You can't (yet) bend the law of Physics, but you can use the power of the cloud to design applications that run as fast as the speed of light! This session will focus on the best practices for optimizing performance to the very last millisecond. We'll dive into topics such as caching at every layer of your application, TCP optimizations, SSL optimizations, latency based routing, and much more. These best practices can help you to streamline your infrastructure utilization, improve performance and allow you to scale economically.

Microservices are becoming more mainstream and bring with them a host of benefits and challenges. In this session, Netflix highlights the pros and cons of building software applications as suites of independently deployable services, as well as practical approaches for overcoming challenges. You get a firsthand look at the robust interprocess communications (IPC) framework that Netflix built and how they address the varying capacities, network usage patterns, and performance characteristics of the hundreds of microservices (e.g., Eureka, Karyon, Ribbon, RxNetty) in their cloud ecosystem.

Complex distributed systems fail. They fail more frequently, and in different ways, as they scale and evolve over time. In this session, you learn how Netflix embraces failure to provide high service availability. Netflix discusses their motivations for inducing failure in production, the mechanics of how Netflix does this, and the lessons they learned along the way. Come hear about the Failure Injection Testing (FIT) framework and suite of tools that Netflix created and currently uses to induce controlled system failures in an effort to help discover vulnerabilities, resolve them, and improve the resiliency of their cloud environment.

Netflix tunes Amazon EC2 instances for maximum performance. In this session, you learn how Netflix configures the fastest possible EC2 instances, while reducing latency outliers. This session explores the various Xen modes (e.g., HVM, PV, etc.) and how they are optimized for different workloads. Hear how Netflix chooses Linux kernel versions based on desired performance characteristics and receive a firsthand look at how they set kernel tunables, including hugepages. You also hear about Netflix's use of SR-IOV to enable enhanced networking and their approach to observability, which can exonerate EC2 issues and direct attention back to application performance.

Auto Scaling groups used in conjunction with auto-scaling policies define when to scale out or scale in instances. These policies define actionable states based on a defined event and time frame (e.g., add instance when CPU utilization is greater than 90% for 5 consecutive minutes). In this session, Electronic Arts (EA) discusses a pro-active approach to scaling. You learn how to analyze past resource usage to help pre-emptively determine when to add or remove instances for a given launch configuration. Past data is retrieved via Amazon CloudWatch APIs, and the application of supervised machine learning models and time series smoothing is discussed.

In this session, learn how Dropbox scales to provide one of the largest cloud storage and file sharing services in the world. Hear how Dropbox leverages Amazon EC2 to run varied workloads including thumbnail generation and document prevent, as well as document indexing to support full-text search. Dropbox presents ''Livefill'' - a generic framework built on top of Amazon SQS. Livefill enables them to trigger customizable data-processing workloads on data stored in Amazon S3 and helps them support more than 200,000 workload requests per second, spread across thousands of machines.

This expert level session covers best practices and tips on how to reduce latency to the absolute minimum when dealing with high volume, high speed datasets, using Amazon DynamoDB. We take a deep dive into the design patterns and access patterns geared to provide low latency at very high throughput. We cover some ways in which customers have achieved low latencies and have a customer speak about their experience of using DynamoDB at scale.

This session drills deep into the Amazon S3 technical best practices that help you maximize storage performance for your use case. We provide real-world examples and discuss the impact of object naming conventions and parallelism on Amazon S3 performance, and describe the best practices for multipart uploads and byte-range downloads.

Amazon Elastic MapReduce is one of the largest Hadoop operators in the world. Since its launch five years ago, AWS customers have launched more than 5.5 million Hadoop clusters. In this talk, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters and other Amazon EMR architectural patterns. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost efficient.

Peek behind the scenes to learn about Amazon ElastiCache's design and architecture. See common design patterns of our Memcached and Redis offerings and how customers have used them for in-memory operations and achieved improved latency and throughput for applications. During this session, we review best practices, design patterns, and anti-patterns related to Amazon ElastiCache.