Moving serverless compute closer to users can change where energy is consumed, how often compute runs, and how much network traffic crosses grids with different electricity emissions. These effects can pull emissions in opposite directions. Practical teams need a reproducible experiment rather than assumptions. This article gives a step by step research protocol and analysis framework you can apply to a real workload so your migration decision is based on measured change in CO2 per user interaction.<\/p>\n
Locality reduces network distance and origin compute. Caching at the edge can avoid repeated backend work. Higher concurrency and warm function reuse reduce waste per request. Countervailing forces include duplication of state across many edge points, lower resource consolidation at the edge, increased idle capacity, and the overhead of more frequent cold starts. The net effect depends on workload characteristics, user distribution, cacheability and the electricity carbon intensity of the locations involved.<\/p>\n
Start with a clear hypothesis you can falsify. Example hypotheses that are simple to test include whether moving a particular API endpoint to edge locations reduces CO2 per request for a defined user segment, and whether the same migration raises CO2 at peak traffic for a different segment. Restrict scope to one workload variant at a time. Document traffic profile, percent of requests that are cacheable, and performance constraints that cannot be relaxed.<\/p>\n
You must collect both infrastructure telemetry and workload level measurements. At a minimum include the following metrics for each deployment variant and region you test.<\/p>\n
Compute time and CPU usage let you estimate energy used for processing. Network bytes are necessary because energy and emissions can shift from data center compute to networks with different carbon intensity. Cold starts inflate duration and sometimes require additional execution steps. Cache efficiency shows how much origin work is avoided. Provider reported energy or emissions, when available, are the most direct evidence and should be used to validate your model.<\/p>\n
Run a controlled experiment that compares the baseline central region deployment and the candidate edge deployment. Keep the client workload identical for both arms and run each variant long enough to capture steady state as well as transient behavior such as warmup and peak load. The following ordered steps form a repeatable protocol you can adapt.<\/p>\n
Keep calculations transparent and auditable. One defensible approach combines measured compute time with energy intensity estimates and regional electricity carbon intensity. The calculation outline below is a minimal viable model you can refine.<\/p>\n
Step one is to estimate energy used for compute. Where provider energy reporting exists use it directly. Otherwise estimate energy from CPU time and an average power draw for the provisioned resources. Multiply compute energy by a data center overhead factor to represent PUE. Step two is to estimate energy due to network. For each regional path multiply bytes transferred by an energy per byte figure drawn from published literature or industry reports and attribute grid intensity to the regions traversed as appropriate. Step three is to multiply the total energy by the contemporaneous grid carbon intensity for the region where that energy was consumed.<\/p>\n
Document assumptions clearly and provide sensitivity analysis. For example test how results change if PUE is plus or minus an uncertainty range and if the energy per byte for network traffic uses different literature values. If provider emissions reporting is available, compare model results to provider numbers and reconcile differences.<\/p>\n
Translate the experiment output into a simple decision rule. A useful metric is delta CO2 per 1000 requests or delta CO2 per active user per day. Present both absolute and relative changes and show the contribution breakdown for compute, network and infrastructure overhead. If moving to the edge reduces CO2 per request by a meaningful margin for the primary user segment without increasing other risk metrics such as cost or latency beyond tolerances, migration is worth considering. If the edge reduces CO2 only under specific traffic patterns, consider routing those user segments to the edge while keeping other traffic in the central region.<\/p>\n
Be careful about results that look positive on average but hide increases in particular regions or at peak times. For example an edge deployment can reduce global average emissions but raise CO2 in a low carbon region by shifting work into higher carbon grids. Also check that lower emissions do not depend on artificially long cache TTLs that break correctness or on a warmers strategy that wastes energy in low traffic periods.<\/p>\n
Based on measured drivers you will usually be able to implement controls to nudge results toward lower emissions. Region aware routing that sends cacheable GETs to the nearest edge while sending non cacheable writes to the origin reduces unnecessary duplication. Tune function memory and CPU so the compute time per request is minimized while avoiding excessive memory waste. Prefer shared runtime options where available so cold starts and duplicated runtime overhead are lower. Use cache transparency so you can measure and improve hit rates. Finally use provider carbon reporting where available as a governance input but do not treat it as a substitute for your independent measurements.<\/p>\n
Publish the experiment plan, raw telemetry snapshots, calculation notebook and model assumptions to a shared repository. Include a simple reproducible script that computes CO2 per request from raw logs so others can validate changes. Use the same reporting format for future experiments to track regressions. If the organisation sets emissions targets for cloud operations, incorporate the per request metrics into regular runbooks and release checks so changes to deployment topology cannot regress emissions silently.<\/p>\n
If provider telemetry or regional grid data is not available you can still run comparative experiments that focus on proxies such as total execution time, network bytes and cache hit ratio. Those proxies often predict CO2 directionality and can be used for interim decisions, but mark them as provisional and plan to remeasure when better data is available.<\/p>\n
Pick one representative endpoint that matters for user experience or traffic volume and run the experiment protocol for a two week window. Keep the experiment lightweight but documented. Use the findings to create a small set of deployment rules that route only the traffic segments that show measured CO2 improvement to the edge. Where results are mixed introduce traffic policies that combine performance and emissions objectives rather than one off wholesale migrations.<\/p>\n","protected":false},"excerpt":{"rendered":"
A reproducible experimental framework engineers can use to measure the carbon impact of migrating serverless functions from central cloud regions to edge locations. Learn the metrics to collect, the calculations to run, and the decisions to make so outcomes are verifiable and repeatable.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","footnotes":""},"categories":[60,29,4],"tags":[],"class_list":["post-784","post","type-post","status-publish","format-standard","hentry","category-architecture","category-measurement","category-sustainability"],"aioseo_notices":[],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false},"uagb_author_info":{"display_name":"Webcarbon Team","author_link":"https:\/\/webcarbon.io\/news\/author\/webcarbon_wqpz61\/"},"uagb_comment_info":0,"uagb_excerpt":"A reproducible experimental framework engineers can use to measure the carbon impact of migrating serverless functions from central cloud regions to edge locations. Learn the metrics to collect, the calculations to run, and the decisions to make so outcomes are verifiable and repeatable.","_links":{"self":[{"href":"https:\/\/webcarbon.io\/news\/wp-json\/wp\/v2\/posts\/784","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/webcarbon.io\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/webcarbon.io\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/webcarbon.io\/news\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/webcarbon.io\/news\/wp-json\/wp\/v2\/comments?post=784"}],"version-history":[{"count":1,"href":"https:\/\/webcarbon.io\/news\/wp-json\/wp\/v2\/posts\/784\/revisions"}],"predecessor-version":[{"id":785,"href":"https:\/\/webcarbon.io\/news\/wp-json\/wp\/v2\/posts\/784\/revisions\/785"}],"wp:attachment":[{"href":"https:\/\/webcarbon.io\/news\/wp-json\/wp\/v2\/media?parent=784"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/webcarbon.io\/news\/wp-json\/wp\/v2\/categories?post=784"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/webcarbon.io\/news\/wp-json\/wp\/v2\/tags?post=784"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}