The Case for Visualization as a Service

Mobile Cloud Gaming as an Example

Abdelmounaam Rezgui

and Zaki Malik

Department of Computer Science & Engineering, New Mexico Tech, Socorro, NM, U.S.A.

Department of Computer Science, Wayne State University, Detroit, MI, U.S.A.

Keywords:

Mobile Cloud Gaming, Visualization as a Service, GPUs, Ofﬂoading.

Abstract:

In recent years, signiﬁcant progress has been made to improve the power efﬁciency of mobile devices. In

particular, new GPU architectures have made it possible to run compute-intensive applications directly on

battery-powered mobile devices. In parallel, research is also being conducted in the area of application of-

ﬂoading, the process of running compute-intensive tasks on cloud servers and delivering the results of these

computations to mobile devices through their wireless interfaces. It is important to understand the power con-

sumption implications of each of these two options. In this paper, we use mobile cloud gaming as an example

to evaluate and compare these two alternatives (running games on the cloud or on mobile devices.) Based on

this comparison, we introduce the concept of Visualization as a Service (VaaS) as a new model to design and

implement graphics-intensive applications for mobile devices. In this model, advanced visualization capabili-

ties (e. g., interactive visualization of high resolution videos/images) would be provided to mobile users as a

service via the Internet. We show through actual hardware speciﬁcations that, despite the recent introduction

of ultra low power GPUs for mobile devices, it remains far more power efﬁcient to ofﬂoad graphics-intensive

tasks to the cloud. The associated latency can still be tolerated in most applications.

1 INTRODUCTION

The growth in the use of mobile devices (mobile

phones, tablets, and ultra mobile PCs) is driving a

phenomenal market shift. A 2013 Gartner report (Ta-

ble 1) predicted that, by 2017, device shipments will

reach more than 2.9 billion units, out of which 90%

will be mobile devices (Milanesi et al., 2013). This

growth is accompanied by an equally phenomenal

boom in mobile applications. According to the re-

search ﬁrm MarketsandMarkets, the total global mo-

bile applications market is expected to be worth $25

billion by 2015 (up from about $6.8 billion in 2010)

(MarketsandMarkets, 2010). A 2012 study by the Ap-

plication Developers Alliance found that 62% of the

U. S. online population owned app-capable devices

and that 74% of those device owners use mobile ap-

plications. As the rendering capabilities of mobile de-

vices improves, mobile applications are becoming in-

creasingly graphics-intensive. This requires intensive

computations that quickly drain the device’s battery.

Several solutions are being developed to reduce

power consumption in graphics-intensive mobile ap-

plications. Some solutions are to be used at develop-

ment time while others are to be used when the appli-

cation is running. The former focus on tools that help

developers estimate power consumption at develop-

ment time. For example, in (Thompson et al., 2011),

the authors present SPOT (System Power Optimiza-

tion Tool), which is a model-driven tool that auto-

mates power consumption emulation code generation.

In (Hao et al., 2013), the authors use program analy-

sis during development time to estimate mobile appli-

cation energy consumption. The latter type of solu-

tions focus on reducing power consumption of hard-

ware components such as the GPU or NIC at run-time.

Examples include the racing to sleep technique (that

sends data at the highest possible rate), wide channels,

and multiple RF chains (Halperin et al., 2010).

A third alternative is application ofﬂoading, the

process of running compute-intensivetasks on servers

(often in the cloud) and delivering the results of these

computations to mobile devices through their wireless

interfaces. However, these wireless interfaces also

may consume substantial amounts of power when re-

ceiving large amounts of data as is typical in many

modern, interactive, graphics-intensive mobile appli-

cations. It is therefore important to understand the

577

Rezgui A. and Malik Z..

The Case for Visualization as a Service - Mobile Cloud Gaming as an Example.

DOI: 10.5220/0005437105770585

In Proceedings of the 5th International Conference on Cloud Computing and Services Science (CLOSER-2015), pages 577-585

ISBN: 978-989-758-104-5

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

Table 1: Worldwide Devices Shipments by Segment (Thousands of Units) (Milanesi et al., 2013).

Device Type 2012 2013 2014 2017

PC (Desk-Based and Notebook) 341,263 315,229 302,315 271,612

Ultramobile 9,822 23,592 38,687 96,350

Tablet 116,113 197,202 265,731 467,951

Mobile Phone 1,746,176 1,875,774 1,949,722 2,128,871

Total 2,213,373 2,411,796 2,556,455 2,964,783

power consumption implications of the two alterna-

tives: running the graphics-intensive application on

the cloud or on the mobile device itself. In this paper,

we use mobile cloud gaming as an example to ana-

lyze and compare these two alternatives in terms of

power consumption. We show through actual hard-

ware speciﬁcations that, despite the recent introduc-

tion of ultra low power GPUs for mobile devices, it

remains far more power efﬁcient to ofﬂoad graphics-

intensive tasks to cloud servers. To make our discus-

sion concrete, we focus on two cases of mobile de-

vices: (i) notebooks and (ii) smartphones. In both

cases, we only consider gaming using the device’s

WiFi interface not its cellular interface. The reason

for this is that the high latency and high cost make

mobile cloud gaming using cellular networks (UMTS,

LTE, etc.) an impractical alternative for most con-

sumers. We will elaborate on this in Section 3.

This paper is organized as follows. We ﬁrst give

an overviewof mobile cloud gaming. In Section 3, we

contrast cellular-based and WiFi-based mobile cloud

gaming from the perspectives of power consumption,

throughput, latency, and cost. In Sections 4 and 5,

we present power consumption trends in modern mo-

bile GPUs and 802.11 network cards. In Section 6,

we quantitatively evaluate and compare power con-

sumption of a gaming session in the two previously

mentioned scenarios in the context of notebooks. We

repeat the same analysis for smartphones in Section 7.

In Section 8, we give the conclusions from our study

and suggest directions for future research.

2 WHAT IS MOBILE CLOUD

GAMING?

Mobile cloud computing (MCC) is the process of of-

ﬂoading compute-intensivetasks from mobile devices

to cloud servers (Soliman et al., 2013; Shiraz et al.,

2013). The purpose is often to save power on the mo-

bile device and/or access servers with much higher

computing power. A prime example of MCC is mo-

bile cloud gaming which is the process of provid-

ing video games on-demand to consumers through

the use of cloud technologies. One beneﬁt is that

the cloud, instead of the user’s device, carries out

most of the computations necessary to play the game,

e. g., complex graphical calculations. This is obvi-

ously a tremendous advantage in case the player uses

a battery-powered, mobile device. Even when power

is not a critical issue for the user’s device, cloud gam-

ing still provides other cloud services, e. g., stor-

age. Cloud gaming enables power savings also on the

cloud itself as it makes it possible that several play-

ers simultaneously share cloud GPUs. For example,

Nvidia’s VGX Hypervisor manages GPU resources to

allowmultiple users to share GPU hardware while im-

proving user density and the utilization of GPU cycles

(Nvidia, 2015a). To illustrate, a single cloud gaming-

capable Nvidia VGX K2 unit requires 38 Watts per

cloud user (Nvidia, 2015c), whereas a comparable

single-user Nvidia GTX 690 consumer unit requires

300 Watts to operate (Nvidia, 2015b). In this case,

cloud gaming can reduce the overall graphics-related

power consumption by 87%.

3 CELLULAR-BASED VS.

WiFi-BASED MOBILE CLOUD

GAMING

Mobile cloud gaming may be achieved using cellular

connections or WiFi connections. While both options

are technically possible and relatively comparable in

terms of power consumption, the WiFi option seems

much more attractive when we consider throughput,

latency and cost. In this section, we present results

from recent studies analyzing power consumption,

throughput, latency, and cost in both scenarios:

3.1 Power Consumption and

Throughput

In (Carroll and Heiser, 2010), the authors analyze

power consumption of smartphones. In particular,

they studied power consumption of the two main net-

working components of the device: WiFi and GPRS

(provided by the GSM subsystem). The test con-

sisted of downloading a simple ﬁle via HTTP using

wget

. The ﬁles contained random data, and were 15

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Figure 1: Power Consumption of WiFi and GSM Modems,

CPU, and RAM (Carroll and Heiser, 2010).

MiB for WiFi, and 50 KiB for GPRS. While the test

was not a gaming session, it still gave valuable in-

sights. The experiments showed that WiFi achieved

a throughput of 660.1 ± 36.8 KiB/s, and GPRS 3.8

± 1.0 KiB/s. However, they both show comparable

power consumption far exceeding the contribution of

the RAM and CPU (Figure 1). The experiments also

showed that, with the increase in throughput possible

using WiFi, CPU and RAM power consumption also

increases reﬂecting the increase in the cost of process-

ing data with a higher throughput.

3.2 Latency

In the context of mobile cloud gaming, latency refers

to the timespan between a user’s action and the cor-

responding reaction (Lampe et al., 2013), e. g., time

between the action of pressing a button and seeing

a character in the game move as a result of that ac-

tion. High latency is a real challenge in mobile cloud

gaming. Wireless connections (WiFi and cellular)

and even wired residential end host round trip times

(RTTs) can exceed 100 ms (Lee et al., 2014). To many

gamers, this is the point when a game’s responsive-

ness becomes unacceptable. A recent effort to reduce

latency in mobile cloud gaming is Outatime, a specu-

lative execution system for mobile cloud gaming that

is able to mask up to 250 ms of network latency (Lee

et al., 2014). It produces speculative rendered frames

of future possible outcomes, delivering them to the

client one entire RTT ahead of time.

While latency is an issue in both cellular-based

and WiFi-based mobile gaming, WiFi connections

typically have much less latency than cellular connec-

tions (Lampe et al., 2013).

3.3 Cost

Cost is also a major factor in favor of WiFi-based mo-

bile cloud gaming. For example, in (Lampe et al.,

2013), the authors give an analytical assessment that

shows that the cost (from cellular data transfer) of a

gaming session of one hour would be about 2.36 Eu-

ros without including the likely additional usage fee

to be paid to the cloud gaming provider.

As we may conclude from the previous discus-

sion, WiFi-based mobile cloud gaming is currently

more practical than cellular-based mobile cloud gam-

ing. We, therefore, limit our discussion to this option

in the remainder of this paper.

4 POWER CONSUMPTION

TRENDS IN MODERN MOBILE

GPUs

It is currently generally true that GPUs offering a

good rendering capability consume much power for

operation and cooling. To illustrate the current power

consumption trends of mobile GPUs, we list in Ta-

ble 2 some modern notebook GPUs and their respec-

tive power consumptions. The table suggests that

playing a game on a notebook equipped with one

of the listed GPUs may not be a viable option. For

example, the Dell Precision M6700 mobile worksta-

tion (which Dell touted as the “world’s most power-

ful 17.3” mobile workstation”) is equipped with the

Nvidia Quadro K5000M GPU. The conﬁguration can

pull 98 Watts of power when running on battery un-

der a heavy CPU or GPU load. This means that

it would be possible to drain the system battery in

about an hour (Notebook Review, 2015). Even with

this limited ability to support long running, compute-

intensive applications, this conﬁguration costs more

than $2K. Better battery life may be possible but with

much more expensive conﬁgurations. Efforts are un-

derway to develop mobile devices with power efﬁ-

cient computing components (e. g., multicore CPUs

and ultra low power GPUs) and batteries that can

run compute-intensive applications (e. g., games and

other graphics-intensiveapplications) for many hours.

For example, Nvidia is introducing Tegra 4, a mobile

GeForce GPU with up to 72 custom cores, a quad-

core ARM Cortex-A15 processor with a ﬁfth Com-

panion Core that further improves performance and

battery life. According to Nvidia, a battery of a ca-

pacity of 38 watt-hours would be sufﬁcient to operate

a Tegra 4 mobile device running a gaming application

between 5 and 10 hours. This corresponds to a power

consumption (for the entire device) of 4 to 8 Watts

(Hruska, 2013). However, mobile devices with these

high-end conﬁgurations will remain beyond the reach

of average users for the foreseeable future.

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Table 2: Energy Consumption of Some Modern Notebook GPUs.

GPU Card Power Consumption (W)

NVIDIA GeForce GTX 680M SLI 2 x 100

AMD Radeon HD 7970M Crossﬁre 2 x 100

NVIDIA GeForce GTX 680MX 122

NVIDIA GeForce GTX 675M SLI 2 x 100

GeForce GTX 680M 100

Quadro K5000M 100

AMD Radeon HD 7970M 100

5 POWER CONSUMPTION

TRENDS IN MODERN

NOTEBOOK NICs

The original 1997 release of the IEEE 802.11 stan-

dard operated in the 2.4 GHz frequency band and pro-

vided a data bit rate of 1 to 2 Mb/s. The standard re-

lease approved in February 2014 (known as 802.11ad)

operates in the 2.4/5/60 GHz frequency bands and

provides a data bit rate of up to 6.75 Gbit/s. While

higher bit rates often translate into higher power con-

sumption, this is less true in recent ultra-low power

802.11 standards. For example, today’s fastest 3 an-

tenna 802.11n device can achieve 450 Mbps. A single

antenna 802.11ac device can achieve a similar bit rate

with similar power consumption. This means that a

typical tablet with single antenna 802.11n 150Mbps

WiFi can now support 450 Mbps with 802.11ac with-

out any increase in power consumption or decrease in

battery life (Netgear, 2012).

6 GRAPHICS-INTENSIVE

APPLICATIONS: GPUs VS. NICs

To assess the beneﬁts of using a mobile GPU ver-

sus ofﬂoading to the cloud, we consider gaming as

it is a typical example of graphics-intensive mobile

applications. Speciﬁcally, we consider four modern

games that rely heavily on GPUs. We compare two

scenarios in terms of power consumption. In the ﬁrst

scenario, the game is run entirely on the mobile de-

vice and uses only its GPU. In the second scenario,

we consider an execution where the game is run on

a cloud server and the mobile device only receives

and renders sequences of frames produced by the

server. We analytically evaluate power consumption

in these two scenarios and show that, with modern

wireless technology, ofﬂoading is a far better alter-

native to running graphics-intensive applications us-

ing the device’s GPU. To make the comparison even

more in favor of the GPU-based alternative, we ig-

nore the power consumption of the device’s disk. We

assume that, when a graphics-intensive application is

run on a mobile device, most of the power is con-

sumed by the device’s GPU. This is becoming in-

creasingly true with the wide availability of mobile

devices with solid-state disk drives.

To compare power consumption in the two scenar-

ios, we ﬁrst present a simple model that captures the

interactions between the player and the gaming appli-

cation. We will assume that, during a given gaming

session of duration t, the player takes an action after

every r seconds in average. We call r the reactivity

of the player. To respond to the player’s action, the

application generates a video stream of length v sec-

onds.

So, during the entire session, the application

generates t/r video sequences whose length is v sec-

onds each. In total, the application generates tv/r sec-

onds of video during the given gaming session.

6.1 Scenario 1: Gaming using the

Mobile Device’s GPU

To assess the power consumed by a notebook’s GPU

in a gaming session, we used the benchmark pre-

sented in (NoteBookCheck, 2014). The benchmark

has a large number of notebook GPUs and a num-

ber of popular games. For each combination of

game and GPU card, the benchmark gives the aver-

age number of frames per second (fps) that the GPU

card achieves with four different resolution levels:

Low (L), Medium (M), High (H), and Ultra (U). The

benchmark considers that a frame rate of 25 fps is

sufﬁcient for ﬂuent gaming. For the purpose of this

study, we considered four GPU cards and four 2014

games, namely GRID Autosport, Watch Dogs, Titan-

fall, and Thief. Table 3 gives the frame rates ob-

This is to simplify our discussion. In practice, the ap-

plication likely generates two video sequences of different

lengths in response to two different actions.

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tained in the given combinations

. The resolutions

in the table are as follows: Low (1024x768), Medium

(1366x768), High (1920x1080 for the ﬁrst two games

and 1366x768 for the last two games), and Ultra

(1920x1080). Table 3 also gives power consumption

for the four GPU cards.

As an example, consider a mobile device equipped

with a GPU of type Nvidia GeForce GTX 850M. As

shown in Table 3, this GPU card will consume be-

tween 40 and 45 Watts in one hour. We will show that

ofﬂoading to the cloud (Scenario 2) brings an order of

magnitude reduction in terms of the power consumed

by the mobile device.

6.2 Scenario 2: Mobile Cloud Gaming

We now evaluate the required data bit rate that the

NIC card of a notebook would have to support to

achieve the same game ﬂuency (i. e., 25 fps) for one

of the four GPU cards of Table 3. As an example, con-

sider again the Nvidia GeForce GTX 850M (which

is the best of the four GPUs in terms of power con-

sumption.) For the game GRID Autosport and for low

resolution, the Nvidia GeForce GTX 850M is able to

support 166.65 fps which is: 166.65 x 1024 x 768 x 8

= 1048471142.4 bits/second (assuming a color depth

of 8 bits/pixel). Thus the NIC card would have to

operate at a bit rate of about 1.05 Gb/s. A similar

computation for the Ultra high resolution level gives

us a bit rate of: 34.7 x 1920 x 1080 x 8 = 575631360

bits/second. Thus, to support the same gaming ﬂu-

ency at the Ultra-high resolution level, the NIC would

have to operate at 575 Mb/s. Note that the required bit

rate at the Ultra-high resolution level is almost half of

that of the required bit rate at the low resolution level

because the GPU supports a lower frame rate at the

Ultra-high resolution level. To support these bit rates,

the mobile device’s NIC would have to be 802.11ad

compliant. The 802.11ad standard is able to support

bit rates up to 6.77 Gbit/s.

To evaluate the power consumed by the device’s

wireless networking card during the considered gam-

ing session, we will assume a model of a wireless

networking card that consumes ρ

watts when in

transmit mode and ρ

watts when in receive mode.

With single-antenna 802.11 devices, the devices can-

not send and receive simultaneously. This normally

implies that one has also to take into account the

cost of frequently switching the device’s radio be-

tween the transmit and the receive mode. This, how-

ever, is changing as mobile devices are now increas-

The missing value in the last row corresponds to a test

that could not be run because the GPU card could not sup-

port a sufﬁciently acceptable frame rate.

ingly being equipped with MIMO (multiple-input and

multiple-output) technology enabling the use of mul-

tiple antennas at both the transmitter and receiver. In

fact, Mobile Experts predicts that the use of MIMO

technology will reach 500 million PCs, tablets, and

smartphonesby 2016 (Madden, 2011). As a result, we

will only take into account power consumption due to

transmission, reception, and idling. We will note the

power consumption of the radio during idling by ρ

Let µ

and µ

be the transmission and reception

rates respectively. Let l be the length of the packet

sent to the application when the player takes an ac-

tion. The time needed to transmit this packet is then:

l/µ

. Let t be the length of the entire gaming session

(in seconds). During the time t, the device transmits

t/r times where r is the player’s reactivity (deﬁned

earlier). The total time during which the device trans-

mits is therefore:

rµ

secs. (1)

The corresponding power consumption during the

period of time t is:

rµ

(2)

To evaluate the power consumed by the device’s

receiver, recall that our model assumes that, to re-

spond to each player’s action, the application gener-

ates a video stream of length v seconds. The devices

spends v/µ

seconds to receive each of these video

streams. Since we have t/r of these video streams

during the considered time period of length t, the de-

vice’s NIC receives video streams during:

rµ

secs. (3)

Let P

be the power that the device’s NIC con-

sumes to receive the t/r video sequences. P

can be

given by:

rµ

(4)

The device’s NIC is in the idle mode when it is not

transmitting and not receiving. This occurs during:

t −

rµ

−

rµ

secs. (5)

The power consumed by the device’s NIC while

idling is therefore:

= ρ

t(1−

rµ

−

rµ

) (6)

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Table 3: Average Frame Rate of Some Combinations of GPU cards, Games, and Resolutions.

GPU Card GRID Autosport Watch Dogs Titanfall Thief

L M H U L M H U L M H U L M H U

GeForce GTX 770M (75 Watts)

199.6 130.3 92.6 46.5 80.7 66.1 27.7 19.8 60 60 59.3 48.3 57.1 51.3 46.8 26.6

GeForce GTX 860M (60 Watts)

192.15 109.65 88 47.2 71.2 60.7 27.7 18.9 60 60 59.5 42.4 60.5 52.7 44 23.95

GeForce GTX 850M (40-45 Watts)

166.65 99.33 68.3 34.7 61.8 52.3 20.75 14.7 60 59.7 53.25 34.3 46.45 39.6 36.65 18.2

GeForce GTX 765M (50-75 Watts)

191.9 130.7 74.1 34.8 81.3 56.9 21.1 60 59.7 54.3 35.6 58.2 43.1 37 19.1

Table 4: Power Consumption for the Intel Dual Band Wireless-AC 7260 802.11ac, 2x2 Wi-Fi Adapter (Hewlett Packard,

2013).

Mode Power (mW)

Transmit 2000

Receive 1600

Idle (WLAN associated) 250

Idle (WLAN unassociated) 100

Radio Off 75

Let P

NIC

(t) be the power consumed by the wire-

less NIC during the t-second gaming session. P

NIC

(t)

is then:

NIC

(t) = P

+ P

rµ

+ ρ

t(1−

rµ

−

rµ

)

In practice, one must consider values for ρ

that

accommodate high reception rates (for high deﬁni-

tion gaming) and values for ρ

that correspond to

low transmission rates since the user’s actions usually

translate into short packets.

To illustrate, we consider the case of an HP Elite-

Book Folio 1040 G1 Notebook PC. This notebook is

equipped with the Intel Dual Band Wireless-AC 7260

802.11ac Wi-Fi Adapter whose power consumption

is given in Table 4) (Hewlett Packard, 2013). As-

sume that the NIC card is 80% of the time in recep-

tion mode, 10% of the time in transmit mode, and is

idle (but associated) 10% of the time. If we apply our

power model to this WiFi adapter, power consump-

tion in one hour would be (approximately):

NIC

(t) = P

+ P

idle

= 0.1× 2000+ 0.8× 1600+ 0.1x250

= 1505 milliwatts

assuming the highest Rx and Tx power levels.

Considering the examnple of a notebook equipped

equipped with a GPU of type Nvidia GeForce GTX

850M (Section 6.1), we can estimate that, in one hour,

the GPU card will consume about betwee 0.8x40 W

and 0.8 x 45 W, i.e., between 32W and 36W, assuming

a GPU ustilzation of 80% similar to our assumption of

the NIC card being in the Rx mode 80 % of the time.

From the results obtained in the two scenarios,

it is clear that using the wireless networking inter-

face in a gaming session consumes much less power

than using a modern GPU card installed on the same

device. Speciﬁcally, the power consumed using the

wireless card would be around (1505 / 34000) x 100,

i.e., around 4.42% of the power consumed by the on-

device GPU.

7 MOBILE CLOUD GAMING

USING SMARTPHONES

We now compare power consumption between GPU-

based gaming and cloud-based gaming on smart-

phones.

7.1 Power Consumption of GPU-based

Gaming on Smartphones

In (Kim et al., 2015), the authors measured power

consumption of a Qualcomm Adreno 320 GPU in a

Google Nexus 4 smartphone. They used two games

in their tests: Angry Birds (2D game) and Droid In-

vaders (3D game). The authors report results for a

gaming session that lasted 560 seconds for Angry

Birds and 505 seconds for Droid Invaders. Through-

out the two gaming sessions, power consumption re-

mained approximately at around 1750 mW for Angry

Birds and at around 2000 mW for Droid Invaders. We

will use the average of these two numbers (1875 mW)

as an estimate of the average power consumption of

both 2D and 3D games.

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Table 5: Frame Rates for the Adreno 320 GPU on a Google Nexus 4 and on a Samusung Galaxy S4 using the Manhattan

Benchmark (GFXBench, 2015)

Smartphone Model GPU Resolution Frame Rate

Google Nexus 4 (LG E960) Adreno 320 1196 x 768 9.2

Google Nexus 5 Adreno 330 1794 x 1080 10.1

Samsung GT-I9507 Galaxy S4 Adreno 320 1920 x 1080 5.4

Samsung GT-I9515 Galaxy S4 Value Edition Adreno 320 1920 x 1080 5.1

Samsung Galaxy S4 Active (GT-I9295, SGH-I537) Adreno 320 1920 x 1080 5.1

Samsung Galaxy S4 (GT-I9505, GT-I9508, SC-04E, SCH-I545,

SCH-R970, SGH-I337, SGH-M919, SPH-L720) Adreno 320 1920 x 1080 5.1

Figure 2: 802.11ac Throughput and Power Comparison for Galaxy S4 and Galaxy S5 with a Channel Width of 20/40/80 MHz

and FA on. (Saha et al., 2015).

7.2 Power Consumption of Cloud-based

Gaming on Smartphones

To compare power consumption of cloud-based gam-

ing with GPU-based gaming, we ﬁrst need to evaluate

the NIC bit rate that would be necessary to provide

a gaming experience comparable to the one achieved

through GPU-based gaming. For this, we used results

from the GFXBench 3.0 benchmark, a cross-platform

OpenGL ES 3 benchmark designed for measuring

graphics performance, render quality and power con-

sumption on several types of devices including smart-

phones. In particular, the benchmark has battery

and stability tests that measure the devices battery

life and performance stability by logging frames-per-

second (fps) performance and expected battery run-

ning time while running sustained game-like anima-

tions (GFXBench, 2015). We focused on results for

the Adreno 320 GPU on a Google Nexus 4, which is

the same conﬁguration used in the GPU-based sce-

nario of the previous section.

Table 5 shows the frame rate for several tests using

the Manhattan benchmark (GFXBench, 2015). Row

1 of the table shows that the Adreno 320 GPU on a

Google Nexus 4 achieveda frame rate of 9.2 fps. Con-

sidering this frame rate and the given resolution (1196

x 768), the NIC bit rate that would be necessary to

achieve a similar gaming experience can be derived

as: 9.2 x 1196 x 768 x 24 (bits/pixel) = 202810982.4

bps ≈ 203 Mbps.

We now turn to evaluating the power needed on

the NIC to sustain this bit rate. For this, we use the re-

sults from (Saha et al., 2015) where the authors exper-

iment with a variety of smartphones supportingdiffer-

ent subsets of 802.11n/ac features. In particular, the

authors measured throughput and power consumption

in a Galaxy S4 using different conﬁgurations. Based

on their ﬁndings for the Galaxy S4 used in the ex-

periment, only 802.11ac offers Rx throughput levels

sufﬁcient for the considered gaming bit rate (of 203

Mbps). Figure 2 (reproduced from (Saha et al., 2015))

shows that the best Rx throughput with 802.11ac was

about 250 Mbps. Power consumption in this case was

about 1100 mW.

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Table 6: Power Consumption (in mW) in Non-

Communicating Modes. (Saha et al., 2015).

Conﬁguration PSM Idle

802.11n, 20 MHz, SS 24 ± 16 398 ± 7

802.11n, 40 MHz, SS 25 ± 5 413 ± 2

802.11ac, 20 MHz, SS 22 ± 9 374 ± 7

802.11ac, 40 MHz, SS 20 ± 9 425 ± 3

802.11ac, 80 MHz, SS 19 ± 10 529 ± 11

The authors did not provide measurements for the

throughput and power consumption in transmit mode

with 802.11ac. They, however, measured through-

put and power consumption in transmit mode with

802.11n. Figure 3 shows their results. In particular,

the results show that that it is possible to achieve a

Tx throughpout of more than 40 Mbps with as little

power as 800 mW. Note that, in a cloud-based gaming

session, a Tx throughout of 40 Mbps is typically sufﬁ-

cient. The authors also measured power consumption

of the Galaxy S4 when it is in non-communication

modes, i.e., power saving mode (PSM) or idle. Their

results (Table 6) show that the highest 802.11ac power

consumption in PSM was 31 mW and that the high-

est 802.11ac power consumption when idle was 540

mW. The relatively high idle mode power consump-

tion of larger channel widths (80 Mhz) has also been

observed by other studies (e. g., (Zeng et al., 2014)).

Figure 3: Comparison of Different CPU Gover-

nors/Frequencies for Galaxy S4 (802.11n) (Saha et al.,

2015).

Based on all the previous results from (Saha et al.,

2015) and assuming that, in a cloud-based gaming

session, the device’s 802.11 adapter spends 80% of

the time receiving, 10% of the time transmitting, and

10% of the time idle, the total power consumed in one

hour by the 802.11 adapter would be:

NIC

(t) = P

+ P

idle

= 0.1 × 800+ 0.8 × 1100+ 0.1× 540

= 1014 milliwatts

Comparing power consumption in the two scenar-

ios: using GPU-based gaming (which is 1875 mW

as derived in Section 7.1 and cloud-based gaming

(which is 1014 mW as derived in this section), we

conclude that, in the considered smartphone conﬁgu-

ration, cloud-based gaming can potentially result into

a power saving of about 46%.

8 CONCLUSION

We presented a comparative analysis between two

scenarios of mobile gaming, one that relies entirely

on the GPU of the mobile device and one where the

gaming application runs on the cloud. We analyti-

cally evaluated and compared power consumption in

these two scenarios. Based on our analysis, we ar-

gue that the idea of Visualization-as-a-Service (VaaS)

is a viable computing model that enables the users of

mobile devices with limited power capabilities to still

use long running graphics-intensive applications. In

this model, advanced visualization capabilities would

be provided to users as a service via the Internet.

Two research directions are worth studying: (i) the

impact of protocol (TCP/UDP/IP) overhead and (ii)

the impact of the CPU overhead for processing the

large number of packets typical in cloud gaming. We

believe that considering these two types of overhead

will provide a more accurate assessment of the bene-

ﬁts of cloud-based gaming over GPU-based gaming.

ACKNOWLEDGEMENTS

The ﬁrst author gratefully acknowledges travel sup-

port from the Institute for Complex Additive Systems

Analysis (ICASA) at New Mexico Tech for presenta-

tion of this paper at the CLOSER’2015 Conference.

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