Effectiveness Evaluation

In the experiment of the real network environment, quantity of users is added up incrementally in order to observe performances of system with different algorithms.

In Figure 29, the result of the experiment is in accordance with the experiment applying network delay correlation model as a way of generating network delay. Still, as far as SQF is concerned, mean response time gets raised by the growth of users on account of existence of incubation period phenomenon. Mean response time of system derived from most of the algorithms shows no significant difference when quantity of users remains in a low level. As quantity of users keeps growing, however, loading of system becomes heavier and as a result, mean response time of system gets higher when RR, Random, and LCF get stuck in the continuous congestion. While future congestion is predicted through historical data in SeeFuND, bursty traffic produced by incubation period phenomenon becomes more drastic as quantity of users grows, and it eventually increases mean response time. Meanwhile, F-SeeFuND is actually recognized to have a lower mean response time in the face of bursty traffic and continuous congestion in network. To sum up, this experiment meets the assumption that future congestion can be predicted through historical information and is consistent with the result of the previous experiment which uses network delay autocorrelation model to generate network delay. This experiment indirectly verifies the correctness of the network delay autocorrelation model. Therefore, according to this model, we are able to produce any number of users' network delay in various applications in the future.

45

Figure 29. Response Time with Different Loading in Real World Network

0 5 10 15 20 25

2 0 0 U S E R S 3 0 0 U S E R S 4 0 0 U S E R S 5 0 0 U S E R S 6 0 0 U S E R S 7 0 0 U S E R S

R es po ns e T im e (Seco nd)

Loading (Numver of Users)

Response Time w ith Different Loading in Real -w orld Netw ork

SeeFuND F-SeeFuND SQF LUF Random RR

46

CHAPTER 7

Conclusions and Future Work

In this thesis, we propose a Mobile Banking Messaging as a Service Framework to satisfy the demand of mobile financial service. With the coming of Bank 3.0, users can enjoy the financial service with their mobile devices everywhere. Because of the overwhelming need for network and system resources when the opening of stock markets or astonishing revelation in news happens, the task assignment policy proposed here can be implemented to solve the problems of the explosive need for network resources and unbalanced network.

To verify our algorithm, we proposed network delay autocorrelation model to simulate the realistic condition of network congestion, and we found that when faced with the problem of heavy congestion and explosive need for network resources, we could actually use this algorithm to improve the ability of the system to manage resource. Last but not least, we allocated user robots at datacenter worldwide to verify our experimental results, and the result obtained in the real condition is the same as our simulation result. Therefore, this research can indirectly verify the correctness of the network delay autocorrelation model and the effectiveness of our task assignment algorithm.

In the future, because cloud providers offer computer resources to users on pay-per-use, we will consider the user priority to accommodate the demands of different users. They may enjoy different levels of service quality.

On the other hand, using a fixed number virtual machine configuration may fail to deliver good quality of service when the number of incoming requests rapidly rises. It

47

may leave many virtual machines idle when the number of users is very small. In order to effectively handle a large number of incoming requests, we hope that the system can be auto scaled out before the system is overwhelmed.

48

REFERENCE

[1] KING, Brett. Bank 3.0: Why Banking Is No Longer Somewhere You Go But Something You Do. John Wiley & Sons, 2012.

[2] 陳欣昌. 下一步: 數位證券 3.0. 證券服務, 2014, 628: 9-14.

[3] KING, Brett. Breaking Banks: The Innovators, Rogues, and Strategists Rebooting Banking. John Wiley & Sons, 2014.

[4] 隨時隨地隨取的行動銀行創新實務分享, Available:

https://www.fisc.com.tw/Upload/727606e2-2b6d-450a-a519-950191c11010/TC/14.pdf

[5] SKINNER, Chris. Digital Bank: Strategies to launch or become a digital bank.

Marshall Cavendish International Asia Pte Ltd, 2014.

[6] 分秒必爭，期貨商砸錢提升 IT 系統, Available:

http://news.ltn.com.tw/news/business/paper/654740

[7] XV, Lingbo; MENG, Qingjun. Interference in and Ecological Strategies to Mobile Financial Services Developed by Commercial Banks. Open Journal of Social Sciences, 2015, 3.07: 194.

[12] Apple Push Notification Service, Available:

https://developer.apple.com/library/ios/documentation/NetworkingInternet/Conc eptual/RemoteNotificationsPG/Chapters/ApplePushService.html

[13] Parse, Available: https://parse.com/products/push

49

[14] LINE – Naver, Available: http://line.me/zh-hant/

[15] WhatsApp, Available: https://www.whatsapp.com/?l=zh_tw [16] 業界首見！日本 SBI 證券散戶可透過 Line 下單, Available:

http://ascii.jp/elem/000/000/923/923586/

[17] REALTIME, Ignite. Openfire Server, 2009.

[18] Connection Manager Module - Ignite Realtime: Openfire, Available:

http://www.igniterealtime.org/projects/openfire/connection_manager.jsp

[19] XU, Zhong; HUANG, Rong. Performance study of load balancing algorithms in distributed web server systems. CS213 Parallel and Distributed Processing Project Report, 2009, 1.

[20] A predictive modified round robin scheduling algorithm for web server clusters, 2015

[21] SIDHU, Amandeep Kaur; KINGER, Supriya. Analysis of load balancing techniques in cloud computing. International Journal of Computers & Technology, 2013, 4.2: 737-741.

[22] WANG, Weikun; CASALE, Giuliano. Evaluating Weighted Round Robin Load Balancing for Cloud Web Services. In: Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2014 16th International Symposium on. IEEE, 2014. p. 393-400.

[23] BUOT, Max. Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Journal of the American Statistical Association, 2006, 101.473: 395-396.

[24] FALLIS, Don. The reliability of randomized algorithms. The British journal for the philosophy of science, 2000, 51.2: 255-271.

50

[25] TENG, Sheng-bo; LIAO, Jian-xin; ZHU, Xiao-min. Dynamic weighted random load balancing algorithm for SIP application server. The Journal of China Universities of Posts and Telecommunications, 2009, 16.4: 67-70.

[26] CHOI, DongJun; CHUNG, Kwang Sik; SHON, JinGon. An Improvement on the Weighted Least-Connection Scheduling Algorithm for Load Balancing in Web Cluster Systems. In: Grid and Distributed Computing, Control and Automation.

Springer Berlin Heidelberg, 2010. p. 127-134.

[27] TEO, Yong Meng; AYANI, Rassul. Comparison of load balancing strategies on cluster-based web servers. Simulation, 2001, 77.5-6: 185-195.

[28] GUPTA, Varun, et al. Analysis of join-the-shortest-queue routing for web server farms. Performance Evaluation, 2007, 64.9: 1062-1081.

[29] GUILLEMIN, Fabrice; SIMONIAN, Alain. Analysis of the shortest queue first service discipline with two classes. In: Proceedings of the 7th International Conference on Performance Evaluation Methodologies and Tools. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2013. p. 1-10.

[30] KOKILAVANI, T.; AMALARETHINAM, Dr DI George. Load balanced min-min algorithm for static meta-task scheduling in grid computing. International Journal of Computer Applications, 2011, 20.2: 43-49.

[31] PATEL, Gaurang; MEHTA, Rutvik; BHOI, Upendra. Enhanced Load Balanced Min-min Algorithm for Static Meta Task Scheduling in Cloud Computing.

Procedia Computer Science, 2015, 57: 545-553. x

[32] BONALD, Thomas, et al. A queueing analysis of max-min fairness, proportional fairness and balanced fairness. Queueing systems, 2006, 53.1-2: 65-84.

51

[33] Ahmed El Rheddane, No¨el De Palma, Alain Tchana, Scalable Store and Forward Messaging. In: Cloud Computing (CLOUD), 2013 The Fourth International Conference on Cloud Computing, GRIDs, and Virtualization

[34] ZHANG, Lijun; ZHU, Qiuyu. The Overtime Waiting Model for Web Server Performance Evaluation. In: Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2014 International Conference on. IEEE, 2014.

p. 229-232.

[35] HARCHOL-BALTER, Mor. Performance Modeling and Design of Computer Systems: Queueing Theory in Action. Cambridge University Press, 2013.

[36] ZHANG, Lijun; ZHU, Qiuyu. The Overtime Waiting Model for Web Server Performance Evaluation. In: Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2014 International Conference on. IEEE, 2014.

p. 229-232.

[37] PRABHAVAT, Sumet, et al. Effective delay-controlled load distribution over multipath networks. Parallel and Distributed Systems, IEEE Transactions on, 2011, 22.10: 1730-1741.

[38] DigitalOcean: Simple Cloud Infrastructure for Developers, Available:

https://www.digitalocean.com/