Towards Bandwidth Optimization in Fog Computing
using FACE Framework
Rosangela de F
atima Pereira Marquesone
Erico Augusto da Silva
, Nelson Mimura Gonzalez
Karen Langona
, Walter Akio Goya
, Fernando Frota Red
Tereza Cristina Melo de Brito Carvalho
, Jan-Erik M
and Azimeh Sefidcon
Laboratory of Computer Networks and Architecture, Escola Polit
ecnica, University of S
ao Paulo, Brazil
Ericsson Research, Sweden
Fog Computing, Bandwidth Optimization, Video Surveillance.
The continuous growth of data created by Internet-connected devices has been posing a challenge for mobile
operators. The increase in the network traffic has exceeded the network capacity to efficiently provide services,
specially for applications that require low latency. Edge computing is a concept that allows lowering the
network traffic by using cloud-computing resources closer to the devices that either consume or generate data.
Based on this concept, we designed an architecture that offers a mechanism to reduce bandwidth consumption.
The proposed solution is capable of intercepting the data, redirecting it to a processing node that is allocated
between the end device and the server, in order to apply features that reduce the amount of data on the network.
The architecture has been validated through a prototype using video surveillance. This area of application was
selected due to the high bandwidth requirement to transfer video data. Results show that in the best scenario is
possible to obtain about 97% of bandwidth gain, which can improve the quality of services by offering better
response times.
The widespread adoption of smartphones, many mo-
bile devices and sensors from the Internet of Things
(IoT) has been creating a world with ubiquitous con-
nectivity. According to a report published by Erics-
son, only in the year of 2014, 800 million smartphone
subscriptions were created worldwide. Consequently,
it is expected a total of 5.4 billion subscriptions of
mobile broadband by 2020 (eri, 2015).
Mobile subscribers take full advantage of existing
wireless network infrastructure, which allows them
to enjoy with a wide range of applications, includ-
ing video streaming, gaming and social networking.
These applications require faster response times to
provide efficient and satisfying experience to the end
user. However, the rising use of these applications re-
sults in a continuous growth on the mobile data traffic.
As a consequence, the data traffic has been exceeding
the capacity of current network infrastructure, impact-
ing application performance and network efficiency.
As the data volume across the network increases,
application service providers are being challenged to
scale their data center with properly storage and pro-
cessing capacity. In this context, cloud computing
has been used to support the large volume of data
consumption by end users. Cloud computing allows
service providers to obtain computing and storage
resources on demand, providing them with flexibil-
ity and dynamic resource allocation. However, even
in a cloud infrastructure, the data are generated or
terminated in a central location. For this reason,
cloud computing cannot solve the problem related to
network performance, since the data must be trans-
ferred through the network until it finds its destina-
tion. Therefore, the more mobile devices and appli-
cations are made available, larger and faster network
infrastructure becomes necessary.
An approach capable of reducing data traffic
across the network is to process the data before it
reaches its destination. Edge computing can be a
promising way to address this requirement. It con-
sists of storage and computing resources allocated as
closer as possible to the edges of the network. This
approach makes possible to process the data as soon
as it reaches the network, thus reducing the amount of
Marquesone, R., Silva, É., Gonzalez, N., Langona, K., Goya, W., Redígolo, F., Carvalho, T., Mångs, J-E. and Sefidcon, A.
Towards Bandwidth Optimization in Fog Computing using FACE Framework.
DOI: 10.5220/0006303804910498
In Proceedings of the 7th International Conference on Cloud Computing and Services Science (CLOSER 2017), pages 463-470
ISBN: 978-989-758-243-1
Copyright © 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
data that must be sent to the final destination. There-
fore, edge computing allows the execution of many
applications on intermediary nodes, instead of only in
their central location. Fog and Edge computing are in-
terchangeable terms according to some authors (Gon-
zalez et al., 2016; Dastjerdi and Buyya, 2016; Hu
et al., 2016).
Given the computing capability offered by fog
computing, it is necessary to investigate how to en-
hance network performance from it. Considering this
context, in this paper we propose the FACE. It of-
fers a framework with functionalities capable of ei-
ther reducing or optimizing the network consumption.
The functions provided are (F)iltering, (A)ggregation,
(C)ompression and (E)xtraction. In this paper we ex-
plained the definition and objectives of each function
and also present potential scenarios where FACE can
be applied. In order to test the capabilities of our solu-
tion, we performed an experiment using FACE frame-
work in a video surveillance application. This context
was selected due to the large amount of data gener-
ated by video cameras and due to the need of such
applications for low latency. Results show that FACE
framework provides significant reduction of data traf-
fic, and it can be applied in the network infrastructure
in a transparent way.
The remainder of this paper is organized as fol-
lows. In Section 2 we present a contextualization
about fog computing, describing its definition and dis-
tinctions of cloud computing. The description in de-
tail of the FACE framework is presented in Section 3.
In Section 4 is described our experiment with a video-
surveillance use case. The tests, results and analy-
sis are presented in Section 5, and finally, Section 6
brings the conclusions and future work.
The current wireless network infrastructure provides
the connection between the end users and the data
center from application providers. A user of a mo-
bile phone when performing a search using a browser
on its device counts on the wireless network infras-
tructure to upload her/his request and deliver it to the
destination. The response to the user’s request will
be brought to her/his device by the wireless network
infrastructure. Therefore, the request from the user
has to reach the central server in order to provide the
response to the user. The data generated by an end
user on the edges of the wireless networks will have
to travel through the network until it finds its destina-
tion. The problems with this centralized processing
(conventional scenario) are:
Delay insertion to the response time. This oc-
curs due to the time that it takes for the request
to be transferred from the user device to the cen-
tral server and then back, to bring the response to
the end user. This time can compromise the per-
formance of latency-sensitive applications such as
video streaming and gaming. Therefore, the faster
that a response can be issued to the end user, the
better the network meets the application response
time requirements.
Traffic addition to the network. Since all the end-
users requests have to be transferred through the
network until it finds its central server, more traf-
fic is added to the network. With the growing
number of smartphone subscriptions, the telecom
operators will have to provide networks that sup-
port more traffic and more users.
From these two observations, we can conclude
that telecom operators need to find innovative ap-
proaches to support the continuous growth of data
traffic. Therefore, fog computing emerges as an al-
ternative to meet this requirement (Margulius, 2002;
Pang and Tan, 2004). Considered an extension of
cloud computing paradigm, this concept was defined
for the first time in 2012 by the work of Flavio
Bonomi et al. (Bonomi et al., 2012) According to the
authors, fog computing is a virtualized platform that
brings cloud computing services (compute, storage,
and network) closer to the end user, in the edges of the
networks. By offering these services, the platform can
better meet the requirements of time-sensitive appli-
cations. It implies that the cloud computing resources
will be geographically distributed to locations closer
to the end users and/or IoT endpoints. This distributed
infrastructure allows to get bandwidth savings by ap-
plying data processing across intermediary processing
nodes, rather than only in the cloud.
The FACE framework consists of a set of specific
functions that aims at improving response time and
avoiding sending unnecessary traffic through the net-
work. It was designed to perform data processing in
the edge or intermediary nodes of the network. As
depicted in Fig. 1, the functions can be hosted in
a box composed by compute and storage resources.
This box may be placed either at the base station level
(setup 1) or right after it (setup 2). In both cases the
box is added to the network topology and is responsi-
ble for intersecting the data, applying the functions to
data processing, and delivering the data results to the
next component of the network.
CLOSER 2017 - 7th International Conference on Cloud Computing and Services Science
To reduce the data traffic, decrease overhead and
allow better management of the network bandwidth,
this framework is composed by four different func-
tions: Filtering, Aggregation, Compression, and Ex-
traction. The name FACE is an acronym formed by
the first letter of each function name. They can be ei-
ther individually or jointly applied to the input traffic.
This choice is determined by each individual use case.
Figure 1: The box and the two network topologies.
In this section we describe the definition of each
function and also present potential scenarios that may
benefit from the FACE functions.
The filtering function allows reducing data traffic by
either analyzing or processing the data at the box. All
data collected are computed by this function and only
the data that satisfy a group of predefined filtering cri-
teria will be sent on the network. The criteria consist
of measures and/or attributes defined by the applica-
tion service providers. Therefore, the filtering func-
tion offer bandwidth savings by excluding data before
it reaches the central server. In addition, the same ap-
proach can be used for data cleaning mechanisms, by
defining criteria to detect and discard inconsistent and
corrupted data.
Figure 4 illustrates an use case that applies the fil-
tering function on pictures taken by surveillance cam-
eras installed along a freeway. The monitoring de-
vices help control the average speed allowed on the
freeway. For this example it was assumed that the
surveillance is done by eight installed cameras. As
the car passes by the first set of cameras a picture is
taken at P1. When the car passes by the second set
of cameras another picture is taken at P2. Then the
function calculates the average speed and if it is over
the limit for that freeway the pictures will be sent to
the central server to be processed and a ticket will be
issued to that car. Assuming the size of a picture is
30KB, each set of eight cameras would send 240KB
to the network. However, if we assume that the infrac-
tion rate on the freeway is 15%, the filtering function
will only allow the 15% of the data to be carried over
the network. The data that is collected on the edges of
the network is then filtered and only 15% of it is sent
to a central location.
Figure 2: The gains of applying the filtering function.
As stated before, the filtering function can be ap-
plied to scenarios where parts of the data can be dis-
carded, in case a set of criteria is not satisfied. An
example could be the data collected from sensor net-
works used to control a device or an environment, e.g.
temperature sensor to prevent fires. Using the filtering
function, only the data that present abnormal event
will be sent to the final destination, allowing to sub-
stantially reduce data traffic.
The aggregation function reduces the overhead on the
network since it combines and accumulates data to be
sent through the network in batches. Therefore, a sin-
gle connection can be used to send data from multiple
users. This solution is suitable for scenarios that col-
lect large amount of small data in frequent times. If
the application is not latency sensitive, the aggrega-
tion function can store the data and send it only in
predefined time intervals. Considering the previous
scenario, the surveillance cameras in a freeway, the
pictures taken at P1 and P2 do not need to be pro-
cessed in real-time. It is possible to archive the pic-
tures using the aggregation function and send batches
of them in predefined intervals.
While the filtering function requires processing
capacity from the box, the aggregation function re-
quires storage capacity to cache the data. Triggering
mechanisms can be used to define rules regarding the
data delivery. This trigger can also be turned off when
the amount of data is lower than usual. For example,
applications that concentrates higher data traffic only
during the day, do not need to aggregate the data at
night. Therefore, it is necessary to investigate which
compression mechanism better reduces the data with-
out generating low responses.
Towards Bandwidth Optimization in Fog Computing using FACE Framework
Although the filtering function allows reducing the
amount of data, it is not applicable when applications
require to store all the data in a central server, or in
cases when it is not possible to define filtering cri-
teria for the data. A different strategy to reduce the
data traffic is to use the compression function. This
function enables the execution of compression mech-
anisms according to the data structure. For exam-
ple, pictures collected from video surveillance cam-
eras can be compressed by converting the raw photos
to JPEG format.
In order to perform further processing over the
data in the central server, service providers must be
advised about which algorithm was used for data
compression. By knowing the algorithm they can
decompress the data. A service provider of video
streaming could add its own compression algorithm
to the box, so data from video uploads could be re-
Due to the large alternatives to compress the data,
the execution of compression function can represent
a trade-off. While it allows reducing the data size,
and consequently reducing data traffic, some algo-
rithms may aggregate an additional overhead dur-
ing the compression, impacting the performance of
latency-sensitive applications.
The extraction function, similar to the filtering func-
tion, focuses on mechanisms to discard unnecessary
data. However, this function enables the data re-
duction by extracting only an specific portion of the
data. For example, considering the aforementioned
scenario that takes pictures of a car to calculate the
average speed. The extraction function can be used to
extract the license plate information from the picture
and send to the server only the text with this content,
instead of the whole picture. Since the text file with
only this specific portion contains just a few kilobytes,
the data traffic can be reduced.
Similar to the compression function, the service
providers may determine the application logic in the
extraction function. There are many approaches to it.
Text mining algorithms can be applied on text data,
and only the output data from this computation can
be sent through the network. Pattern recognition tech-
niques and image segmentation can also be applied in
the box, allowing to determine the application logic
over the data in a faster response time. The data sent
to the server are the one that must be recorded.
The FACE functions can also be jointly applied
to the input traffic. When two or more functions are
applied to the input traffic, the data reduction can be
even higher. These possibilities result in many bene-
fits, such as reduce costs related with network infras-
tructure for Telecom operators, optimize the perfor-
mance of application service providers and improve
end users experience. The experiment and evaluation
of FACE framework is presented in the next section.
In order to perform an experiment to analyze the per-
formance of the FACE framework, we designed an
use case applied to video surveillance. We demon-
strate our strategy to apply the compression function
over image data in a box closer to the base station.
The tests were performed to identify the bandwidth
savings by applying this function and to evaluate the
overhead to intersect, apply the processing and deliver
the data.
Fog computing applied to video surveillance can
improve the performance of the system by reduc-
ing the amount of data transmitted across the net-
work. This reduction allows improving the applica-
tion response times, since the transmission time is
decreased. The overall response time of the system
can also be improved by the bandwidth optimization.
A strategy to decrease the amount of data in a video
surveillance scenario that does not require high qual-
ity images is to convert the images to grayscale mode
and resize them to a lower resolution. Therefore, be-
fore the video data reaches a cloud infrastructure or
a data center to be processed, these optimizations can
be applied in a modular compute and storage device,
which is located closer to where the images have been
generated (e.g. cameras). Considering video surveil-
lance systems composed by several cameras, this ap-
proach can result in significant bandwidth reduction
and high quality services.
In a traditional video surveillance scenario, the
source continuously sends images (IMG.png) directly
to a server. The system must provide an online view
of the scenes, specially for the ones that presents some
activity (Nilsson and Axis, 2017). As illustrated in
Figure 3, we designed an architecture that intercepts
the images from the source, redirecting them to the
Processing Node (PN), where, if applicable, the com-
pression functions are applied, and only then the im-
ages are sent to the final destination. This model ap-
plies compression to a given image if there is a differ-
ence between this image and the previous one.
We proposed two ways to connect the PN to the
rest of the system: setup 1 connects the PN to the
CLOSER 2017 - 7th International Conference on Cloud Computing and Services Science
Figure 3: System architecture to apply compression function closer to the network.
Base Station (BS) only, the PN receives images from
the BS and sends them back, so BS can send them
to the server; setup 2 is a connection in series, where
PN is connected between the BS and the server, so the
PN receives images from BS and send them directly
to the server.
Data redirection is done through iptables con-
figuration. The iptables are an administrative tool
for IPv4 packets filtering and NAT (Network Address
Translation) and they are used to control packets in
transit within the machine through rules definitions.
The compression takes place in the PN and it is done
by the optimizer. This component is responsible for
comparing two images and deciding whether it should
apply compression. If so, the optimizer lowers the
image resolution and converts it to greyscale, which
reduces its size.
Within the PN there are the local mover and
remote mover. These mechanisms are responsible
for moving images within the machine file system and
starting the transfer between PN and server. These
mechanisms implement time-based differentiation al-
gorithm, which allows new files detection and the sub-
sequent processing of the previous ones. The idea be-
hind it is that if there is a new file listed in the file sys-
tem, the old ones are safe to be processed, without any
synchronization risks or race conditions. The system
generates two types of log files: timestamp logs and
bandwidth log. The first one extracts latency metrics
and the second one evaluates the bandwidth gain by
keeping track of the size of incoming and outcoming
In this section we present the testbed setup, the test
description and the metrics used in the prototype. We
also present results obtained from a set of experiments
performed to evaluate the effectiveness of the FACE
5.1 Testbed Setup
According to the architecture presented in Fig. 3,
the testbed is composed of one notebook with camera
(source), one wireless router, three physical machines
(BS, PN and server) and one PC (launcher), con-
nected to the laboratory network infrastructure. Table
1 shows the hardware specs used in the testbed. The
wireless router is a Netgear WPN824 v3 and we use
Secure Shell (SSH) to establish access to the hosts.
We used the laptop webcam to run the test because
we needed a custom application capable of collecting
some of the latency metrics. However, in a real video
surveillance scenario any type of camera with image
transfer capabilities could be used.
Towards Bandwidth Optimization in Fog Computing using FACE Framework
Table 1: Testbed hardware specs.
Host Processor RAM
Intel Core i7-3635
8 GB
Intel Xeon E3-1230
16 GB
Intel Xeon E3-1230
16 GB
Intel Xeon E3-1230
16 GB
Intel Core i5-2500
8 GB
5.2 Tests Description
In order to identify the latency introduced by our so-
lution and the amount of bandwidth gain it can gen-
erate, we have used the metric files collected from
the testbed hosts. These files allowed us to identify
the amount of time consumed by each operation per-
formed during the tests.
The tests were performed with five different sce-
Test 0: is the conventional scenario, without the
processing node;
Test 1A: uses the processing node in the Setup 1,
without compression functionality;
Test 1B: uses the processing node in Setup 1, but
now with compression functionality;
Test 2A: uses the processing node in the Setup 2,
without compression functionality;
Test 2B: uses the processing node in Setup 2, but
now with compression functionality;
Regarding latency measurements, each machine
listens to a set of events, which are described in Ta-
ble 2. When triggered, these events generate entries
in the timestamp log, containing the event type and
the time they occurred. These two pieces of informa-
tion are used to extract the amount of time consumed
by each step. We used Network Time Protocol (NTP)
to ensure the machines had their clocks synchronized.
PN is responsible for accounting the amount of
data received and sent. Once the image transfer be-
tween source and PN is completed, it registers the im-
age size in in data file and, before sending the image
to server (after processing it), in registers the image
size in out data file.
Algorithm 1 describes the steps for setting up the
testbed. The launcher runs code that sets the network
topology and triggers applications in the other ma-
chines, making remote call with ssh. These applica-
tions remove files generated in the previous test and
also collect the metrics for the current test, according
to the current topology. Once all applications are run-
ning, the launcher waits for the test to be completed,
collects all metric files and generates the analysis.
There are two environments considered: wired
and wireless. The first one is used as a reference for
system architecture validation and the second one is
the target environment. We set the sending rate based
on preliminary network analysis that showed that the
network could only handle one image per second. We
repeat each test ten times. For both test environments,
we used two test loads: no movement and movement.
The first one gives the results for the best case sce-
nario for the application, since it implies intensive use
of compression function. The second one gives re-
sults on the worst case scenario.
5.3 Results
Table 3 and 4 display the results from the tests per-
formed on the wired and wireless environments, re-
spectively. The tables contain all the average latency
for each operation, the total latency and the bandwidth
In both environments, the latency values for the
two topologies proposed are similar, even when com-
pared among different test loads. The PN introduced
about 2.37 seconds of latency in the wired environ-
ment and 5.12 seconds in the wireless, but only 0.03
seconds have been spent with compression. The rest
of the time has been spent with disk operations, such
as move and copy within the PN.
Most of the time operations was spent with image
transmission: before the compression starts, the im-
age transfer must be completed, and before the image
can be sent to the final destination, the image com-
pression must be complete. These rules are imple-
mented through the mover time-based differentiation
Algorithm 1: Test execution algorithm.
1: for each scenario in scenarios list do
2: SETUP TOPOLOGY(scenario)
3: for i 0 to n repetitions do
5: for each host in remote hosts do
6: CLEAN(host, images f older)
7: CLEAN(host, metrics f olders)
8: RUN PROGRAMS(scenario, host)
9: end for
10: SLEEP(test duration)
11: for each host in remote hosts do
12: COLLECT DATA(host)
13: end for
15: end for
16: end for
CLOSER 2017 - 7th International Conference on Cloud Computing and Services Science
Table 2: Timestamps description.
Timestamp Host Event Program
T1 Source Image is created. photo
T2 BS Receives packet requesting FTP transfer for
server., read
T3 BS Redirects packet requesting FTP transfer to PN., read
T4 PN Start receiving the photo in its FTP income folder.
T4A PN Mover is called to move the image from the in-
come folder to optimizer input folder.
T5 PN Image is transferred for optimizer input folder.
T5A PN Optimizer starts the optimization process. camera
T5B PN Optimizer finishes the optimization process. camera
T6 PN Image is transferred to optimizer output folder.
T6A PN Mover is called to start FTP transfer to BS (topol-
ogy 1) or server (topology 2).
T7 Server Start receiving the image in its FTP income folder.
Table 3: Wired test results.
(in seconds) BS PN Server
Test Mov T2-T1 T3-T2 T4-T3 T4A-T4 T5-T4A T5A-T5 T5B-T5A T6-T5(B) T6A-T6 T7-T2(6A) Latency Gain
0 Yes 4.297 0.002 4.299 0
1A Yes 4.340 4.66E-6 0.001 1.046 0.003 0.003 1.006 0.185 6.584 0
1B Yes 4.338 4.53E-6 0.001 1.055 0.003 0.055 0.004 0.003 1.017 0.184 6.659 0.01
2A Yes 4.332 4.39E-6 0.003 1.057 0.003 0.003 1.011 0.185 6.593 0
2B Yes 4.338 4.02E-6 0.006 1.214 0.007 0.060 0.004 0.003 1.051 0.186 6.867 0.01
0 No 4.222 0.002 4.224 0
1A No 4.267 4.61E-6 0.000 1.045 0.003 0.003 1.007 0.185 6.509 0
1B No 4.269 4.59E-6 0.013 1.134 0.004 0.057 0.004 0.000 1.030 0.184 6.695 0.97
2A No 4.265 4.51E-6 0.004 1.074 0.003 0.003 1.015 0.183 6.548 0
2B No 4.262 4.03E-6 0.002 1.060 0.002 0.054 0.004 0.000 1.021 0.184 6.588 0.96
The sending rate was set to one image per second
in both environments. The wired network was able
to handle this rate and the images were moved at al-
most the same rate they were created. On the other
hand, the wireless network was only able to handle
one image every 2.34 seconds, approximately, which
resulted in higher latency value. Table 5 shows the
comparison between the two environments.
Figure 4 shows the time ratio for each operation
inside of the PN (application of the compression al-
gorithm over data). As mentioned before, the main
cause for the latency was the characteristic of our so-
lution, which creates a dependency between the la-
tency and the image receiving interval.
Although the system showed a latency insertion in
the data transmission, it also presented a major benefit
in the bandwidth consumption. Considering the best
scenario, where no movement was detected, in com-
parison to the traditional scenario the compression
function resulted in about 97% of bandwidth gain.
Time ratio
Figure 4: Processing node time composition.
In this paper we presented the FACE framework,
a fog-computing strategy to reduce bandwidth con-
sumption. It consists of data processing functions
Towards Bandwidth Optimization in Fog Computing using FACE Framework
Table 4: Wireless test results.
(in seconds) BS PN Server
Test Mov T2-T1 T3-T2 T4-T3 T4A-T4 T5-T4A T5A-T5 T5B-T5A T6-T5(B) T6A-T6 T7-T2(6A) Latency Gain
0 Yes 4.519 0.001 4.520 0
1A Yes 4.725 4.91E-6 0.000 2.175 0.002 0.002 2.164 0.175 9.244 0
1B Yes 4.755 5.17E-6 0.001 2.494 0.002 0.049 0.003 0.003 2.466 0.172 9.945 0.01
2A Yes 4.778 4.90E-6 0.001 2.514 0.003 0.003 2.507 0.172 9.978 0
2B Yes 4.788 5.01E-6 0.002 2.567 0.002 0.048 0.004 0.003 2.545 0.172 10.130 0.01
0 No 4.460 0.001 4.461 0
1A No 4.656 5.29E-6 0.001 2.076 0.002 0.003 2.064 0.176 8.978 0
1B No 4.693 5.10E-6 0.000 2.306 0.002 0.049 0.005 0.000 2.288 0.172 9.516 0.97
2A No 4.710 5.12E-6 0.001 2.236 0.002 0.003 2.227 0.174 9.353 0
2B No 4.782 5.08E-6 0.001 2.386 0.003 0.047 0.004 0.000 2.377 0.174 9.774 0.96
Table 5: Environment comparison.
Environment BS to PN (s) PN (s) PN to server (s) Total (s) Difference (s) Gain
Wired 0.000 2.192 0.184 2.376 0.113 96.50%
Wireless 0.000 4.735 0.173 4.907 0.425 96.50%
(filtering, aggregation, compression, and extraction)
performed in the edges of the network, to reduce the
data traffic from the end user until a central location.
Each function is applied for a different purpose, and
aims at meeting requirements of application service
providers. To evaluate the performance of the frame-
work, we designed an architecture that allows to ap-
ply compression function over the data between an
end device and a cloud server. Results evaluations
have identified savings in network infrastructure at
the cost of latency increase. The developed prototype
showed bandwidth gain in video surveillance when
image compression is applied in cases when the scene
does not change from one frame to another.
The tests related to the video surveillance scenario
showed that most of the latency introduced by the pro-
cessing node comes from the time spent in the image
transmission. The processing node holds the image
until it has completed the transmission, so it can be
processed and then forwarded. For applications that
are not latency-sensitive, this solution can be applied
to promote savings in network infrastructure. If ap-
plied to an environment with dynamic scene changes,
the advantages of using this solution are substantially
reduced. Other compression rules can also be applied
to fit custom application requirements.
Next steps consist of further tests to investigate
how the latency could be improved. A set of tests
could be applied to a real video surveillance scenario,
using more cameras that continuously send data. It
would also be interesting to evaluate the architecture
on an infrastructure with telecommunication compo-
nents, such as base stations and small cells. Since the
mover mechanism was the main cause of latency, it is
important to study how to optimize it, by reducing the
time it takes to start sending images. This achieve-
ment would result in a significant latency decrease,
and then provide a more effective solution. We also
plan to evaluate the other functions provided by FACE
framework in scenarios where data traffic is intensive.
This work was sponsored by the Ericsson Innovation
Center, Ericsson Telecomunicac¸
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CLOSER 2017 - 7th International Conference on Cloud Computing and Services Science