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題名 以任務分配解決即時金融服務中突發流量及網路不穩定問題
Task Assignment for Real-time Financial Service System under Bursty Traffic and Unstable Networks作者 陳泰銘
Chen, Tai Ming貢獻者 蔡子傑
Tsai, Tzu Chieh
陳泰銘
Chen, Tai Ming關鍵詞 任務分配
負載平衡
突發流量
網路不穩定
網路延遲自相關模型
排隊理論
金融3.0
Task Assignment
Load Balance
Bursty Traffic
Network Instability
Network Delay Autocorrelation Model
Queuing Theory
Bank 3.0日期 2016 上傳時間 1-Jul-2016 15:17:13 (UTC+8) 摘要 最近，金融科技(FinTech)和行動金融服務，吸引越來越多的目光。新的創新金融科技服務，改變了金融服務的消費行為。行動網路的發展使人們能夠隨時隨地的享受行動銀行的服務已經是個不爭的事實。然而，由於無線網路先天的特性以及行動裝置的移動性，使行動金融的服務品質受到網路不穩定的影響。而且，隨著Bank 3.0時代的來臨，將會有大量的使用者同時使用行動金融服務，特別是在股市開盤以及重大訊息揭露的時候。因著大量使用者瞬間湧入，以及無線網路不穩定的影響，交易系統的效能很可能會時好時壞，所以無法滿足即時金融市場的需求。 本論文中，我們提出「行動銀行訊息即服務」的框架，使系統能夠很容易的水平擴充，並且能夠輕易的實現雙向通訊和雙向交易等多項行動金融服務。為了達到最少成本追求最大利益的目的，我們發展了能夠適應突發流量以及網路不穩定性的任務分配演算法，使得不用增加額外硬體成本的前提下改善系統效能。然後，為了實驗欲模擬大量行動裝置的使用者，我們觀察真實網路的特性並發明了網路延遲自相關模型來驗證我們提出的任務分配演算法。結果顯示，透過此任務分配演算法，確實能夠有效改善系統資源管理的能力。最後，本研究將系統佈署於真實網路環境當中，並且發現進行同樣實驗的結果與採用網路延遲自相關模型的實驗結果一致。因此，本研究間接驗證了網路自相關模型的正確性，以及證明本任務分配演算法，在突發流量和網路不穩定的即時行動金融服務環境下，能有效降低系統響應時間。
FinTech (financial technology) and mobile financial services are getting more and more attention recently. New innovative FinTech services change the consumption behavior for financial services. It is an indisputable fact development of the mobile Internet allows people to enjoy mobile banking everywhere and anytime. However, due to the nature of wireless networking and the mobility of the mobile device, the quality of mobile financial service will be affected by network instability. Moreover, with the coming of Bank 3.0, a huge amount of users would be in the mobile service of finance simultaneously, especially when the instances of the stock market opening or disclosure of highly important financial message. As the result of bursty traffic and network instability, the performance of transaction system is up and down, making it tough to satisfy the demand of real-time financial markets. In this thesis, we propose a “Mobile Banking Messaging as a Service Framework” that can easily scale out and fulfill functions comprising Bilateral Communication, Bilateral Trading, and many other mobile financial services. To pursuit of the greatest benefit along with investment of the least resources, we develop the task assignment algorithm which can adapt the system to bursty traffic and unstable networks to improve performance for free. Then, in order to simulate a large number of mobile users, we observe the characteristic of real-world network delay and propose a network delay autocorrelation model to verify our task assignment algorithm. The results of experiment show that we could actually use our task assignment algorithm to improve the ability of the system to manage resource. Finally, we deploy our system in a real-world network delay environment and find that the results obtained in the real condition are the same with our simulation results. Therefore, this research can indirectly verify the correctness of the network delay autocorrelation model, and prove that our task assignment algorithm can effectively reduce the system response time for real-time mobile financial service system under bursty traffic and unstable networks.參考文獻 [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.[8] 股票即時交易倒數計時，新制引爆市場洗牌：證券業生存戰, Available: http://news.pchome.com.tw/magazine/print/li/iThome/9908/137753280084570075005.htm[9] 臺灣證券交易所, 縮短撮合秒數時程規劃專區, Available: http://www.twse.com.tw/ch/trading/information/information2.php[10] 臺灣證券業啟動IT軍備競賽，證交所領軍打造逐筆撮合平臺, Available: http://www.ithome.com.tw/people/96276[11] Google Cloud Messaging, Available: https://developers.google.com/cloud-messaging/gcm[12] Apple Push Notification Service, Available: https://developer.apple.com/library/ios/documentation/NetworkingInternet/Conceptual/RemoteNotificationsPG/Chapters/ApplePushService.html[13] Parse, Available: https://parse.com/products/push[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.[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.[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/ 描述 碩士
國立政治大學
資訊科學學系
102753032資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102753032 資料類型 thesis dc.contributor.advisor 蔡子傑 zh_TW dc.contributor.advisor Tsai, Tzu Chieh en_US dc.contributor.author (Authors) 陳泰銘 zh_TW dc.contributor.author (Authors) Chen, Tai Ming en_US dc.creator (作者) 陳泰銘 zh_TW dc.creator (作者) Chen, Tai Ming en_US dc.date (日期) 2016 en_US dc.date.accessioned 1-Jul-2016 15:17:13 (UTC+8) - dc.date.available 1-Jul-2016 15:17:13 (UTC+8) - dc.date.issued (上傳時間) 1-Jul-2016 15:17:13 (UTC+8) - dc.identifier (Other Identifiers) G0102753032 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/98623 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學學系 zh_TW dc.description (描述) 102753032 zh_TW dc.description.abstract (摘要) 最近，金融科技(FinTech)和行動金融服務，吸引越來越多的目光。新的創新金融科技服務，改變了金融服務的消費行為。行動網路的發展使人們能夠隨時隨地的享受行動銀行的服務已經是個不爭的事實。然而，由於無線網路先天的特性以及行動裝置的移動性，使行動金融的服務品質受到網路不穩定的影響。而且，隨著Bank 3.0時代的來臨，將會有大量的使用者同時使用行動金融服務，特別是在股市開盤以及重大訊息揭露的時候。因著大量使用者瞬間湧入，以及無線網路不穩定的影響，交易系統的效能很可能會時好時壞，所以無法滿足即時金融市場的需求。 本論文中，我們提出「行動銀行訊息即服務」的框架，使系統能夠很容易的水平擴充，並且能夠輕易的實現雙向通訊和雙向交易等多項行動金融服務。為了達到最少成本追求最大利益的目的，我們發展了能夠適應突發流量以及網路不穩定性的任務分配演算法，使得不用增加額外硬體成本的前提下改善系統效能。然後，為了實驗欲模擬大量行動裝置的使用者，我們觀察真實網路的特性並發明了網路延遲自相關模型來驗證我們提出的任務分配演算法。結果顯示，透過此任務分配演算法，確實能夠有效改善系統資源管理的能力。最後，本研究將系統佈署於真實網路環境當中，並且發現進行同樣實驗的結果與採用網路延遲自相關模型的實驗結果一致。因此，本研究間接驗證了網路自相關模型的正確性，以及證明本任務分配演算法，在突發流量和網路不穩定的即時行動金融服務環境下，能有效降低系統響應時間。 zh_TW dc.description.abstract (摘要) FinTech (financial technology) and mobile financial services are getting more and more attention recently. New innovative FinTech services change the consumption behavior for financial services. It is an indisputable fact development of the mobile Internet allows people to enjoy mobile banking everywhere and anytime. However, due to the nature of wireless networking and the mobility of the mobile device, the quality of mobile financial service will be affected by network instability. Moreover, with the coming of Bank 3.0, a huge amount of users would be in the mobile service of finance simultaneously, especially when the instances of the stock market opening or disclosure of highly important financial message. As the result of bursty traffic and network instability, the performance of transaction system is up and down, making it tough to satisfy the demand of real-time financial markets. In this thesis, we propose a “Mobile Banking Messaging as a Service Framework” that can easily scale out and fulfill functions comprising Bilateral Communication, Bilateral Trading, and many other mobile financial services. To pursuit of the greatest benefit along with investment of the least resources, we develop the task assignment algorithm which can adapt the system to bursty traffic and unstable networks to improve performance for free. Then, in order to simulate a large number of mobile users, we observe the characteristic of real-world network delay and propose a network delay autocorrelation model to verify our task assignment algorithm. The results of experiment show that we could actually use our task assignment algorithm to improve the ability of the system to manage resource. Finally, we deploy our system in a real-world network delay environment and find that the results obtained in the real condition are the same with our simulation results. Therefore, this research can indirectly verify the correctness of the network delay autocorrelation model, and prove that our task assignment algorithm can effectively reduce the system response time for real-time mobile financial service system under bursty traffic and unstable networks. en_US dc.description.tableofcontents CHAPTER 1 Introduction 11.1 Background 11.2 Motivation 41.3 Problem Definition and Our Goal 41.3.1 Load Balance 61.3.2 Bursty traffic 71.3.3 Network Instability 71.4 Organization 8CHAPTER 2 Related Work 92.1 Risk Diversified Schemes 92.1.1 Round Robin Based Schemes 102.1.2 Opportunity Based Schemes 102.2 Least-Number Based Schemes 112.2.1 Least Connection First (LCF) 112.2.2 Shortest Queue First (SQF) 112.3 Job-Size Based Schemes 122.3.1 Shortest Job First 122.3.2 Min-Min 122.3.3 Max-Min 122.4 Consumption Fast First Strategies 132.4.1 Flow Control Principle 132.4.2 M/M/1 132.5 Summary of Related Work 14CHAPTER 3 Mobile Banking Messaging as a Service Framework 153.3 Mobile Banking Messaging as a Service Framework (MBMaaS) 15CHAPTER 4 Network Delay Autocorrelation Model 174.1 Hypothesis 174.2 Autocorrelation 184.3 Network Delay Autocorrelation Model 194.4 Result of the Network Delay Autocorrelation Model 234.4 Autocorrelation Coefficient 25CHAPTER 5 Research Method 265.1 Pre-study 265.2 Method Applied in the Research 285.2.1 See the Future Network Delay (SeeFuND) Task Assignment Algorithm 315.2.2 Incubation Period Phenomenon 325.2.3 Foresight-SeeFuND Task Assignment Algorithm 33CHAPTER 6 Simulation and Emulation 346.1 Simulation Environment Setup 346.1.1 Response Time with Different Network Correlation 356.1.2 Response Time with Different Loading 386.1.3 Response Time with Bursty Traffic 416.2 Emulation Environment Setup 436.2.1 Effectiveness Evaluation 44CHAPTER 7 Conclusions and Future Work 46REFERENCE 48 zh_TW dc.format.extent 3128350 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102753032 en_US dc.subject (關鍵詞) 任務分配 zh_TW dc.subject (關鍵詞) 負載平衡 zh_TW dc.subject (關鍵詞) 突發流量 zh_TW dc.subject (關鍵詞) 網路不穩定 zh_TW dc.subject (關鍵詞) 網路延遲自相關模型 zh_TW dc.subject (關鍵詞) 排隊理論 zh_TW dc.subject (關鍵詞) 金融3.0 zh_TW dc.subject (關鍵詞) Task Assignment en_US dc.subject (關鍵詞) Load Balance en_US dc.subject (關鍵詞) Bursty Traffic en_US dc.subject (關鍵詞) Network Instability en_US dc.subject (關鍵詞) Network Delay Autocorrelation Model en_US dc.subject (關鍵詞) Queuing Theory en_US dc.subject (關鍵詞) Bank 3.0 en_US dc.title (題名) 以任務分配解決即時金融服務中突發流量及網路不穩定問題 zh_TW dc.title (題名) Task Assignment for Real-time Financial Service System under Bursty Traffic and Unstable Networks en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [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.[8] 股票即時交易倒數計時，新制引爆市場洗牌：證券業生存戰, Available: http://news.pchome.com.tw/magazine/print/li/iThome/9908/137753280084570075005.htm[9] 臺灣證券交易所, 縮短撮合秒數時程規劃專區, Available: http://www.twse.com.tw/ch/trading/information/information2.php[10] 臺灣證券業啟動IT軍備競賽，證交所領軍打造逐筆撮合平臺, Available: http://www.ithome.com.tw/people/96276[11] Google Cloud Messaging, Available: https://developers.google.com/cloud-messaging/gcm[12] Apple Push Notification Service, Available: https://developer.apple.com/library/ios/documentation/NetworkingInternet/Conceptual/RemoteNotificationsPG/Chapters/ApplePushService.html[13] Parse, Available: https://parse.com/products/push[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.[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.[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/ zh_TW
