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Improve grammar, fix typos and clean up code
This commit is contained in:
Rendani Gangazhe
committed by
Dannon Baker
Dannon Baker
parent
8a0a662ced
commit
15d926f982
@@ -16,7 +16,7 @@ const testServerInstance = {
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};
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describe("CarbonEmissions/CarbonEmissions.vue", () => {
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it("correctly calculated carbon emissions.", async () => {
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it("correctly calculates carbon emissions.", async () => {
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const wrapper = mount(CarbonEmissions, {
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propsData: {
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estimatedServerInstance: testServerInstance,
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@@ -14,18 +14,16 @@ const dictionary = [
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},
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{
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term: "Carbon Intensity",
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definition:
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"A measure of how much greenhouse gases are emitted when producing per unit of electricity produced.",
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definition: "The amount greenhouse gases emitted per unit of electricity produced.",
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},
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{
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term: "AWS EC2",
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definition:
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"An Amazon service which allows users to rent virtual computers upon which users can deploy their own web services.",
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definition: "An Amazon service allowing users to rent virtual computers and deploy their own web services.",
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},
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{
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term: "CO2e",
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definition:
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"CO2e represents other types of greenhouse gases the have similar global warming potential as a metric unit amount of CO2 itself.",
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"Other types of greenhouse gases that have similar global warming potential as a metric unit amount of CO2 itself.",
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},
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];
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@@ -62,23 +60,21 @@ const greenAlgorithmsUrl = "https://www.green-algorithms.org/";
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to it (in MB). Additionally, we require information about the CPU model of the server that ran the job.
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In particular, we consider the CPU
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<Abbreviation :explanation="'Thermal Design Power'">TDP</Abbreviation>, its core count and the number of
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those cores allocated to the job. For the CPU, we assume a real usage factor of 1.0, meaning that it is
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assumed that 100% percent of each allocated core's resources are used. Given that CPU specifications can
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vary greatly and that we cannot always assume this information is provided we two approaches:
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those cores allocated to the job. We assume a real core usage factor of 1.0, meaning that that 100% of
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each allocated core's resources are used. Given that CPU specifications can vary greatly and that we
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cannot always assume this information is provided, we have two approaches:
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</p>
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<p>
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1. In the case that we know the CPU specifications from the server, we proceed as normal.
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1. In the case that the server's CPU specifications are known, we proceed as normal.
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<br />
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2. When the CPU information isn't provided, we estimate a server configuration given the job's metrics
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and match this to comparable general purpose
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2. When no information is provided, we estimate the server's configuration by matching your job's CPU
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and/or memory usage to a comparable general purpose
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<Abbreviation :explanation="'Amazon Web Services Elastic Compute Cloud'">AWS EC2</Abbreviation>
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instance that could have run your job. We interpolate the memory and core count allocated to the job,
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using step linear interpolation, to map the job metrics to an EC2 instance. EC2 was chosen as it is a
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common service which provides numerous server configuration types, so we can cover many real-world
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server configurations.
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instance. EC2 was chosen because its service provides numerous server configurations allowing us to
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cover more real-world situations.
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<ExternalLink :href="'https://aws.amazon.com/ec2/instance-types/'">
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(Read here to find further information about general purpose EC2).
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(Click here to read further about general purpose EC2 machines).
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</ExternalLink>
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</p>
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@@ -86,7 +82,7 @@ const greenAlgorithmsUrl = "https://www.green-algorithms.org/";
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Once we have the information needed, we calculate the power usage of the CPU and memory in watts. For
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each component, the respective power usage is the product of the amount of allocated resources, a
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<Abbreviation :explanation="'Power Usage Effectiveness'">PUE</Abbreviation> value and a power usage
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factor. For CPUs power usage factor is the (TDP normalized to TDP-per-core) and for memory we use an
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factor. For CPUs power usage factor is the TDP (normalized to TDP-per-core) and for memory we use an
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average memory power draw constant of 0.375 W/GiB.
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</p>
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@@ -96,11 +92,13 @@ const greenAlgorithmsUrl = "https://www.green-algorithms.org/";
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normalized_tdp_per_core = tdp_per_core * cores_allocated_to_job
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power_needed_cpu = pue * normalized_tdp_per_core
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power_needed_memory = pue * memory_allocated_in_gibibyte * memory_power_used
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power_needed_memory = pue * memory_allocated_in_gibibyte * memory_power_usage_constant
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total_power_needed = power_needed_cpu + power_needed_memory
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</pre>
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<p>The power usage is then converted into energy usage (in kWh) by factoring in job runtime (in hours):</p>
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<p>
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The power usage is then converted into energy usage (in kWh) by factoring in the job runtime (in hours):
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</p>
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<pre>
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energy_needed_cpu = runtime * power_needed_cpu / 1000
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@@ -109,8 +107,8 @@ const greenAlgorithmsUrl = "https://www.green-algorithms.org/";
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</pre>
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<p>
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Finally we convert the energy usage into estimated carbon emissions (metric unit CO2e) by multiplying
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the carbon intensity of the server location.
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Finally, we convert the energy usage into estimated carbon emissions (in metric units CO2e) by
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multiplying the carbon intensity of the server location.
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</p>
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<pre>
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@@ -4,13 +4,6 @@ import { getLocalVue } from "tests/jest/helpers";
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import AwsEstimate from "./AwsEstimate";
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import { mount } from "@vue/test-utils";
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// Ignore all axios calls, data is mocked locally -- just say "OKAY!"
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jest.mock("axios", () => ({
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get: async () => {
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return { response: { status: 200 } };
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},
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}));
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const localVue = getLocalVue();
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describe("JobMetrics/AwsEstimate.vue", () => {
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@@ -43,7 +36,6 @@ describe("JobMetrics/AwsEstimate.vue", () => {
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},
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});
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// Wait for axios and rendering.
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await flushPromises();
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if (wrapper.find("#aws-estimate").exists()) {
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