVM deployments and various sizing techniques. While predominantly most enterprise class Java workloads can fit into a scaled-out set of JVM instances of less than 4GB JVM heap, there are workloads in the in memory database space that require fairly large JVMs. We will look at various Java platform scales, some holding a few large JVMs of 90GB heap space – servicing large multi-terabyte in memory DBs, while other platforms are of thousands of JVM instances of less than 4GB heap space on each, typical of web-app deployments. We will also take a close look at an example XYZCars.com, where a microservices approach was designed and deployed. The discussion will cover how to more correctly deploy microservices, without causing fragmentation of scale, and hence without impeding performance. In this session, we take a deep dive into the issues and the optimal tuning configurations for tuning large JVMs in the range of 4GB to 360GB, using the GC tuning recipes that were gained over the past 15 years of GC tuning engagements. You should be able to walk away with the ability to commence a decent GC tuning exercise on your own, and your way to platform engineer your application runtimes to feasibly meet your SLAs.

Virtualizing and Tuning Large Scale Java Platforms

Tuesday, 3:15 PM EST

In this 2 part session (each part 1.5 hours), I will deep dive into the deployment architectures of large scale Java platforms. I will first set the context of the discussion around what problems exist in our industry before I proceed to lay out the concept of platform engineering and its renaissance among the developer/deployment community today. It is astonishing to see that majority of new application platforms being rolled out today miss their SLA, and 90% of these systems require 2X more hardware than what they actually need in order to run. This is an industry suffering from a double whammy, where you spend 2X on hardware and still miss your SLA; clearly something is completely broken. Now prior to delving into these new concepts, such as Microservices, Cloud Native, 3rd Platform, & 12Factor App, it is imperative to first understand the problem at hand before we apply these deployment architectural patterns. I will layout the definition of Platform Engineering as the software engineering science needed in order to understand how to precisely deploy application components onto application runtimes and how in-turn one should appropriately map the application runtimes onto the infrastructure that it needs. With this knowledge you should be able to more effectively decide when to scale-out (by how many instances), and when to scale-up.

We will conclude with covering various GC tuning techniques, and how to best build platform engineered systems; in particular, the focus will be on tuning large scale JVM deployments and various sizing techniques. While predominantly most enterprise class Java workloads can fit into a scaled-out set of JVM instances of less than 4GB JVM heap, there are workloads in the in memory database space that require fairly large JVMs. We will look at various Java platform scales, some holding a few large JVMs of 90GB heap space – servicing large multi-terabyte in memory DBs, while other platforms are of thousands of JVM instances of less than 4GB heap space on each, typical of web-app deployments. We will also take a close look at an example XYZCars.com, where a microservices approach was designed and deployed. The discussion will cover how to more correctly deploy microservices, without causing fragmentation of scale, and hence without impeding performance. In this session, we take a deep dive into the issues and the optimal tuning configurations for tuning large JVMs in the range of 4GB to 360GB, using the GC tuning recipes that were gained over the past 15 years of GC tuning engagements. You should be able to walk away with the ability to commence a decent GC tuning exercise on your own, and your way to platform engineer your application runtimes to feasibly meet your SLAs.

Books

Virtualizing and Tuning Large Scale Java Platforms (VMware Press Technology)

by Emad Benjamin

Virtualizing and Tuning Large Scale Java Platforms (VMware Press Technology)

  • Virtualizing and Tuning Large-Scale Java Platforms

     

    Technical best practices and real-world tips for optimizing enterprise Java applications on VMware vSphere®

     

    Enterprises no longer ask, “Can Java be virtualized”? Today, they ask, “Just how large can we scale virtualized Java application platforms, and just how efficiently can we tune them?” Now, the leading expert on Java virtualization answers these questions, offering detailed technical information you can apply in any production or QA/test environment.

     

    Emad Benjamin has spent nine years virtualizing VMware’s own enterprise Java applications and working with nearly 300 leading VMware customers on projects of all types and sizes—from 100 JVMs to 10,000+, with heaps from 1GB to 360GB, and including massive big-data applications built on clustered JVMs. Reflecting all this experience, he shows you how to successfully size and tune any Java workload.

     

    This reference and performance “cookbook” identifies high-value optimization opportunities that apply to physical environments, virtual environments, or both. You learn how to rationalize and scale existing Java infrastructure, modernize architecture for new applications, and systematically benchmark and improve every aspect of virtualized Java performance. Throughout, Benjamin offers real performance studies, specific advice, and “from-the-trenches” insights into monitoring and troubleshooting.

     

    Coverage includes

    --Performance issues associated with large-scale Java platforms, including consolidation, elasticity, and flexibility

    --Technical considerations arising from theoretical and practical limits of Java platforms

    --Building horizontal in-memory databases with VMware vFabric SQLFire to improve scalability and response times

    --Tuning large-scale Java using throughput/parallel GC and Concurrent Mark and Sweep (CMS) techniques

    --Designing and sizing a new virtualized Java environment

    --Designing and sizing new large-scale Java platforms when migrating from physical to virtualized deployments

    --Designing and sizing large-scale Java platforms for latency-sensitive in-memory databases

    --Real-world performance studies: SQLFire vs. RDBMS, Spring-based Java web apps, vFabric SpringTrader, application tiers, data tiers, and more

    --Performance differences between ESXi3, 4.1, and 5

    --Best-practice considerations for each type of workload: architecture, performance, design, sizing, and high availability

    --Identifying bottlenecks in the load balancer, web server, Java application server, or DB Server tiers

    --Advanced vSphere Java performance troubleshooting with esxtop

    --Performance FAQs: answers to specific questions enterprise customers have asked

     

     


Enterprise Java Applications Architecture on VMware

by Emad Benjamin

Enterprise Java Applications Architecture on VMware
  • This book is the culmination of 7 years of experience in running Java on VMware vSphere both internally at VMware and at VMware customer sites. In fact many of VMware’s customers run critical enterprise Java applications on VMware vSphere where they have achieved better TCO, and SLAs. This book covers high level architecture and implementation details, such as design and sizing, high availability designs, automation of deployments, best practices, tuning, and troubleshooting techniques.