Serverless computing allows users to create short, stateless functions and invoke hundreds of them concurrently to tackle massively parallel workloads. We observe that even though most of the footprint of a serverless function is fixed across its invocations --- language runtime, libraries, and other application state --- today's serverless platforms do not exploit this redundancy. Such an inefficiency has cascading negative impacts: longer startup times, lower throughput, higher latency, and higher cost. To mitigate these problems, we have built Photons, a framework leveraging workload parallelism to co-locate multiple instances of the same function within the same runtime. Concurrent invocations can then share the runtime and application state transparently, without compromising execution safety. Photons reduce function's memory consumption by 25% to 98% per invocation, with no performance degradation compared to today's serverless platforms. We also show that our approach can reduce the overall memory utilization by 30%, and the total number of cold starts by 52%.

The difficulty of building large data centers in dense metro areas is pushing big cloud providers towards a different approach to scaling: multiple smaller data centers within tens of kilometers of each other, comprising a "region". We show that networking this small number of nearby sites with each other is a surprisingly challenging and multi-faceted problem. We draw out the operational goals and constraints of such networks, and highlight the design trade-offs involved using data from Microsoft Azure's regions.

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