Resource usage optimization in Mobile Cloud Computing

Computer Communications

Volumn 99, Issue , 2017, Pages 1-12

  Nawrocki, Piotr ^a   Reszelewski, Wojciech ^a  

^a AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Poland)

Author keywords

Asynchronous processing; Load balancing; Mobile cloud computing; Multi tenancy; Resource usage

Indexed keywords

ARCHITECTURE; BALANCING; COMPUTER ARCHITECTURE; RESOURCE ALLOCATION;

ARCHITECTURAL PATTERN; ASYNCHRONOUS PROCESSING; ASYNCHRONY; MOBILE CLOUDS; MULTI TENANCIES; RESOURCE DEMANDS; RESOURCE USAGE;

MOBILE CLOUD COMPUTING;

EID: 85008156096     PISSN: 01403664     EISSN: 1873703X     Source Type: Journal    
DOI: 10.1016/j.comcom.2016.12.009     Document Type: Article

References (31)

1. [1] T. Danova, The smartphone report by country: Adoption, platform, and vendor trends in major mobile markets around world, 2015, http://www.businessinsider.com/, Accessed: 2016-03-02.
2. [2] Kovachev, D., Cao, Y., Klamma, R., Mobile cloud computing: a comparison of application models. CoRR, abs/1107.4940, 2011 URL http://arxiv.org/abs/1107.4940.
3. [3] C. Hoffman, Ubuntu may beat windows 10 to phone-pc convergence after all, 2015, http://www.pcworld.com/article/2919552/ubuntu-may-beat-windows-10-to-phone-pc-convergence-after-all.html, Accessed: 2016-03-02.
4. [4] Lu, Y., Xie, Q., Kliot, G., Geller, A., Larus, J., Greenberg, A., Join-idle-queue: a novel load balancing algorithm for dynamically scalable web services. The 29th International Symposium on Computer Performance, Modeling, Measurements and Evaluation, 2011, Elsevier, 10.1016/j.peva.2011.07.015 Best Paper Award - http://event.cwi.nl/performance2011/bestpaperawards.html, URL http://research.microsoft.com/apps/pubs/default.aspx?id=153348.
5. [5] Nakrani, S., Tovey, C., On honey bees and dynamic server allocation in internet hosting centers. Adap. Behav. 12:3,4 (2004), 223–240, 10.1177/105971230401200308.
6. [6] Cuervo, E., Balasubramanian, A., Cho, D.-k., Wolman, A., Saroiu, S., Chandra, R., Bahl, P., MAUI: making smartphones last longer with code offload. MobiSys'10 Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, 2010, ACM, 49–62, 10.1145/1814433.1814441.
7. [7] Kemp, R., Palmer, N., Kielmann, T., Bal, H., Cuckoo: a computation offloading framework for smartphones. Mobile Computing, Applications, and Services., 2012, 10.1007/978-3-642-29336-8_4.
8. [8] Kovachev, D., Yu, T., Klamma, R., Adaptive computation offloading from mobile devices into the cloud. Proceedings of the 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications, 2012, IEEE Computer Society, 784–791, 10.1109/ISPA.2012.115.
9. [9] Kosta, S., Aucinas, A., Hui, P., Mortier, R., Zhang, X., ThinkAir: dsynamic resource allocation and parallel execution in the cloud for mobile code offloading. INFOCOM, 2012 Proceedings IEEE, 2012, IEEE, 945–953, 10.1109/INFCOM.2012.6195845.
10. [10] Chun, B.-G., Ihm, S., Maniatis, P., Naik, M., Patti, A., CloneCloud: elastic execution between mobile device and cloud. Proceedings of the Sixth Conference on Computer Systems, 2011, ACM, 301–314, 10.1145/1966445.1966473.
11. [11] Flores, H., Srirama, S., Adaptive code offloading for mobile cloud applications: exploiting fuzzy sets and evidence-based learning. Proceeding of the Fourth ACM Workshop on Mobile Cloud Computing and Services, 2013, ACM, 9–16, 10.1145/2482981.2482984.
12. [12] Flores, H., Hui, P., Tarkoma, S., Li, Y., Srirama, S., Buyya, R., Mobile code offloading: from concept to practice and beyond. IEEE Commun. Mag. 53:3 (2015), 80–88 URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7060486&filter%3DAND%28p_IS_Number%3A7060469%29. doi: 10.1109/MCOM.2015.7060486.
13. [13] Madhavapeddy, A., Singh, S., Reconfigurable data processing for clouds. Field-Programmable Custom Computing Machines (FCCM), 2011 IEEE 19th Annual International Symposium on, 2011, 141–145, 10.1109/FCCM.2011.35.
14. [14] Bachara, P., Brzoza-Woch, R., Długopolski, J., Nawrocki, P., Ruta, A., Zaborowski, W., Zieliński, K., Construction of hardware components for the internet of services. Comput. Inf., 34(4), 2015.
15. [15] Brzoza-Woch, R., Nawrocki, P., Fpga-based web services – infinite potential or a road to nowhere?. IEEE Internet Comput. 20:1 (2016), 44–51, 10.1109/MIC.2015.23.
16. [16] J. Varia, Architecting for the cloud: best practices, 2011, http://www.lifted-llc.com/docs/AWS_Cloud_Architecture_Best_Practices.pdf, Accessed: 2016-03-18.
17. [17] Flores, H., Srirama, S., Mobile cloud middleware. J. Syst. Softw. 92 (2014), 82–94, 10.1016/j.jss.2013.09.012 URL http://www.sciencedirect.com/science/article/pii/S0164121213002318.
18. [18] Yadav, A., Joshi, L.M., Verma, A., Virtual machine load balancing: state-of-the-art and its research challenges. Int. J. Comput. Appl. 119:15 (2015), 9–11, 10.5120/21141-4156.
19. [19] Ahmad, M., Cheema, I., Prognostic load balancing strategy for latency reduction in mobile cloud computing. Middle-East J. Sci. Res. 6 (2013), 805–813, 10.5829/idosi.mejsr.2013.16.06.11314 URL http://www.idosi.org/mejsr/mejsr16%286%2913/9.pdf.
20. [20] Lozano, J.A., Lorido-Botran, T., Miguel-Alonso, J., A review of auto-scaling techniques for elastic applications in cloud environments. J. Grid Comput. 12:4 (2014), 559–592, 10.1007/s10723-014-9314-7.
21. [21] P. Koperek, W. Funika, Dynamic Business Metrics-driven Resource Provisioning in Cloud Environments, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 171–180. doi: 10.1007/978-3-642-31500-8_18.
22. [22] Gambi, A., Toffetti, G., Modeling cloud performance with kriging. Proceedings of the 34th International Conference on Software Engineering ICSE'12, 2012, IEEE Press, Piscataway, NJ, USA, 1439–1440 http://dl.acm.org/citation.cfm?id=2337223.2337441.
23. [23] Han, R., Ghanem, M.M., Guo, L., Guo, Y., Osmond, M., Enabling cost-aware and adaptive elasticity of multi-tier cloud applications. Future Gener. Comput. Syst. 32 (2014), 82–98, 10.1016/j.future.2012.05.018.
24. [24] Dutta, S., Gera, S., Verma, A., Viswanathan, B., Smartscale: automatic application scaling in enterprise clouds. Proceedings of the 2012 IEEE Fifth International Conference on Cloud Computing CLOUD'12, 2012, IEEE Computer Society, Washington, DC, USA, 221–228, 10.1109/CLOUD.2012.12.
25. [25] Barrett, E., Howley, E., Duggan, J., Applying reinforcement learning towards automating resource allocation and application scalability in the cloud. Concurr. Comput. 25:12 (2013), 1656–1674, 10.1002/cpe.2864.
26. [26] Merkel, D., Docker: lightweight linux containers for consistent development and deployment. Linux J., 2014(239), 2014 http://dl.acm.org/citation.cfm?id=2600239.2600241.
27. [27] K. Fromm, Why the future of software and apps is serverless, 2012, URL http://readwrite.com/2012/10/15/why-the-future-of-software-and-apps-is-serverless/.
28. [28] Felter, W., Ferreira, A., Rajamony, R., Rubio, J., An updated performance comparison of virtual machines and linux containers. Performance Analysis of Systems and Software (ISPASS), 2015 IEEE International Symposium on, 2015, 171–172, 10.1109/ISPASS.2015.7095802.
29. [29] Manu, A.R., Patel, J.K., Akhtar, S., Agrawal, V.K., Murthy, K.N.B.S., Docker container security via heuristics-based multilateral security-conceptual and pragmatic study. 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), 2016, 1–14, 10.1109/ICCPCT.2016.7530217.
30. [30] Bui, T., Analysis of docker security. CoRR, 2015 URL http://arxiv.org/abs/1501.02967.
31. [31] M. Roberts, Serverless architectures, 2016. URL http://martinfowler.com/articles/serverless.html.