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GreenScale: Carbon-Aware Systems for Edge Computing

Young Geun Kim§∗ Udit Guptaδ

Andrew McCrabb‡ Yonglak Son§ Valeria Bertacco‡ David Brooks†δ Carole-Jean Wuδ

§Korea University †Harvard University ‡Univeristy of Michigan δ Meta

ABSTRACT

To improve the environmental implications of the growing de-

mand of computing, future applications need to improve the

carbon-efficiency of computing infrastructures. State-of-the-

art approaches, however, do not consider the intermittent na-

ture of renewable energy. The time and location-based carbon

intensity of energy fueling computing has been ignored when

determining how computation is carried out. This poses a new

challenge — deciding when and where to run applications

across consumer devices at the edge and servers in the cloud.

Such scheduling decisions become more complicated with

the stochastic runtime variance and the amortization of the

rising embodied emissions. This work proposes GreenScale,

a framework to understand the design and optimization space

of carbon-aware scheduling for green applications across the

edge-cloud infrastructure. Based on the quantified carbon

output of the infrastructure components, we demonstrate that

optimizing for carbon, compared to performance and energy

efficiency, yields unique scheduling solutions. Our evaluation

with three representative categories of applications (i.e., AI,

Game, and AR/VR) demonstrate that the carbon emissions

of the applications can be reduced by up to 29.1% with the

GreenScale — with the scale of edge-cloud application users

(in the order of millions), the yearly reduced carbon emis-

sion is 232.7tCO2, on average, which is on par the average

yearly carbon emissions of 55 vehicles. The analysis in this

work further provides a detailed road map for edge-cloud

application developers to build green applications.

1. INTRODUCTION

With a dramatic advancement of information and communi-

cation technology (ICT), a variety of novel applications, such

as artificial intelligence (AI), extended reality (XR), and cryp-

tocurrencies, have been introduced [3, 8, 43, 47, 83]. Despite

the benefits of such applications, ICT has caused significant

energy and environmental overheads worldwide. In 2022,

the carbon emission from ICT accounts for 3% of worldwide

carbon emissions [5, 51], which is on par with that from the

aviation industry. It is even expected to account for up to 8%

of worldwide emissions in the next decade [50]. There is a

pressing need to design sustainable green applications with

minimal carbon.

To design green applications, developers will need to im-

prove the carbon-efficiency of the infrastructure components.

At-scale computing infrastructures have been significantly op-

timized by at-scale computing infrastructure design [9,44,81],

*Correspondence to Young Geun Kim younggeun_kim@korea.ac.kr

Figure 1: The overall carbon emission of the edge-cloud

infrastructure significantly varies with workload charac-

teristics, varying carbon intensity, and runtime variance.

With judicious selections of execution targets for users us-

ing GreenScale, the carbon emission can be significantly

reduced by up to 29.1%.

operational efficiency, such as microprocessor energy effi-

ciency and power usage effectiveness (PUE), optimization

from industry [41, 45, 80, 82] and academia [74, 96, 134].

Further operational energy efficiency improvement is increas-

ingly more challenging [13, 32, 53, 65, 69, 73, 102, 116, 117,

118, 127, 132]. In addition to efficiency optimization, renew-

able energy, such as solar and wind, is increasingly adopted

to reduce computing’s carbon footprint (CF) [2, 54, 106, 114].

Unfortunately, renewable energy generation is intermittent

and is not always available at any single location all the

time [2, 106]. Due to the intermittent availability of renew-

able energy, the carbon intensity of computing across the

computing spectrum of client devices at the edge and at-scale

datacenter infrastructures can vary along with their locations

and the time of use.

A new challenge arises — deciding where to run applica-

tions when, in order to minimize computing’s carbon. Mod-

ern computing infrastructures enable flexible computation

execution via auto-scaling. For example, many personalized

AI and entertainment use cases are powered by a collabora-

tive execution environment composed of smartphones and

the cloud [35,56,94,121]. In addition, virtual and augmented

reality systems can consist of wearable electronics, smart-

phones as the staging device, and the cloud [42, 46, 84, 92].

There are a variety of points where computations can occur.

However, the decision process is challenging for any appli-

cation, since the carbon intensity of each execution target

across the edge-cloud infrastructure can significantly vary

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arXiv:2304.00404v1 [cs.DC] 1 Apr 2023

depending on where and when a computing execution target

is charged or powered by what energy generation sources.

To minimize an application’s carbon, embodied emissions

and runtime variance introduce additional challenges to the

design of green applications. Recent work characterizing

the carbon footprint of modern hardware demonstrates that

embodied carbon emissions (i.e., emissions owed to manu-

facturing processors, memory, and storage) are beginning to

dominate computing’s footprint [6, 21, 48, 50, 51], owing to

high overheads of manufacturing integrated circuits and data

center construction. Although it is not possible for an appli-

cation to directly change the embodied carbon emissions, the

embodied carbon emissions can be amortized by the users

co-sharing the system during the application runtime. There-

fore, to maximize carbon efficiency, applications must take

into account both operational and embodied emissions when

allocating users across the infrastructure components. Fur-

thermore, mobile execution is stochastic by nature [37,38,72].

Carbon-efficiency variability can stem from interference be-

tween and within applications [123] and the stability of net-

work [22]. Unfortunately, state-of-the-art approaches, such

as [13, 32, 53, 54, 65, 66, 68, 69, 73, 88, 102, 114, 116, 117, 118,

127, 130, 132], determine the execution target primarily rely-

ing on the operational characteristics, such as performance

and energy, without considering the aforementioned features

(i.e., location- and time-dependent renewable energy avail-

ability, embodied emissions, and runtime variance), leaving

a significant room for improvement (Fig. 1). This is due to

the absence of a tool to quantify and analyze the carbon emis-

sions of the infrastructure components by taking into account

the aforementioned features, due to various challenges such

as the heterogeneous interface across the system stacks and

multiple organizations of infrastructure components.

To enable insightful guidelines for developers to build

green applications, this paper proposes GreenScale — a car-

bon design space exploration and optimization framework.

GreenScale quantifies the carbon emissions across the edge-

cloud scheduling landscape by taking into account the appli-

cation workload characteristics, location- and time-dependent

renewable energy availability at power grids [25,120], amorti-

zation of embodied emissions [50], and runtime variance [37].

Based on the quantified carbon emissions, GreenScale ex-

plores the design space of carbon-aware scheduling for three

important edge-cloud application categories — AI, Game,

and AR/VR — in different runtime environments.

The core contributions of this work include:

• We build a framework, GreenScale, to model and quan-

tify the design space of carbon-aware green applica-

tions. GreenScale enables computation scaling with

minimal environmental footprint considering the impact

of application workload characteristics, location- and

time-dependent renewable energy availability, amorti-

zation of embodied emissions, and runtime variance

(Section 3).

• By using GreenScale, we explore the design space of

carbon-aware scheduling for representative edge-cloud

application categories. Based on the characterization

results, we demonstrate carbon-aware scheduling is

distinct from conventional performance- and/or energy-

Figure 2: An example of edge-cloud infrastructure

aware scheduling, leaving a significant room for carbon

improvement (Section 5).

• We additionally distill key insights for green applica-

tion development — we provide guidelines for develop-

ers on making carbon-informed decisions for applica-

tion design and system infrastructure parameters, such

as, scheduling methods and resource provisioning (Sec-

tion 5.4) in order to minimize an application’s carbon

impact.

2. EDGE-CLOUD INFRASTRUCTURE

Applications aiming to run on the client devices at the

edge can consider the distributed heterogeneous resources in

the network hierarchy, from the edge of the network to the

multi-hop remote cloud [54, 66, 88, 114, 130]. Fig. 2 shows

an example of the edge-cloud infrastructure.

Edge devices are the computing resources located at the

edge of the network (i.e., Local Area in Fig. 2). Examples of

the edge devices include smartphones, smartwatches, laptops,

appliances, and so forth. Traditionally, edge devices have

been mainly used as user-end sensors, user interfaces, or both,

in many mobile services. Recently, with the advancements

in powerful mobile systems-on-a-chip (SoCs) [52, 58, 118], a

varying amount of computations can be executed locally on

the edge devices [102, 116, 117, 118, 122, 127, 132]. By doing

so, the services can improve their response time and remove

the data transmission overhead [13, 32, 53, 65].

Edge network connects the edge devices in the multi-user

access network with another access network, or the high-

speed core network [49,124,125]. For the mobile edge-cloud

environment, the multi-user access network can be either a

Wi-Fi access point network (i.e., Access Point in Local Area

of Fig. 2) or a wireless cellular network (i.e., Edge Network

in Fig. 2). In a typical cellular network, different scales (e.g.,

macro-, small-, and femto-scale) of base stations (BSs) are

established in each cell, acting as a wireless transceiver [124].

Edge data center refers to the small-scale data center

(DC) co-located within the edge network [36, 100, 101]. As

shown in Fig. 2, edge DC can be located in either 1) urban

area (e.g., in-house small-scale servers operated by the de-

veloper or the application company [18], or edge-scale cloud

2

Figure 3: Design space overview of carbon-aware green applications.

servers rented from the cloud service providers [1,23,115]) or

2) rural area alongside the base station [31]. Since user-end

devices only need to pass through the edge network, edge

DCs can provide relatively powerful computing resources

with tolerable data transmission overhead (from 5 ms to 20

ms depending on the location of edge DC as shown in Fig. 2).

However, due to space and scalability limitations [36,66], the

servers in an edge DC usually have less computing capabili-

ties and efficiency than those in a hyperscale DC [18].

Core network includes the large-capacity core routers

and high-speed fiber optic cables which connect consumers

and businesses to DCs [19, 20, 61, 126]. Packets from edge

networks are provided to a core router, passed from router to

router, and delivered to another edge network which contains

its destination.

Data center refers to the large-scale DC usually operated

by industrial companies [4, 43, 85]. The DCs benefit from

economies of scale for large workloads and co-location (i.e.,

batching) by exploiting a large number of highly efficient co-

processors, such as GPUs and tensor processing units (TPUs).

However, as the user-end devices need to pass through the

edge network as well as the multiple hops of routers in the

core network, DCs usually incur higher data transmission

overheads compared to the edge DCs [54, 66, 88, 114, 130].

3. GREENSCALE

In this section, we propose GreenScale, a carbon design

space exploration and optimization framework to enable

green application development. Fig. 3 shows the overall

design space of carbon-aware edge-cloud scheduling. The op-

timization objective of the design space is to minimize carbon

emissions of an application, where computation kernels can

be flexibly scheduled onto processors across the wide edge-

cloud computing spectrum, satisfying the latency constraint.

To achieve the optimization objective, GreenScale quantifies

the carbon emissions of infrastructure components for the

computation kernels based on the performance and energy

measurements with different application workloads under

runtime variance, hourly energy generation data of all the

US power grids powering the infrastructure components, and

embodied CF modeling tools. Based on the quantified car-

bon emissions, GreenScale exhaustively explores the carbon-

optimal scheduling decisions in different environments.

To quantify the carbon emissions of infrastructure com-

ponents, GreenScale uses four models: 1) execution time

performance model, 2) energy consumption model, 3) oper-

ational carbon footprint (CF) model, and 4) embodied CF

model. Here, the performance and energy models are ob-

tained based on the measurements with different application

workloads under runtime variance. The operational CF model

is calculated based on estimated energy consumption and car-

bon intensity of each infrastructure component [6, 7, 17, 50,

51, 60, 105, 108, 113]. For the embodied CF model, two

embodied CF modeling tools are employed: ACT [50] and

LCA [7, 60, 105, 108, 113]. The details of the CF models are

explained in Section 3.1.

3.1 Carbon Emission Model

As explored by many previous works [6, 7, 17, 50, 51, 60,

105, 108, 113], the carbon emissions across hardware life

cycles can be split into four main phases: hardware manufac-

turing, hardware transport, operational use, and end-of-life

processing and recycling. Among emissions from the phases,

GreenScale considers the operational carbon emissions along

with embodied carbon emissions (i,e., emissions from the

rest of hardware life cycles). Table 1 summarizes the car-

bon emission models of the infrastructure components and

Table 2 describes the abbreviations and notations used in the

models.

Operational Carbon Emission: The operational carbon

emission of each execution target is calculated based on the

operational energy consumption of the components involved

in the execution target and their respective carbon intensities

(CI in Table I) [2, 50, 51, 106].

The operational energy consumption includes three compo-

nents [70,71,129]: 1) computation energy, 2) communication

energy, and 3) idle energy. The computation energy is the

combination of the execution time (Tcomp in Table I) during

which the required computations are performed on the actual

execution target (either Mobile Device, Edge DC, or Hyper-

scale DC), and the power (Pcomp in Table I) consumed during

the execution time. The communication energy is the combi-

nation of the time (Tcomm in Table I) during which the data is

transmitted from the user-end device to the execution target

and the power (Pcomm in Table I) consumed by each com-

ponent during the data transmission time. The idle energy

consumption is the idle energy consumed by non-involved ex-

ecution targets. The idle energy overhead is calculated based

on the idle power (Pidle) consumed during the application

runtime. It is divided by the number of users per components

3

Table 1: Carbon emission models of the infrastructure

Execution Target

Components

Operational CF

Embodied CF

Mobile Device

Mobile Device

Tcomp_M ×Pcomp_M ×CIM

ECFM ×

Tcomp_M

LTM

Edge Network

-

-

Edge DC

Tcomp_M×Pidle_E_DC

Nuser_E

×CIE

ECFE_DC

Nuser_E

×

Tcomp_M

LTE_DC

Core Network

-

-

Hyperscale DC

Tcomp_M×Pidle_H

Nuser_DC

×CIH

ECFH

Nuser_DC

×

Tcomp_M

LTH

Edge DC

Mobile Device

(Tcomm_E ×Pcomm_M

+ Tcomp_E_DC ×Pidle_M)×CIM

ECFM ×

(Tcomm_E +Tcomp_E_DC)

LTM

Edge Network

Tcomm_E ×Pcomp_BS

Nuser_BS

×CIE

ECFBS

Nuser_BS

× Tcomm_E

LTBS

Edge DC

Tcomp_E_DC×Pcomp_E_DC

Nuser_E

×CIE

ECFE_DC

Nuser_B

×

Tcomp_E_DC

LTE_DC

Core Network

-

-

Hyperscale DC

(Tcomm_E +Tcomp_E_DC)×Pidle_H

Nuser_DC

×CIH

ECFH

Nuser_DC

×

(Tcomm_E +Tcomp_E_DC)

LTH

Hyperscale DC

Mobile Device

(Tcomm_E ×Pcomm_M

+ (Tcomm_R +Tcomp_H)×Pidle_M)×CIM

ECFM ×

(Tcomm_E +Tcomm_R+Tcomp_H )

LTM

Edge Network

Tcomm_E ×Pcomp_BS

Nuser_BS

×CIE

ECFBS

Nuser_BS

× Tcomm_E

LTBS

Edge DC

(Tcomm_E +Tcomm_R+Tcomp_H )×Pidle_E_DC

Nuser_E

×CIE

ECFE_DC

Nuser_E

×

(Tcomm_E +Tcomm_R+Tcomp_H )

LTE_DC

Core Network

Tcomm_R×Pcomm_R

Nuser_R

×CIR

ECFR

Nuser_R

× Tcomm_core

LTR

Hyperscale DC

Tcomp_H ×Pcomp_H

NB

×CIH

ECFH

NB

×

Tcomp_H

LTH

(Nuser) assuming that the application developer (or company)

along with users are responsible for the carbon emissions of

the computing components that are used by the application.

Depending on which components are involved in the ex-

ecution target, the operational energy consumption of the

execution target varies. For example, when the mobile device

is considered as the execution target, the computation energy

consumption of mobile device as well as the idle energy over-

head of the data centers are included in the operational energy

consumption. On the other hand, when a data center is con-

sidered as the execution target, the communication energy of

the client device and network components (i.e., Base Station

or Core Routers), the computation energy of the data center,

and the idle energy overhead of the client device are included

in the operational energy.

The carbon intensity of each component depends on which

energy source is used to power it. The carbon intensity of

each energy source is summarized in Table 3 [2, 50, 51, 106].

Typically, the components are powered by the closely located

power grid [2, 106, 120]. As hourly generation of energy

sources in each grid highly depends on its location, the car-

bon intensity of each component is eventually determined

depending on the location and time. To accurately model the

carbon intensity of each component, the hourly energy gener-

ation data of all the US power grids [25, 120] are collected

and used for GreenScale.

Embodied Carbon Emission: The embodied carbon emis-

sion includes the emissions from the hardware life cycles

except for the operational use. The embodied CF of a compo-

nent is calculated based on its total embodied CF (ECF) and

lifecycle time (LT), application runtime (Tcomp or Tcomm), and

the number of users co-sharing the component during the ap-

plication runtime (Nuser or NB), assuming that the embodied

CF consumed during the application runtime (i.e., latency)

can be discounted by the number of users co-sharing the com-

ponent over the lifecycle time of the component. Here, ECF

of each component can be obtained by using the life cycle

analysis tools (LCAs) [7,60,105,108,113] or the architectural

carbon model tool (ACT) [50]. Those models calculate the

embodied CF based on a variety of parameters related with

the hardware life cycles, such as SoC area, energy per area,

gas per area, yield, raw materials, etc.

3.2 Design Space Parameters

Workload characteristics: The carbon optimal execu-

tion target depends on the workload characteristics. This is

because the operational efficiency of the execution targets de-

pends on the computation-communication ratio of the work-

loads. The computation energy and latency are determined

by the amount of required computations of the workload

(e.g., the number of floating point operations of AI work-

load), whereas the communication energy and latency are

determined by the size of transmission data (e.g., input image

or text of AI workload). Here, the energy is directly related

with the operational carbon emission of the infrastructure

components, and the latency is related with their embodied

carbon emission.

The performance constraint is also dependent on the work-

load characteristics. For example, in case of AI workload,

quality of service (QoS) expectations of users can be de-

fined as a certain latency (e.g., 33.3 ms for 30 frames per

second (FPS) video frame rate [24,133], or 50 ms for interac-

tive applications [26, 78]), below which users rarely perceive

the difference. On the other hand, in case of game work-

load, more types of QoS metrics, such as latency, FPS, jitter,

etc. [14, 15, 75], can be considered. Similar QoS metrics can

also be used for AR/VR workloads [131]. Since the QoS is a

crucial metric for mobile optimization, it is also important to

4

Table 2: Abbreviations and Notations

Abbreviation

and Notation

Description

M

Mobile device

E

Edge

DC

Data center

H

Hyperscale DC

BS

Base station

R

Core Router

Tcomp

Computation time

Tcomm

Communication time

Pcomp

Computation power consumption

Pcomm

Communication power consumption

Pidle

Idle power consumption

CI

Carbon intensity

ECF

Total embodied carbon footprint

LT

Lifecycle time

Nuser_DC

Number of users per DC

Nuser_edge

Number of users at the edge system

Nuser_BS

Number of users per edge base station

NB

Number of batched users in DC

meet the workload-dependent performance constraints.

Varying carbon intensity: Another important factor that

affects the carbon optimal execution target is the carbon in-

tensity of energy sources used for the components. As we

present in Table 3, the carbon intensity of renewable energy

(i.e., wind and solar) is much lower than that of the other

energy sources, such as coal. Due to the carbon intensity

gaps, the operational emissions of components significantly

depend on the availability of energy sources, affecting the

carbon optimal execution target. For example, in case of

server-class computing systems whose operational emissions

are larger than that of mobile systems, using renewable en-

ergy may significantly save the global carbon footprint, by

reducing the carbon intensity of operational emissions.

However, the generation of each energy source in grids

highly depends on their location and time [25, 120]. For

example, in case of a grid in California (i.e., CISO in Fig. 4),

the solar energy is only available during the daytime so that

the carbon intensity of the grid is low only during the daytime.

On the other hand, in case of a grid in New York state (i.e.,

NYISO in Fig. 4), the wind energy is available intermittently

so that the carbon intensity of the grid fluctuates during a day.

Due to the time-varying location-dependent energy source

availability, the actual carbon intensity of the components

can also vary. In case of the client devices at the edge, the

carbon intensity can be determined when and how the users

charge the battery of their devices [106]. Fig. 4 shows two

different users’ battery charging models along with the hourly

carbon intensity of the grid in California (i.e., CISO) and that

in New York State (NYISO): 1) nighttime charger who usu-

ally charges the battery during the nighttime and 2) average

charger who charges the battery on demand throughout a

day. As shown in Fig. 4, nighttime chargers may have higher

carbon intensity for their mobile device since they mostly

charges their battery when the carbon intensity is high. The

carbon intensity may also depend on the location of the user.

In case of the data centers, the carbon intensity can be

Table 3: Operational carbon intensity of energy sources

Type

gCO2eq/kW h

Wind

11

Solar

41

Water

24

Oil

650

Natural Gas

490

Coal

820

Nuclear

12

Other (Biofuels etc.) 230

Figure 4: Depending on when users charge their battery,

the carbon intensity of the mobile device can vary; night-

time chargers mostly charge the battery when the carbon

intensity is high (yellow area) whereas average chargers

almost uniformly charge the battery (blue area).

determined by the average carbon intensity of the powering

grid, or the amount of renewable energy the companies pur-

chase with sophisticated accounting frameworks that track

renewable energy credits. Since the carbon optimal execution

target can vary depending on the actual carbon intensity of

the components, it is crucial to consider the time-varying

location-dependent carbon intensity for the green application.

Geographical trade-off: Considering the time-varying

renewable energy availability, it is also possible to consider

other execution targets powered by another grid. For example,

we can consider offloading to farther execution targets in spite

of longer network latency (e.g., edge DC in rural area with

a plenty of available renewable energy instead of mobile

device or closer edge DC in Fig. 2), in order to have lower

carbon intensity. Since the amount of trade-off between the

network latency and carbon intensity may depend on the size

of transmission data, it is important to carefully consider this

geographical trade-off.

Runtime variance: The edge-cloud execution environ-

ment is stochastic by nature [37, 38, 72]. In a realistic execu-

tion environment there can be several co-located workloads

not only on the server-scale data centers but also on the mo-

bile devices — recent mobile devices support multi-tasking

features, such as screen sharing of multiple applications. The

resource interference from the co-located workloads can af-

fect the computing efficiency of each components shifting

the carbon optimal execution target. Network variability

stemming from the varying wireless signal strength in edge

network and congestion in core network can also significantly

affect the communication efficiency. For example, the signal

strength of wireless cellular network can vary considerably

as edge device users move — users undergo significant signal

strength variations in daily life (43% of data is transmitted

under weak signal strength [22]). In addition, queuing delays

in core networks are also dynamic depending on the traffic.

5

Table 4: NN inference workloads

Category

NN

FLOPs Params

Input/

Output

(KB)

MobileNet

0.31G

3.5M

150.5

SqueezeNet

0.82G

1.2M

150.5

Vision

ResNet 50

4.09G

25.6M

150.5

MobileNet-SSD

0.8G

6.8M

270

Inception

5.71G

23.8M

268.2

Text

BERT

25.3G

17.5M

1

4. METHODOLOGY

4.1 Workloads

To understand the carbon design space for green applica-

tion with GreenScale, we run three state-of-the-art workloads

on our edge-cloud infrastructure: artificial intelligence (AI),

game, and augmented/virtual reality (AR/VR). We explore

the AI workloads as they are widely used in many of recent

intelligent services and applications [53, 79, 98, 119]. In ad-

dition, we explore the game applications as they dominate

63.5% of application market in 2022 [109]. We also explore

the AR/VR applications, as they have gained recent traction

in both consumer and research communities [131] thanks to

the advances in efficient computing technologies, high-speed

communication, and specialized hardware platforms.

For AI workloads, we run the inference of state-of-the-art

neural networks (NNs) [53,79,98,119] which are widely used

in mobile intelligent services [35, 42, 46, 56, 84, 92, 94, 121].

Table 4 summarizes the NNs. As shown in Table 4, NNs

have different computing characteristics depending on the

number of floating point operations (FLOPs) and parame-

ter sizes. In addition, they have different input/output sizes

depending on the their categories. Different classes of NNs

also have different performance requirements. For the vision

NNs, we consider 30 FPS as the performance requirement —

users rarely perceive QoS difference as long as FPS exceeds

30 [24, 133]. For the text workload, we use 100 ms as the

performance requirement [98].

For game workloads, we run three different types of games

summarized in Table 5 which are widely used by mobile

users [14, 15, 75]. As shown in Table 5, different types of

games have different performance requirements. For the case

where the execution target is mobile devices, we directly run

the Android applications on the mobile device while measur-

ing latency, FPS, and power consumption of the device. On

the other hand, for the case where the execution target is the

DC, we run the cloud gaming Android application, NVIDIA

Geforce Now [91], on the mobile device while measuring

the latency, FPS, and power consumption of the device. For

the power measurement of the cloud server, we also run the

desktop version of the game on our DC infrastructure.

For AR/VR workloads, we use four workloads from IL-

LIXR [59] summarized in Table 6: VR - 3D World [89],

VR - 3D Material [39], VR - 3D Cartoon [40], and AR [59].

AR/VR workloads have four sub tasks which include 1) Per-

ception which reads inputs from sensors and understands the

current surrounding environment, 2) Visual which combines

Table 5: Game workloads

Category

Name

Input/

Output

(MB)

FPS

Req.

Latency

Req. (ms)

1st-Person

Game

Fortnite

3.2

60

100

3rd-Person

Role Playing

Game

Genshin

Impact

3.0

60

500

Omnipresent

Strategy

Team Fight

Tactics

1.9

60

1,000

Table 6: AR/VR workloads

Category

Name

Tasks

Input/

Output

(KB)

Perf.

Req.

(ms)

3D

World

Sponza

1) Input

2) Perception

3) Visual/

Audio

540.47

97.83

3D

Material

Materials

3D

Cartoon

Platformers

AR

AR Demo

the virtual information with physical world and renders the

final frames, and 3) Audio which calculates and plays the au-

dio. For all the workloads, the inputs are camera and motion

tracking data and the outputs are the computed frames to be

streamed on the client device.

4.2 Edge-Cloud Infrastructure

This study for edge-cloud infrastructures is based on: mo-

bile device, edge DC, DC, edge network, and core network.

For AI and game workloads, we use an off-the-shelf Android

smartphone, Pixel 3 [48], as the mobile device, which is

equipped with a Snapdragon 845 SoC [95]. On the other

hand, for AR/VR workloads, we use an NVIDIA Jetson de-

vice which is equipped with NVIDIA Volta GPU, since the

AR/VR workload does not run on Android devices. The

specifications of the devices are summarized in Table 7. We

measure the power consumption of the devices by using an

external power measurement device, Monsoon power moni-

tor [90].

For edge DC and hyperscale DC, we use the AWS instances

of p3.2xlarge and p4d.24xlarge, respectively, which have

similar specifications to the ones used in MLCommons [87,

100, 101]. For detailed specifications, see Table 8.

For BS of edge network, we use the macro-scale BS whose

TDP is 1000W [49]. We calculate the single-user power

consumption based on the number of users who can be served

by the macro-scale BS [49]. For the core routers, we use the

typical power and bandwidth of off-the-shelf routers [19, 20].

4.3 GreenScale Parameters

Time-varying Location-Dependent Carbon Intensity:

As we explain in Section 3.1, the carbon intensity of the

components depends on when, where, and how they are pow-

6

Table 7: Mobile device specification

Device

CPU

GPU

Accel.

RAM

(GB)

Pixel 3

Cortex A75

2.4GHz

Adreno 630

0.7GHz

Hexagon

685

4

Jetson

AGX

Xavier

NVIDIA

Carmel

2.4GHz

NVIDIA

Volta

1.4GHz

NVDLA

64

ered. To explore the realistic carbon intensity of the compo-

nents, we consider reasonable scenarios along with the hourly

renewable generation reports of the US grids [25, 120].

For mobile devices, we consider three scenarios for user

charging behaviors based on the previous user studies [34,93,

103]: 1) nighttime charger who charges the battery only dur-

ing the nighttime, 2) average charger who uniformly charges

the battery throughout a day, and 3) intelligent charger who

only charges the battery when the renewable energy is avail-

able at the close grid. By exploiting the statistical models

in [34, 93, 103], we calculate the average carbon intensity of

the mobile device in different locations.

For edge data centers and base stations, we consider two

scenarios depending on their locations [49, 124]: 1) urban

area where the amount of renewable energy generation is

relatively small (with short data transmission latency) and

2) rural area where there exist a plenty of renewable energy

sources (with relatively longer data transmission latency). In

case of the core routers, we use the average carbon intensity

of the grids in any state, since the core routers are usually

scattered across the state for delivering packets [19, 20, 64].

For data centers, we consider two scenarios depending on

how they are powered [2, 106]: 1) grid-mix where the data

center is powered by the mixed energy sources generated by

the closest grid and 2) carbon free1 where the data center is

fully covered by the renewable energy.

Runtime Variance: We explore two types of runtime vari-

ance which users widely experience while using the appli-

cations [37, 38, 72]: interference from co-located workload

and network stability. For the interference from co-located

workloads, we measure the computation latency on each com-

ponent, while running other workloads. To model the unsta-

ble edge network, we measure the data transmission latency

under the bad wireless signal strength [70], [72]. Similarly,

we model the unstable core network based on the impact of

variability (e.g., congestion) modeled in [10, 12, 61, 62].

Embodied CF Models: Although rising embodied carbon

emissions have received much attention recently [50, 51], the

deployment of embodied carbon footprint (CF) models is still

in a nascent stage. To explore the impact of embodied CF

models on the scheduling decisions of green applications, we

use two embodied CF modeling tools: ACT and LCA tools.

For the computing components, we first calculate the em-

bodied carbon emissions of the computing components using

1Note NetZero claimed by cloud providers (by annually offsetting

the DC’s energy with renewable energy credits) does not meas

their actual energy is carbon free [2] — DCs continue to consume

carbon-intensive energy when renewable energy supply is insuffi-

cient hourly.

Table 8: Server specification

Type

CPU

Accel.

RAM

p3.2xlarge

Intel Xeon

E5-2686 v4

NVIDIA

Tesla V100

64GB

p4d.24xlarge

Intel Xeon

P-8275CL

NVIDIA

A100 x8

1152GB

ACT [50]. We also obtain their embodied carbon emissions

based on the LCA reports [7, 60, 105, 108, 113] provided by

manufacturing companies. For example, in case of mobile

devices, we use embodied CF presented in the sustainability

report of Google Pixel 3 [48]. On the other hand, in case

of server-class systems, we use embodied CF presented in

the sustainability report of Dell R740 [21]. Note, according

to [51], those two modeling tools have 28% gap in terms of

the estimated embodied carbon emissions.

For the base station and routers, we do not use ACT, as

the networking components, such as transceivers, are not

modeled in ACT. We instead obtain their embodied carbon

emissions based on the LCA reports provided by network

infrastructure companies [19, 20, 27, 28, 29, 30, 31].

5. EVALUATION RESULTS AND ANALYSIS

In this section, we explore the design space of carbon-

aware scheduling for representative edge-cloud applications

by using GreenScale. The design space includes 1) schedul-

ing decisions (i.e., allocation of user requests across edge-

cloud infrastructure) and 2) resource allocations (i.e., ad-

justments of the number of server instances to rent). For

each application category, the size of design space is ∼200K,

which is combination of workload characteristics, varying

carbon intensity, runtime variance, and available scheduling

decisions.

5.1 Result Overview of Carbon Design Space

Fig. 5 shows (a) the performance, (b) energy consumption,

and (c) carbon footprint results for the carbon optimization

design space for a variety of application use cases across the

edge-cloud infrastructure. The x-axis shows the available

execution targets for each workload whereas the y-axis of

(a), (b), and (c) shows the normalized latency, energy, and

carbon footprint of the execution targets, respectively. Here,

the carbon intensity models of mobile device, edge DC, and

hyperscale DC are Nighttime Charger, Urban Area, and Grid-

Mix, respectively. ACT is used as the embodied CF modeling

tool, and there is no runtime variance.

The carbon-optimal execution target is not always the same

as the performance- or energy-optimal execution target. The

performance-, energy-, and carbon-optimal execution target

(green stars of Fig. 5) varies based on the use cases.

In case of the AI workloads, the latency-, energy-, and

carbon-optimal execution targets depend on the computation-

communication ratio of NNs. For example, although Mo-

bileNet, SqueezeNet, and ResNet have the same size of trans-

mission data, the carbon-optimal execution target differs (i.e.,

7

Figure 5: Carbon optimization design space across edge-cloud infrastructure: the performance-, energy-, and carbon-

optimal execution targets vary depending on use cases. Note latency is normalized to the respective latency constraints

of the workloads, whereas the energy and CF are normalized to those of Mobile for each workload — numbers above

(b) and (c) show the energy and CF of Mobile for each workload, respectively.

Mobile, Edge DC, and DC, respectively), due to the different

number of FLOPs. On the other hand, the carbon optimal-

execution targets for MobileNet-SSD and Inception are Edge

DC, as they have the larger data transmission overhead along

with a higher number of FLOPs than MobileNet. In case

of BERT, the latency-, energy-, and carbon-optimal execu-

tion targets are DC, due to the smallest data transmission

overhead.

In case of the game workloads, DC (i.e., cloud gaming ser-

vice) provides better quality of experience (QoE) compared

to Mobile (i.e., Android application) by providing higher FPS

satisfying latency constraint. However, due to the high data

transmission overhead in terms of operational energy, Mobile

always shows better carbon emission compared to DC — DC

needs to keep transmitting the captured frames to Mobile.

In case of the AR/VR workloads, the latency-, energy-

and carbon-optimal execution targets also depend on the

computation-communication ratio of the workloads. In fact,

all the AR/VR workloads have the same size of the trans-

mission data as summarized in Table 6. However, VR - 3D

World requires higher amount of computations compared to

the other workloads. Due to the reason, it does not satisfy

the latency constraint with Mobile, eventually having lower

carbon footprint with DC. On the other hand, the rest of the

workloads exhibit lower carbon footprint with Mobile.

This work on GreenScale demonstrates the potential of an

intelligent carbon-aware computation scheduling. An appli-

cation’s carbon cost can be reduced by 29.1 % by scaling the

scope of computations beyond local, client devices.

The carbon optimization design space becomes even more

complex in the presence of varying carbon intensity of the

components and runtime variance. Due to the complex design

space, the carbon optimization is distinct from the conven-

Figure 6: Carbon optimization is distinct from per-

formance and energy optimization leaving a significant

room for carbon improvement. The x-axis shows de-

sign parameters and the y-axis shows the normalized CF

of carbon-aware scheduling over energy-aware schedul-

ing [72].

tional performance and energy optimizations, leaving a sig-

nificant room for carbon-efficiency improvement — carbon-

aware scheduler (i.e,. a scheduler that allocates user requests

across components explicitly considering the carbon features)

outweighs the state-of-the-art scheduler [72] by up to 29.1%

(rightmost bar in Fig. 6).

5.2 Impact of Varying Carbon Intensity

Carbon intensity of Mobile: Fig. 7 shows the CF of

ResNet with different charging scenarios for Mobile. The

x-axis shows the three execution targets with different battery

charging scenarios (i.e., Nighttime Charger, Average Charger,

and Intelligent Charger in each column) and the y-axis shows

the cumulative CF of each execution target. Note, the carbon

intensity models of edge DC and hyperscale DC are Urban

Area and Grid-Mix, respectively. ACT is used as the embod-

ied CF modeling tool, and there is no runtime variance.

Intelligent carbon-aware battery charging for client devices

8

Figure 7: CF of ResNet with different charging scenar-

ios for Mobile. Note CF of execution targets is normal-

ized to that of Mobile in Nighttime Charger. The car-

bon intensity of Mobile depends on when and how the

mobile device user charges the battery. If the user in-

telligently charges the battery considering the renewable

energy availability at the closest grid (i.e., Intelligent

Charger), he/she can significantly save the carbon foot-

print (by 61.2%).

achieves up to 61.2% carbon reduction. With the scale of

client devices, from smartphones to wearables, in the order

of billions [110], the carbon impact is significant.

In case of the Nighttime Charger, the carbon intensity of

Mobile is usually high since the battery is charged during the

nighttime where less amount of renewable energy is available.

When the charging behavior of the user changes from night-

time charger to intelligent charger, the carbon intensity of

the Mobile significantly decreases, saving the overall carbon

footprint by up to 61.2% (i.e., Mobile bars) — note other

workloads show similar result trends. In this case, the carbon

optimal execution target shifts from DC to Mobile.

Carbon intensity of DC: Fig. 8 shows the latency and CF

of (a) ResNet and (b) MobileNet-SSD for different carbon

intensity scenarios of Edge DC and base station. The sub y-

axis shows the normalized latency. Note the carbon intensity

models of mobile device and hyperscale DC are Nighttime

Charger and Grid-Mix, respectively. ACT is used as the

embodied CF modeling tool, and there is no runtime variance.

When the Edge DC and base station are located in an urban

area, their carbon intensity will be usually high due to less

amount of available renewable energy [120]. Instead, the

edge network latency will be relatively short. On the other

hand, when the Edge DC and base station are located in a

rural area, the carbon intensity will be low due to a plenty of

available renewable energy [120] — the edge network latency

will increase accordingly.

Given this geographical trade-off, the carbon optimal ex-

ecution target depends on the location of the Edge DC and

base station, and workload characteristics. In case of ResNet

which has a relatively large amount of computations with

moderate data transmission overhead, the carbon footprint

gain of a Rural Area outweighs the loss from increased net-

work latency (i.e., Edge DC bars). On the other hand, in case

of MobileNet-SSD which has a larger transmission data, the

latency constraint is not satisfied in the Rural Area due to

the increased network latency. This result implies that the

selection of the infrastructure components (e.g., Edge DC in

Urban Area vs. Edge DC in Rural Area) needs to be based on

the careful considerations of geographical trade-off as well

as the workload characteristics.

Figure 8: Latency and CF of (a) ResNet and (b)

MobileNet-SSD for different carbon intensity scenarios

of Edge DC and base station. Note the latency and CF

are normalized to those of Mobile in Urban Area. The

carbon intensity of Edge DC depends on its location (i.e.,

Urban Area with less amount of renewable energy and

Rural Area with a plenty of renewable energy), but the

network latency also depends on the location.

An application’s carbon cost depends on carbon intensities

of electricity used by infrastructures across the entire com-

puting spectrum — edge, datacenters, and base stations with

networking infrastructures connecting the two.

Fig. 9 shows the CF of (a) MobileNet-SSD and (b) AR for

different carbon intensity scenarios of DC. Note the carbon

intensity models of mobile device and edge DC are Nighttime

Charger and Urban Area, respectively. ACT is used as the

embodied CF modeling tool, and there is no runtime variance.

As shown in Fig. 9, the impact of carbon intensity on the

carbon optimal decision depends on the workloads. In case

of MobileNet-SSD, overall CF does not vary significantly

even when the energy consumed by DCs is fully covered by

renewable energy (i.e., Carbon Free). This is because the

operational CF of DC accounts for only a small portion of

CF due to the high network overhead and idle overhead of

other components. On the other hand, in case of AR, Carbon

Free is still beneficial (i.e., DC bars), due to the large amount

of computations that can benefit from DC.

Uncertainty in carbon intensity: In a realistic environ-

ment, the renewable energy generation can fluctuate even

within an hour due to the environmental factors (e.g., clouds

turbulence, solar radiation, etc.) [57, 63, 67, 86, 112]. Such

fluctuations can generate uncertainty in carbon intensity.

To understand the impact of uncertainty in scheduling de-

cisions, we injected the uncertainty into GreenScale based

on statistical distributions of renewable energy generation —

according to [16, 33], fluctuations of solar and wind energy

generations can be modeled with beta and weibull distribu-

tions, respectively. Even in the presence of 16.8% of car-

bon intensity fluctuations, the scheduling decision found by

GreenScale remain consistent (see error bars in figures). This

result implies that the overall conclusions found by Green-

Scale can be generally applicable to realistic environments.

5.3 Impact of Runtime Variance

9

Figure 9: CF of (a) MobileNet-SSD and (b) AR for dif-

fernt carbon intensity scnearios of DC. The carbon opti-

mal execution target also shifts with the carbon intensity

scenarios (i.e., Grid-Mix and Carbon Free in each col-

umn) of DC. The impact of the carbon intensity scenar-

ios of DC on the carbon optimal execution target however

varies with workload characteristics.

Figure 10: Latency and CF of Inception (a) when there

is no runtime variance, (b) when there exist co-located

workloads on computer systems, and (c) when the net-

work is unstable. With runtime variance from various

sources, the carbon optimal execution target shifts.

Fig. 10 shows the CF and latency of Inception (a) when

there is no runtime variance, (b) when there exist co-located

workloads, and (c) when the network is unstable. Note the

carbon intensity models of mobile device, edge DC, and

hyperscale DC are Nighttime Charger, Urban Area, and Grid-

Mix, respectively, and ACT is used.

Runtime variance, especially common for edge-cloud com-

puting, can impact the carbon cost of an application mean-

ingfully.

When there is no runtime variance, the carbon optimal

execution target is Edge DC. However, when there exist co-

located workloads, the carbon-optimal execution target shifts

to DC. This is because the adverse impact of co-located work-

loads depends on the computation and memory capabilities

of each component — DC has the largest computation and

memory capabilities among the computing components. On

the other hand, when either edge network or core network is

unstable, the carbon-optimal execution target shifts to Mobile

Figure 11: Latency and CF of (a) MobileNet-SSD and

(b) MobileNet with two different embodied CF modeling

tools. Depending on the amount of estimated embodied

CF, the carbon optimal decision can vary.

due to the significantly increased data transmission overhead.

5.4 Design Considerations of Applications

Embodied CF model: Given rising embodied CF of the

components, in this study, we explore two embodied CF

modeling tools, i.e., ACT and LCA which estimate lower

and higher embodied CF of the components respectively.

Fig. 11 shows the latency and CF (with two different mod-

eling tools) of execution targets for (a) MobileNet-SSD and

(b) MobileNet. Note the carbon intensity models of mobile

device, edge DC, and hyperscale DC are Nighttime Charger,

Urban Area, and Grid-Mix, respectively, and there is no run-

time variance.

In case of MobileNet-SSD, two different modeling tools

indicate the same execution target — Edge DC — as the

carbon optimal execution target. This is because Edge DC

shows the shortest latency (which linearly affects the embod-

ied CF) and smallest operational energy at the same time.

On the other hand, in case of MobileNet, ACT indicates the

Mobile as the carbon optimal execution target whereas LCA

indicates the Edge DC as the carbon optimal execution target.

This discrepancy comes from different patterns of latency

and operational energy consumption of the execution targets

— in case of mobile-friendly MobileNet, operational energy

efficiency of Mobile is further optimized. Considering the

higher estimation of embodied CF of LCA (compared to

ACT), this result implies that rising embodied CF should be

further considered designing green applications in the future.

Number of servers to rent: Traditionally, the application

designers have tried to optimize the number of servers to

rent from cloud service providers based on the number of

users and their requests. However, in the carbon optimization

design space, it is also crucial to consider the efficiency-

CF trade-off. To provide a guideline on this parameter, we

explore its impact.

GreenScale enables application developers to make carbon-

informed decisions when provisioning and leasing cloud in-

frastructures. Carbon-informed cloud resource planning can

10

Figure 12: Latency and CF of SqueezeNet when different

number of servers are rented from DCs. Latency is nor-

malized to the latency constraint and CF is normalized

to Mobile when Nservers is B/2. Note Nservers and B indi-

cate the number of rented servers and the optimal batch

size (i.e., 1024), respectively. As the number of rented

servers increases (from left to right), the latency and op-

erational efficiency are improved. Due to the improved

latency, idle overhead and embodied CF overhead are

also improved.

achieve up to 24.9% carbon cost reduction.

Fig. 12 shows the latency and CF of SqueezeNet when

there exist different number of servers in DCs. Note the car-

bon intensity models of mobile device, edge DC, and hyper-

scale DC are Nighttime Charger, Urban Area, and Grid-Mix,

respectively. ACT is used and there is no runtime variance.

As shown in Fig. 12, when the number of servers increases,

the latency and operational efficiency are improved. Due to

the improved latency, the idle overhead and embodied CF

overhead are also improved — more number of servers are

used though. As the improvement is higher in the small-scale

edge DC, the carbon optimal execution target shifts from DC

to edge DC.

Workload-dependent parameter: As we present in Sec-

tion 5.1, the carbon optimal execution target depends on the

computation-communication ratio of workloads. By exploit-

ing workload-dependent parameters, developers can change

their computation-communication ratio, improving CF.

Application-level optimization levers, such as frame resolu-

tion setting (Games) and task partitioning (AR/VR pipeline),

provide up to 31.1% and 14.8% carbon savings, respectively.

In case of the game applications, it is possible to sacrifice

the quality of the frames to reduce the amount of computation

and data transmission of the execution targets. Fig. 13(a)

shows the CF of third-person role-playing game application

with different resolution options. When the resolution option

is changed from FHD to HD, the overall carbon footprint is

reduced (by 31.1%) along with the decreased computation

and data transmission overhead. However, the decreased

resolution may have an adverse impact on the QoE of users.

In case of AR/VR applications, it is possible to partition

different pipeline stages between the Mobile and DC by con-

sidering the amount of computations required for each stage

and the size of intermediate results. Fig. 13(b) shows the CF

of AR when we do not consider partitioning the tasks and

when we partition the pipeline stages between the Mobile and

DC. When tasks are partitioned, the overall carbon footprint

is reduced (by 14.8%). There are two reasons: 1) the size

of intermediate results of the stages is lower than that of the

input, reducing the data transmission overhead and 2) the uti-

Figure 13: By exploiting the workload-dependent pa-

rameters, it is possible to change the computation-

communication ratio improving the carbon efficiency.

Note the carbon intensity models of mobile device, edge

DC, and hyperscale DC are Nighttime Charger, Urban

Area, and Grid-Mix, respectively. ACT is used as the em-

bodied CF modeling tool.

Figure 14: Overhead and accuracy vary across the var-

ious scheduling methods (a). Since the runtime over-

head and mis-prediction result in the degradation of car-

bon emissions (b), a careful selection of the scheduling

method is required for designing a green application.

lization of resources (i.e., Mobile Device and DC) increases,

reducing the idle carbon footprint by 55.3%.

Scheduling methods: Since the carbon-optimal execution

target varies with various parameters, it is crucial to select a

scheduling method that can adapt to the parameters. There

have been various scheduling methods that can be used in an

edge-cloud execution environment. Since those techniques

have different amounts of carbon overhead and accuracy, a

careful selection is required.

To provide the selection guidelines, we custom-design and

implement the following scheduling methods, and evaluate

their carbon overhead and accuracy2 using GreenScale:

• Regression which determines the execution targets based

on their CF predicted by a regression-based model [104],

• Classification which determines the execution targets

based on a classification-based model [111, 128],

• Bayesian Optimization (BO) which determines the exe-

cution targets based on a prediction model obtained by

BO [107], and

• Reinforcement Learning (RL) which determines the

execution targets using a policy self-learned by consid-

ering environmental features [72].

Note, for the fair comparison, we fine-tuned all the parameters

of the scheduling methods (e.g., hyperparameters of RL).

2The accuracy and CF results are obtained using the GreenScale,

while the overheads are measured using the real-system implemen-

tation.

11

Fig. 14 shows (a) the carbon overhead of training and pre-

diction accuracy for the techniques and (b) carbon emission

degradation of the techniques compared to the optimal sched-

uler. In general, the training overhead is one-time cost for the

design of the green application, as long as the infrastructure

components are not changed. However, prediction accuracy

can significantly affect the carbon efficiency of the applica-

tion since the mis-prediction causes the CF degradation.

In Fig. 14(b), the larger runtime overhead is, the smaller

carbon emission degradation is, usually coming from the

mis-prediction. Among the methods, RL achieves the best

accuracy by self-learning a policy to adapt to varying features,

such as carbon intensity and runtime variance at the expense

of overhead while the others failed to accurately model the

non-linear relationship of the features — the runtime over-

head of RL is only 2.4% of execution time per frame though.

GreenScale’s carbon-informed decisions can be achieved

through a variety of scheduling algorithms with distinct accuracy-

overhead trade-offs.

6. RELATED WORK

Optimization of edge-cloud applications: From the per-

spective of mobile users, optimizing QoE, which is the prod-

uct of energy efficiency of user-end devices and response

time performance, is crucial. Hence, user-centric optimiza-

tion techniques have tried to identify the best execution

target which minimizes the operational energy consump-

tion of the user-end devices satisfying the performance con-

straint [13, 32, 53, 65, 69, 73, 117, 118, 132].

From the perspective of data center operators, optimiz-

ing resource utilization, operational efficiency, and execution

cost, without affecting the service level agreement (SLA)

constraints is crucial. Hence, the cloud-centric optimization

techniques have tried to maximize the operational efficiency

of infrastructure by splitting the computations across edge-

scale and hyperscale servers [54, 66, 88, 114, 130], satisfy-

ing performance constraints with service level agreements.

Among the techniques, several have tried to consider the time-

varying renewable energy availability at the DC [54, 114].

For instance, [76, 77] tried to switch compuataions between

DCs in different places considering their carbon intensity,

whereas [99] tried to schedule batch jobs considering the

renewable energy within a DC. There has been also carbon-

aware energy capacity planning work for DCs [11] consider-

ing on-site as well as off-site renewable energy generations

and their costs.

Although there have been many techniques, those tech-

niques often fail to make the global carbon optimal decisions,

as they do not consider the unique features of carbon opti-

mization, such as varying carbon intensity at the edge, and the

amortization of embodied carbon emission. This is mainly

due to the absence of framework to quantify and analyze the

carbon emissions of the infrastructure components consider-

ing the aforementioned features.

Carbon modeling tools: Given the increasing carbon foot-

print of ICT, academia and industry have proposed a number

of tools to quantify carbon emissions across the hardware

life cycles. The exergy-based tools follow energy-balance

approach to quantify the environmental impact of servers dur-

ing fabrication and use [55,97]. Although the energy-balance

approach can simplify the design space for sustainable sys-

tems, the exergy-based tools do not consider the impact of

renewable energy during manufacturing and use. The LCA

tools quantify the carbon footprint of products across life cy-

cles [60, 105, 108], not pivoting into the computing systems.

LCA tools use coarse-grained information (e.g., economic

cost of electronics, system’s bill of materials, etc.). Although

product environmental reports published by industry have

been based on the LCA tools [6, 7, 113], they have not been

used for comparative analyses between systems or hardware

components to guide design space exploration. Complement-

ing the above tools, ACT was proposed to consider the direct

carbon footprint from hardware manufacturing and opera-

tional use of computing systems [50].

Based on the above tools, this work proposes GreenScale, a

carbon design space exploration and optimization framework

considering the unique features of carbon optimization. By

using GreenScale, we demonstrate carbon optimization is

distinct from performance and energy optimization, due to

the varying carbon intensity, runtime variance, and embodied

carbon emissions. Based on the design space exploration,

this work provides a guideline to design green application.

7. CONCLUSION

Given the growing carbon emissions of ICT infrastructure,

it is crucial to design green applications which can improve

the carbon-efficiency of the infrastructure components. In this

paper, we propose GreenScale a design space exploration and

optimization framework. Through the in-depth carbon char-

acterization of state-of-the-art applications across edge-cloud

infrastructure, GreenScale demonstrates that the carbon op-

timal scheduling decision depends on various features, such

as workload characteristics, time and location-based carbon

intensity, stochastic runtime variance, and the amortization of

embodied carbon emissions. Those features make the carbon

optimization distinct from performance and energy optimiza-

tion, leaving significant room to improve carbon-efficiency

(by up to 29.1% compared to a state-of-the-art edge-cloud

scheduler). We believe GreenScale can be a viable solution

to provide a detailed guideline for developers to design and

implement green applications that enable sustainable execu-

tion.

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