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The rapid increase in computing demand and corresponding energy consumption have focused attention on computing's impact on the climate and sustainability. Prior work proposes metrics that quantify computing's carbon footprint across several lifecycle phases, including its supply chain, operation, and end-of-life. Industry uses these metrics to optimize the carbon footprint of manufacturing hardware and running computing applications. Unfortunately, prior work on optimizing datacenters' carbon footprint often succumbs to the sunk cost fallacy by considering embodied carbon emissions (a sunk cost) when making operational decisions (i.e., job scheduling and placement), which leads to operational decisions that do not always reduce the total carbon footprint. In this paper, we evaluate carbon-aware job scheduling and placement on a given set of servers for several carbon accounting metrics. Our analysis reveals state-of-the-art carbon accounting metrics that include embodied carbon emissions when making operational decisions can increase the total carbon footprint of executing a set of jobs. We study the factors that affect the added carbon cost of such suboptimal decision-making. We then use a real-world case study from a datacenter to demonstrate how the sunk carbon fallacy manifests itself in practice. Finally, we discuss the implications of our findings in better guiding effective carbon-aware scheduling in on-premise and cloud datacenters. 

Serverless computing is a rapidly growing cloud application model, popularized by Amazon's Lambda platform. Serverless cloud services provide fine-grained provisioning of resources, which scale automatically with user demand. Function-as-a-Service (FaaS) applications follow this serverless model, with the developer providing their application as a set of functions which are executed in response to a user- or system-generated event. Functions are designed to be short-lived and execute inside containers or virtual machines, introducing a range of system-level overheads. This paper studies the architectural implications of this emerging paradigm. Using the commercial-grade Apache OpenWhisk FaaS platform on real servers, this work investigates and identifies the architectural implications of FaaS serverless computing. The workloads, along with the way that FaaS inherently interleaves short functions from many tenants frustrates many of the locality-preserving architectural structures common in modern processors. In particular, we find that: FaaS containerization brings up to 20x slowdown compared to native execution, cold-start can be over 10x a short function's execution time, branch mispredictions per kilo-instruction are 20x higher for short functions, memory bandwidth increases by 6x due to the invocation pattern, and IPC decreases by as much as 35% due to inter-function interference. We open-source FaaSProfiler, the FaaS testing and profiling platform that we developed for this work.