本文選題：移動云　切入點：虛擬機遷移　出處：《哈爾濱工業(yè)大學(xué)》2014年碩士論文

【摘要】：隨著智能手機逐漸替代傳統(tǒng)的手機，移動設(shè)備的計算性能也變得越來越強大了，同樣越來越多的傳感器也安裝在這些設(shè)備上來滿足用戶需求，增強用戶體驗，但是隨之而來的能量緊缺問題也逐漸受到人們重視，對于移動設(shè)備而言，電池難以為繼使得用戶不愿意使用其強大的功能，即使功能強大也無異于形同虛設(shè)。與此同時云計算作為一種可購買的計算力資源也逐漸的商用化，公有云和私有云不斷的發(fā)展壯大，隨著各地數(shù)據(jù)中心的建立，云計算的普及，使用云計算的方式解決移動設(shè)備的能量消耗問題，，增強移動設(shè)備的運算速度的想法也逐一得到落實，由于使用云服務(wù)計算因此可以為移動設(shè)備節(jié)省大量的存儲資源和計算資源，更重要的是可以節(jié)約能量來做更多的事情，例如傳感器數(shù)據(jù)的采集等功能。目前國內(nèi)外的相關(guān)工作主要集中于建立系統(tǒng)，通過離線的應(yīng)用程序分析測量的方法進行調(diào)度計算，進而在移動設(shè)備上對相應(yīng)的應(yīng)用程序進行分割遷移，但是這種預(yù)處理工作難以對所有程序處理，并且對于能量測量而言開發(fā)者難以處理。 本文首先提出了一種在線的程序分析方法，在移動設(shè)備上運行該程序的同時即可獲得一顆反映當前應(yīng)用程序結(jié)構(gòu)的函數(shù)調(diào)用樹，并且在生成過程中對調(diào)用的各項參數(shù)進行統(tǒng)計。之后再函數(shù)調(diào)用樹之上提出以最大化節(jié)約運行時間的問題，并且分析該問題不是一個NP完全問題，之后給出一種在函數(shù)調(diào)用樹上的動態(tài)規(guī)劃算法，用來求解該問題，并且證明該算法的正確性。接著針對于目前國內(nèi)外系統(tǒng)中默認程序代碼預(yù)同步到云服務(wù)器端的想法進行分析，提出了一種將程序進行分割，僅將需要遷移到服務(wù)器端執(zhí)行的代碼和數(shù)據(jù)發(fā)送到服務(wù)器端的，按需求的程序遷移的想法，進而節(jié)約網(wǎng)絡(luò)占用和服務(wù)器磁盤空間。 最后基于Android系統(tǒng)之上實現(xiàn)了在線函數(shù)調(diào)用樹的生成，在線的遷移函數(shù)集合的計算以及按需求的程序遷移功能。首先設(shè)計了一組實驗對移動設(shè)備和服務(wù)器的CPU速度進行測量，之后對于三種不同的應(yīng)用環(huán)境下設(shè)計了三組不同的實驗，包括矩陣乘法，篩法求素數(shù)，文件處理來測試在線函數(shù)調(diào)用樹的生成和遷移函數(shù)集合的計算功能。通過遷移執(zhí)行得到其執(zhí)行速度分別提高7.11倍，23.23倍，8.24倍。最后對按需求的代碼遷移進行實驗，實驗結(jié)果顯示按需求的遷移節(jié)約網(wǎng)絡(luò)流量77.1%，73.5%，76.2%，節(jié)約磁盤空間81.9%，77.8%，80.1%。
[Abstract]:As smartphones gradually replace traditional mobile phones, the computing performance of mobile devices becomes more and more powerful, and more and more sensors are installed on these devices to meet users' needs and enhance the user experience. However, the problem of energy shortage has been paid more and more attention to. For mobile devices, battery is difficult to sustain and users are unwilling to use its powerful function. At the same time, cloud computing as a kind of purchasing computing power resources is gradually commercialized, public cloud and private cloud continue to grow, with the establishment of local data centers, cloud computing is becoming more and more popular. Using cloud computing to solve the energy consumption problem of mobile devices, the idea of increasing the computing speed of mobile devices has been implemented one by one. Because of the cloud service computing, it can save a lot of storage and computing resources for mobile devices. More importantly, it can save energy to do more things, such as sensor data acquisition and other functions. At present, the related work at home and abroad is mainly focused on the establishment of the system, through offline application program analysis and measurement method for scheduling calculation, Then the corresponding applications are partitioned and migrated on mobile devices, but this kind of preprocessing is difficult to deal with all programs, and it is difficult for developers to deal with energy measurement. In this paper, an online program analysis method is proposed. When running the program on a mobile device, a function call tree reflecting the structure of the current application can be obtained at the same time. Then the function call tree is put forward to maximize the running time and the problem is analyzed that this problem is not a NP-complete problem. Then a dynamic programming algorithm based on the function call tree is presented to solve the problem and prove the correctness of the algorithm. Then the idea of the default program code synchronization to the cloud server in domestic and foreign systems is analyzed. In this paper, the idea of dividing the program and sending only the code and data that need to be migrated to the server side to send the code and data to the server side according to the requirement is put forward, thus saving the network occupation and the server disk space. Finally, the generation of online function call tree, the calculation of online migration function set and the function of program migration according to requirement are realized on the basis of Android system. Firstly, a set of experiments are designed to measure the CPU speed of mobile device and server. After that, three groups of experiments were designed for three different application environments, including matrix multiplication, screening method to calculate prime number, File processing is used to test the generation of online function call tree and the computing function set. By migration execution, the execution speed is 7.11 times 23.23 times and 8.24 times respectively. Finally, the code migration according to the requirement is experimented. The experimental results show that the migration according to the demand saves the network flow 77.1% and 73.5% 73.2%, saves the disk space 81.9% and 77.8% 80.1%.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.09


本文編號：1678485