Browse > Article
http://dx.doi.org/10.7472/jksii.2017.18.1.01

A Task Offloading Approach using Classification and Particle Swarm Optimization  

Mateo, John Cristopher A. (Dept. of Information and Communication Engineering, Kunsan National University)
Lee, Jaewan (Dept. of Information and Communication Engineering, Kunsan National University)
Publication Information
Journal of Internet Computing and Services / v.18, no.1, 2017 , pp. 1-9 More about this Journal
Abstract
Innovations from current researches on cloud computing such as applying bio-inspired computing techniques have brought new level solutions in offloading mechanisms. With the growing trend of mobile devices, mobile cloud computing can also benefit from applying bio-inspired techniques. Energy-efficient offloading mechanisms on mobile cloud systems are needed to reduce the total energy consumption but previous works did not consider energy consumption in the decision-making of task distribution. This paper proposes the Particle Swarm Optimization (PSO) as an offloading strategy of cloudlet to data centers where each task is represented as a particle during the process. The collected tasks are classified using K-means clustering on the cloudlet before applying PSO in order to minimize the number of particles and to locate the best data center for a specific task, instead of considering all tasks during the PSO process. Simulation results show that the proposed PSO excels in choosing data centers with respect to energy consumption, while it has accumulated a little more processing time compared to the other approaches.
Keywords
Cloudlet; Classification; Particle Swarm Optimization; Mobile Cloud Computing;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Dillon, T., Wu, C., Chang, E., "Cloud computing: issues and challenges", Advanced Information Networking and Applications (AINA), 24th IEEE International Conference, 2010. http://dx.doi.org/10.1109/AINA.2010.187.   DOI
2 Delforge, P., "America's Data Centers Consuming and Wasting Growing Amounts of Energy", Natural Resource Defense Council, August 2014. https://www.nrdc.org/resources/americas-data-centers-consuming-and-wasting-growing-amounts-energy.
3 Warkehar, P., Gaikawad, V. T., "Mobile Cloud Computing, Approaches and Issues", International Journal of Emerging Trends & Technology in Computer Science, vol. 2, issue, March - April 2013. http://dx.doi.org/10.1016/j.simpat.2014.05.009.   DOI
4 Satyanarayanan, M., Bahl, P., Caccres, R., Davies, N., "The Case for VM-Based Cloudlets in Mobile Computing", IEEE Pervasive Computing, vol. 8, issue 4, pp. 14-23, 2009. http://dx.doi.org/10.1109/MPRV.2009.82.   DOI
5 Satyanarayanan, M., Bahl, P., Caccres, R., Davies, N., "The Case for VM-Based Cloudlets in Mobile Computing", IEEE Pervasive Computing, vol. 8, issue 4, pp. 14-23, 2009. http://dx.doi.org/10.1109/MPRV.2009.82.   DOI
6 Awad, A. I., El-Hefnawy, N. A., Abdel-kader, H. M., "Enhanced Particle Swarm Optimization for Task Scheduling in Cloud Computing Environments", International Conference on Communication, Management and Information Technology, vol. 65, pp. 920-929, 2015. http://dx.doi.org/10.1016/j.procs.2015.09.064.   DOI
7 Pandey, S., Wu, L., Guru, S. M., Buyya, R., "A Particle Swarm Optimization-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments", 24th IEEE International Conference on Advanced Information Networking and Applications, pp. 400-407, 2010. http://doi.ieeecomputersociety.org/10.1109/AINA.2010.31.   DOI
8 Yin, Y., Yu, S., Wang, P., Wang, Y., "A hybrid particle swarm optimization algorithm for optimal task assignment in distributed systems", Computer Standards & Interfaces, vol. 28, issue 4, pp. 441-450, 2006. http://dx.doi.org/10.1016/j.csi.2005.03.005.   DOI
9 Baby, A., "Load Balancing in Cloud Computing Environment using PSO Algorithm", International Journal for Research in Applied Science and Engineering Technology, vol. 2, issue 4, April 2014. http://www.ijraset.com/fileserve.php?FID=349.
10 Al-maamari, A., Omara, F. A.," Task Scheduling using PSO Algorithm in Cloud Computing Environments", International Journal of Grid Computing, vol. 8, no. 5, pp. 245-256, 2015. http://www.sersc.org/journals/IJGDC/vol8_no5/24.pdf.
11 Nirubah, T. J., John, R. R., "Energy-Efficient Task Scheduling Algorithms for Cloud Data Centers", International Journal of Research in Engineering and Technology, vol. 3, issue 3, March 2014. http://esatjournals.net/ijret/2014v03/i03/IJRET20140303 059.pdf.
12 Standard Performance Evaluation Corporation, http://www.spec.org/
13 Gu, L., Zeng, D., Barnawi, A., Guo, S., Stojmenovic, I., "Optimal Task Placement with QoS Constraints in Geo-distributed Data Centers using DVFS", IEEE Transactions on Computers, vol. 64, no. 7, pp. 2049-2059, 2015. http://dx.doi.org/10.1109/TC.2014.2349510.   DOI
14 Soyata, T., Muraleedharan, R., Funai, C., Kwon, M., Heinzelman, W., "Cloud-Vision: Real-time Face Recognition using a Mobile-Cloudlet-Cloud Acceleration Architecture", International Symposium on Computers and Communications, July 2012. http://dx.doi.org/10.1109/ISCC.2012.6249269.   DOI
15 Soyata, T., Muraleedharan, R., Langdon, J., Funai, C., Ames, S., Kwon, M., Heinzelman, W., "COMBAT: mobile-Cloud-based cOmpute/coMmunications infrastructure for BATtlefield applications", Modeling and Simulation for Defense Systems and Applications vol. 7, 2012. https://www.cs.rit.edu/-jmk/papers/combat-spie.pdf.
16 Panchal, B., Kapport, R. K., "Dynamic VM Allocation algorithm using Clustering in Cloud Computing", International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, issue 9, 2013. https://www.ijarcsse.com/docs/papers/Volume_3/9_Septe mber2013/V3I9-0119.pdf.
17 Kordinariya, T. M., Makwana, P. R., "Review on determining number of Cluster in K-means Clustering", International Journal of Advance Research in Computer Science and Management Studies, vol. 1, issue 6, November 2013. http://www.academia.edu/5514429/Review_on_determining_number_of_Cluster_in_K-Means_Clustering.