Sharding is widely used to scale an application. Despite a decade of effort to build generic sharding frameworks that can be reused across different applications, the extent of their success remains unclear. We attempt to answer a fundamental question: what barriers prevent a sharding framework from getting adopted by the majority of sharded applications?

Facebook's web site and mobile apps are very dynamic. Every day, they undergo thousands of online configuration changes, and execute trillions of configuration checks to personalize the product features experienced by hundreds of million of daily active users. For example, configuration changes help manage the rollouts of new product features, perform A/B testing experiments on mobile devices to identify the best echo-canceling parameters for VoIP, rebalance the load across global regions, and deploy the latest machine learning models to improve News Feed ranking. This paper gives a comprehensive description of the use cases, design, implementation, and usage statistics of a suite of tools that manage Facebook's configuration end-to-end, including the frontend products, backend systems, and mobile apps.

Cloud computing promises great efficiencies by multiplexing resources among disparate customers. For example, Amazon's Elastic Compute Cloud (EC2), Microsoft Azure, Google's Compute Engine, and Rack-space Hosting all offer Infrastructure as a Service (IaaS) solutions that pack multiple customer virtual machines (VMs) onto the same physical server.

Algorithmic solutions to the packet classification problem in network equipment have long been a subject of study in academia and industry and with increases in network speeds they are becoming even more important. Since general purpose processors cannot meet performance and cost requirements, researchers have been assuming that ASICs or FPGAs are necessary for hardware implementation. Industry and academia have been working on SRAM-based platforms specialized for tables used in network equipment, but existing publications only describe the mapping of simpler exact match or prefix match lookups to such platforms. In this paper we adopt a software-hardware co-design approach mapping the EffiCuts algorithm to the PLUG platform. Our work confirms that this solution achieves high throughput (142 million packets per second) and low power (3.1 Watts). It identifies and evaluates changes to the original algorithm and to the platform that can improve throughput and memory utilization.