Compute Intensive Code Offloading in
Mobile Device Cloud
IEEE TENCON 2016 — Technologies for Smart Nation
Paper ID # 533
Authors
Sajeeb Saha, Md. Ahsan Habib, Md. Abdur Razzaque
Green Networking Research Group
Department of Computer Science and Engineering,
University of Dhaka, Dhaka-1000, Bangladesh
2
•
Introduction
•
Research Challenges
•
Contributions of this Work
•
State of the art Solutions
•
Code Offloading Architecture
•
Code Offloading Decision
•
Experimental Testbed
•
Simulation Results
•
Conclusion
Dept. of Computer Science and Engineering, University of Dhaka
3
Applications
-Visual Text Translation
- Image Processing
- Face Recognition
- Sensing and Monitoring
- Reality Augmentation
- Real time Multimedia
Dept. of Computer Science and Engineering, University of Dhaka
4
Constraints
- CPU Power
- Memory Size
- Storage Space
- Limited Bandwidth
- Battery
Lifetime
Problems
- Delay
- Energy
- QoS
Dept. of Computer Science and Engineering, University of Dhaka
5
• Migrate computationally intensive software modules
to higher end devices
• Reduce energy consumption or response time
Dept. of Computer Science and Engineering, University of Dhaka
6
Cloud Execution
-Components
are
offloaded to remote
server
-Remote server does the
Processing
Constraints
- Higher Latency
- Network Disruption
Dept. of Computer Science and Engineering, University of Dhaka
7
Cloudlet Execution
-The device offloads the
task to a nearby cloudlet.
Constraints
- Resource contension
- Limited resource
Dept. of Computer Science and Engineering, University of Dhaka
8
MDC Execution
-Offloading to
mobile devices.
nearby
Motivation
- Device idle resource
Dept. of Computer Science and Engineering, University of Dhaka
9
• Apportion of code to be offloaded
• Selection of devices
• Maintenance of connectivity among mobile devices
• To ensure device availability
Dept. of Computer Science and Engineering, University of Dhaka
10
• An algorithm for assigning application modules to
available mobile devices
• A mobile application as a emulation testbed
• Comparative analysis to show the effectiveness of
the proposed algorithm
Dept. of Computer Science and Engineering, University of Dhaka
11
Dept. of Computer Science and Engineering, University of Dhaka
12
• A powerful job structure to speedup computing and
conserve energy.
• Provides an algorithm to disseminate tasks among mobile
devices.
Shi, Cong, et al. "Serendipity: enabling remote computing among intermittently connected mobile devices." Proceedings of the
thirteenth ACM international symposium on Mobile Ad Hoc Networking and Computing. ACM, 2012.
Dept. of Computer Science and Engineering, University of Dhaka
13
• An emulation testbed to quantify the potential gain of
offloading tasks.
• An MDC experimental platform for the assessment of MDCbased solutions.
Abderrahmen Mtibaa, Khaled A Harras, and Afnan Fahim. Towards computational offloading in mobile device clouds. Cloud
Computing Technology and Science (CloudCom), 2013 IEEE 5th International Conference on, volume 1, pages 331–338. IEEE.
Dept. of Computer Science and Engineering, University of Dhaka
14
• An
architecture
for
computational offloading
including cloud, cloudlet
and mobile device
• Minimizes response time
and
energy
while
maximizing
network
lifetime.
Abderrahmen Mtibaa, Khaled A Harras, Karim Habak, Mostafa Ammar, and Ellen W Zegura. Towards mobile opportunistic
computing. In 2015 IEEE 8th International Conference on Cloud Computing, pages 1111–1114. IEEE, 2015.
Dept. of Computer Science and Engineering, University of Dhaka
15
No consideration of dependency between the
modules in parallel execution
Dept. of Computer Science and Engineering, University of Dhaka
16
• Mobile device as a cloud service provider (VM)
• Flexible amount of shareable resources
• A three tier architecture
Dept. of Computer Science and Engineering, University of Dhaka
17
List of Notations
M
Processing modules
Ctime
Level execution time
L
Total dependency levels
Donor device energy
Associativity time of the donor
B
Data rate between donor and cloudlet
etime
Execution time
ttime
Communication time
N
Total available devices
Dept. of Computer Science and Engineering, University of Dhaka
18
Energy Constraint
Bandwidth Constraint
Associativity Constraint
Dept. of Computer Science and Engineering, University of Dhaka
19
Device Parameters
Application Parameters
Dept. of Computer Science and Engineering, University of Dhaka
20
Dept. of Computer Science and Engineering, University of Dhaka
21
Impact on dependency levels
Dept. of Computer Science and Engineering, University of Dhaka
22
Impact on number of available devices
Dept. of Computer Science and Engineering, University of Dhaka
23
• An offloading decision making algorithm for
compute intensive mobile applications
• Algorithm outperforms the state-of-the-art works in
terms of execution time.
Dept. of Computer Science and Engineering, University of Dhaka
24
This work is supported by a grant for the ”Research
Fellowship (2015-2016)” funded by the Information
and Communication Technology Division, Ministry of
Posts,
Telecommunications
and
Technology, Government of Bangladesh.
Dept. of Computer Science and Engineering, University of Dhaka
Information
25
[1] M. Meeker. Internet trends 2015-code conference. Glokalde, 1(3), 2015.
[2] Mobile App Usage Statistics Overview.
http://www.statista.com/topics/1002/mobile-app-usage/, Access Date: 19/05/2016.
[3] Karthik Kumar, Jibang Liu, Yung-Hsiang Lu, and Bharat Bhargava. A survey of computation
offloading for mobile systems. Mobile Networks and Applications, 18(1):129–140, 2013.
[4] Mike Jia, Jiannong Cao, and Lei Yang. Heuristic offloading of concurrent tasks for
computation-intensive applications in mobile cloud computing. In Computer Communications
Workshops (INFOCOM WKSHPS), 2014 IEEE Conference on, pages 352–357. IEEE, 2014.
[5] Zixue Cheng, Peng Li, Junbo Wang, and Song Guo. Justin-time code offloading for wearable
computing. IEEE Transactions on Emerging Topics in Computing, 3(1):74–83, 2015.
[6] Mahbub E Khoda, Md Abdur Razzaque, Ahmad Almogren, Mohammad Mehedi Hassan, Atif
Alamri, and Abdulhameed Alelaiwi. Efficient computation offloading decision in mobile cloud
computing over 5g network. Mobile Networks and Applications, pages 1–16, 2016.
[7] Mahadev Satyanarayanan, Paramvir Bahl, Ram´ on Caceres, and Nigel Davies. The case for
vm-based cloudlets in mobile computing. IEEE pervasive Computing, 8(4):14–23, 2009.
Dept. of Computer Science and Engineering, University of Dhaka
26
[8] Abderrahmen Mtibaa, Khaled A Harras, and Afnan Fahim. Towards computational
offloading in mobile device clouds. In Cloud Computing Technology and Science (CloudCom),
2013 IEEE 5th International Conference on, volume 1, pages 331–338. IEEE, 2013.
[9] Cong Shi, Vasileios Lakafosis, Mostafa H Ammar, and Ellen W Zegura. Serendipity: enabling
remote computing among intermittently connected mobile devices. In Proceedings of the
thirteenth ACM international symposium on Mobile Ad Hoc Networking and Computing, pages
145–154. ACM, 2012.
[10] Abderrahmen Mtibaa, Khaled A Harras, Karim Habak, Mostafa Ammar, and Ellen W
Zegura. Towards mobile opportunistic computing. In 2015 IEEE 8th International Conference
on Cloud Computing, pages 1111–1114. IEEE, 2015.
[11] Luiz Andr´ e Barroso and Urs Holzle. The case for energyproportional computing.
Computer, 40(12):33–37, 2007.
Dept. of Computer Science and Engineering, University of Dhaka
27