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Towards Real-Time Analytics for Mobile Big Data using the Edge Computing Ibrar Yaqoob, Umer Majeed, and Choong Seon Hong Department of Computer Engineering, Kyung Hee University, Yongin, 446-701 Korea {ibraryaqoob, umermajeed, cshong}@khu.ac.kr Abstract The extraordinary proliferation of digital devices has led to the production of enormous amounts of heterogeneous data called mobile big data (MBD). However, obtaining insights into the flood of MBD in a real-time manner has become a crucial issue. Coping with this issue, we propose a theoretical framework based on the edge computing, which envisions bringing computation closer to the user’s proximity. The proposed framework comprises three layers, where each layer is dependent upon the layers above it. The qualitative analysis results reveal the proposed framework can enable real-time analytics for the business environments. 1. Introduction: Recent years have witnessed an astonishing rate of increase in spatiotemporal network-level data generated by broadband mobile networks, known as mobile big data (MBD). MBD analytics is a process of finding meaningful patterns and extracting knowledge from the flood of mobile users’ data collected at the network- level or the application-level [1]. It is reported in [2], currently, over five billion people are using smartphones. In addition, it is predicted that 50 billion devices will be connected to the Internet by the end of 2020 (44 times greater than that in 2009). On the other hand, mobile application vendors are collecting massive amounts of data implicitly or explicitly using crowdsourcing schemes. Figure 1 shows the growth rate of mobile data traffic and the number of mobile phone Internet users, which are putting an intolerable pressure on the network systems [2]. The massive amount of data is currently mostly being processed and analyzed in the central cloud, which will be inadequate in the future because of the latency issues [3][4]. Thus, a necessity is arising to process and analyze the data in a real-time manner. This study aims to address this problem by proposing a framework based on the edge computing concept which envisions bringing computation closer to the user’s proximity. The contributions of this study are two folds: (1) We investigate the recent research works directed at the MBD era in terms of analytics. (2) We propose a framework for enabling real-time analytics for MBD. These contributions are provided in the separate sections from 2-3. We summarize our study in section 4. Figure 1: The growth rate of mobile data traffic and number of mobile phone Internet users [2] 338 2018년 한국컴퓨터종합학술대회 논문집

Transcript of d}Á Z o d]u voÇ ] (} D} ]o ]P µ ]vP Z P }u µ...

Page 1: d}Á Z o d]u voÇ ] (} D} ]o ]P µ ]vP Z P }u µ ]vPnetworking.khu.ac.kr/layouts/net/publications/data/KCC...analytics in a real-time manner is one of the remaining concerns. In [6],

Towards Real-Time Analytics for Mobile Big Data using the Edge Computing

Ibrar Yaqoob, Umer Majeed, and Choong Seon Hong

Department of Computer Engineering, Kyung Hee University, Yongin, 446-701 Korea

{ibraryaqoob, umermajeed, cshong}@khu.ac.kr

Abstract

The extraordinary proliferation of digital devices has led to the production of enormous

amounts of heterogeneous data called mobile big data (MBD). However, obtaining insights

into the flood of MBD in a real-time manner has become a crucial issue. Coping with this issue,

we propose a theoretical framework based on the edge computing, which envisions bringing

computation closer to the user’s proximity. The proposed framework comprises three layers,

where each layer is dependent upon the layers above it. The qualitative analysis results reveal

the proposed framework can enable real-time analytics for the business environments.

1. Introduction:

Recent years have witnessed an astonishing rate of

increase in spatiotemporal network-level data generated

by broadband mobile networks, known as mobile big

data (MBD). MBD analytics is a process of finding

meaningful patterns and extracting knowledge from the

flood of mobile users’ data collected at the network-

level or the application-level [1]. It is reported in [2],

currently, over five billion people are using smartphones.

In addition, it is predicted that 50 billion devices will be

connected to the Internet by the end of 2020 (44 times

greater than that in 2009). On the other hand, mobile

application vendors are collecting massive amounts of

data implicitly or explicitly using crowdsourcing

schemes. Figure 1 shows the growth rate of mobile

data traffic and the number of mobile phone Internet

users, which are putting an intolerable pressure on the

network systems [2]. The massive amount of data is

currently mostly being processed and analyzed in the

central cloud, which will be inadequate in the future

because of the latency issues [3][4]. Thus, a necessity is

arising to process and analyze the data in a real-time

manner. This study aims to address this problem by

proposing a framework based on the edge computing

concept which envisions bringing computation closer to

the user’s proximity.

The contributions of this study are two folds: (1) We

investigate the recent research works directed at the

MBD era in terms of analytics. (2) We propose a

framework for enabling real-time analytics for MBD.

These contributions are provided in the separate

sections from 2-3. We summarize our study in section 4.

Figure 1: The growth rate of mobile data traffic and number of mobile phone Internet users [2]

338

2018년 한국컴퓨터종합학술대회 논문집

Page 2: d}Á Z o d]u voÇ ] (} D} ]o ]P µ ]vP Z P }u µ ]vPnetworking.khu.ac.kr/layouts/net/publications/data/KCC...analytics in a real-time manner is one of the remaining concerns. In [6],

2. Recent Advances:

This section critically investigates the recent studies

conducted on the MBD era in terms of analytics.

The study [5] has proposed a scalable Spark-based

framework for enabling deep learning in MBD analytics.

The framework helps to add value by learning essential

characteristics of the collected raw MBD. The framework

was validated through a large-scale system. Although

the study focuses on MBD analytics, how to perform

analytics in a real-time manner is one of the remaining

concerns.

In [6], an anomaly detection solution based on machine

learning technique was presented for MBD. The solution

analyzes the users’ activities at the different time and

location through k-means and hierarchical clustering

techniques. The user activities causing unusual high

traffic were considered as anomalies. These anomalies

were verified with the ground truth information, i.e.,

there was a soccer match at that location and thus the

traffic rate was very high than on the usual days.

Detecting the anomalies in an advanced manner can

lead towards an efficient resource allocation. The study

further revealed that utilization of big data analytics

solutions can help to analyze the users’ contextual

information, such as mobility pattern, traffic pattern, to

name a few. The extraction of such information helps in

developing intelligent resource allocation algorithms

which result in the efficient resource utilization. Despite

many advantages of the presented work, the solution

overlooks the high latency issue while extracting

invaluable information from MDB.

The work [7] has demonstrated that machine learning

algorithms can help to identify interference issues in 4.9

and fifth generation mobile networks. The networks are

collecting billions of measurement samples used for

resource allocation and link adaptation. These

measurements provide details about the service quality

experienced by the customers at their locations, thus,

applying big data analytics methods can help to extract

the useful information. Later on, optimization methods

can be applied based on the extracted information that

can help to lower the interference levels and improve

the throughputs and data rates in the mobile networks.

Although this study helps to fix several indispensable

issues in the mobile networks, the study does not focus

on how the massive amounts of network generated

data can be analyzed in a real-time manner.

The concept of employing edge computing for the

Internet of things (IoT) was introduced in [8]. In this

context, this study has proposed a new architecture

called GigaSight, which is a hybrid cloud architecture

that employs a decentralized cloud computing platform

in the form of cloudlets. The study envisions utilizing

data analytics techniques on the cloudlets, which are

near to the user proximity (only the end results along

with the metadata are required to send to the cloud).

Although this work enables real-time analytics for the

IoT paradigm, the work is in its infancy.

Unlike the above-mentioned works, we propose a

theoretical framework, which envisions enabling real-

time analytics for MBD using the edge computing.

3. Proposed Framework:

Figure 2 illustrates the proposed framework which can

enable the real-time analytics for MBD by performing

computation closer to the user’s network.

The framework is comprised three layers. The first layer

is responsible for collecting and transmitting the data to

the above layer, whereas, the second layer is

responsible for applying different data analytics tools

and techniques at the edge of the network to extract

the meaningful information and expose the hidden

patterns which help the mobile network operators to

improve the network efficiency. Additionally, the second

layer is responsible for transmitting meta-data to the

cloud. The third layer manages the results along with

meta-data, i.e., data ID, Edge device IP address,

owner/company details, location, and time stamp

information. The proposed framework can provide

certain benefits as follow:

(a) The proposed framework can help in reducing the

time of data analytics. However, in the typical

339

2018년 한국컴퓨터종합학술대회 논문집

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environment, the data need to be processed in the

central cloud, which prolongs the data analytics time.

(b) The second benefit of the framework is scalability of

the analytics. (c) The framework reduces bandwidth

costs. (d) Performing analytics at the edge for MBD can

ensure high privacy and security as devices are in the

user’s proximity.

Decision automation

Business intelligence applications Mobile big data analytics

Security

Cellular network

Ed

ge of the N

etwo

rk

[9]

Data driven businessBusiness performance

managementPredictive analysis

LocationOwner/Company

Mob

ile Big

Data

So

urces

Clou

d

Wireless local area network

Edge device IP

AddressData ID Time stamp

Figure 2: An illustration of the proposed framework

4. Summary:

Tremendous advancements in smartphone technology

have enabled the people to share their data, resulted in

the MBD era. Collected MBD is unprofitable unless

some appropriate analytics techniques are utilized to

extract meaningful information and expose the hidden

patterns in a real-time manner. In this paper, we initially

explored and critically analyzed the recent advances

made in the MBD era in terms of analytics. Then, we

proposed a theoretical framework which helps in

enabling real-time analytics for MBD. In the future, we

are planning to implement and validate our framework

using a large-scale system.

Acknowledgment:

This research was supported by the MSIT (Ministry of

Science and ICT), Korea, under the Grand Information

Technology Research Center support program (IITP-

2018-2015-0-00742) supervised by the IITP (Institute for

Information & communications Technology Promotion).

*Dr. CS Hong is the corresponding author.

[References]

[1] Yazti, Demetrios Zeinalipour, and Shonali Krishnaswamy,"IEEE 15th International Conference on Mobile Data Management, Brisbane, QLD, 2014, Vol.1, pp. 1-2.

[2] Ahmed, Ejaz, et al. "Recent Advances and Challenges in Mobile Big Data." IEEE Communications Magazine 56.2 (2018): 102-108.

[3] Su, Zhou, Qichao Xu, and Qifan Qi. "Big data in mobile social networks: A QoE-oriented framework." IEEE Network 30.1 (2016): 52-57.

[4] Wang, Kun, et al. "Mobile big data fault-tolerant processing for ehealth networks." IEEE Network 30.1 (2016): 36-42.

[5] Alsheikh, Mohammad Abu, et al. "Mobile big data analytics using deep learning and apache spark." IEEE Network 30.3 (2016): 22-29.

[6] Parwez, Md Salik, Danda B. Rawat, and Moses Garuba. "Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network." IEEE Transactions on Industrial Informatics 13.4 (2017): 2058-2065.

[7] Chiu, Peter, et al. "Big Data Analytics for 4.9G and 5G Mobile Network Optimization," IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, NSW, 2017, pp. 1-4.

[8] Satyanarayanan, Mahadev, et al. "Edge analytics in the internet of things." IEEE Pervasive Computing 14.2 (2015): 24-31.

[9] Hashem, Ibrahim Abaker Targio, et al. "The role of big data in smart city." International Journal of Information Management 36.5 (2016): 748-758.

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2018년 한국컴퓨터종합학술대회 논문집


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