As cloud applications shift from monoliths to loosely coupled microservices, application developers must decide how many compute resources (e.g., number of replicated containers) to assign to each microservice within an application. This decision affects both (1) the dollar cost to the application developer and (2) the end-to-end latency perceived by the application user. Today, individual microservices are autoscaled independently by adding VMs whenever per-microservice CPU or memory utilization crosses a configurable threshold. However, an application user's end-to-end latency consists of time spent on multiple microservices and each microservice might need a different number of VMs to achieve an overall end-to-end latency.

Financial exchanges have begun a move from on-premise and custom-engineered datacenters to the public cloud, accelerated by a rush of new investors, the rise of remote work, cost savings from the cloud, and the desire for more resilient infrastructure. While the promise of the cloud is enticing, the cloud's varying network latencies can lead to market unfairness: orders can be processed out of sequence, and market data can be disseminated to market participants at incorrect times due to varying latencies between participants and the exchange. We present CloudEx, a fair-access cloud exchange, which leverages high-precision software clock synchronization to compensate for noisy network conditions in the public cloud. We also discuss refinements to the CloudEx design that were informed by lessons learned from deploying CloudEx in two academic courses and conclude by outlining future research directions.