由於物聯網、人工智慧、巨量資料以及雲端運算的快速發展,促 使了新興的應用出現,像是自駕車、擴增實境、智慧監控等等。 這 些應用有著嚴格的延遲限制,需要大量的運算資源,以及不同的資源 需求,像是感測、運算、網路、地點等等需求, 因此,開發者開始 試著將各種不同的設備進行整合。這些設備可能來自雲、基地台、 網路設備、以及各種終端設施, 而像這樣從雲到終端設施的整合平 台,我們將他稱之為雲到物之連續性平台(cloud-to-things continuum platform)。 這樣的平台包含了兩個角色,一個是提供者一個是使用 者,提供者負責建立平台並且提供應用給予使用者購買與使用, 在這 篇論文裡面,我們根據這兩個角色設計了兩個聰明的部件,分別是全 域優化部件(global optimizer)以及應用優化部件(application-specific optimizer)。全域優化部件是設計給提供者的,用來優化整個平台, 這個部件考慮了使用者的服務品質需求、平台可用的資源量、應用所 要的感測器等條件進行優化, 進而最大化整個平台能夠服務的使用 者數量。而應用優化部件則是設計給使用者的,來優化正在運行的應 用。 為了實現這兩個優化部件,我們解決了三個資源分配的問題,分 別是應用佈建問題(application deployment problem)、對延遲敏感之 應用優化問題(delay-sensitive application optimization problem)、以及 對延遲不敏感之應用優化問題(delay-insensitive application optimization problem)。 對於應用佈建問題,我們設計了一套演算法,他可以在多 項式時間(polynomial time)內解決問題,並且有一數學證明其近似 指標(approximation factor)。對於延遲敏感之應用優化問題,我們的 演算法能在多項式時間解出最佳解並有數學證明佐證其最佳性。對於 延遲不敏感之應用優化問題, 我們則有一動態規劃算法解出最佳解, 並有另ㄧ高效率啟發式算法來快速解決問題,雖然啟發式算法沒有數 學佐證, 但與現存最新的其他算法比較可以有最少20%的優化。提供 這樣雲到物的連續性、複雜且大規模的平台顯然是未來的趨勢, 但實 現這樣的平台是昂貴又困難的,這也正是這篇論文的價值所在。有了 我們的算法, 提供者可以更好地去預測可能需要的花費以及制定價 格,而使用者們也因為有了針對應用優化的演算法們, 可以獲得更好 的服務。除此之外我們也利用智慧路燈建立了一真實平台, 這樣的一個真實的平台未來除了對學術研究以及業界測試有幫助,能夠進行更 多更真實的實驗以外, 更重要的是能夠加速這樣一個複雜的雲到物的 連續性平台開發。

The rapid growth of Internet-of-Things (IoT), Artificial Intelligence (AI), big data, and cloud computing allow developments of next-generation applications, such as self-driving cars, wearable Augmented Reality (AR), and intelligent surveillance cameras. These applications have strict delay, large computing power, and diverse resources requirements, which make developers start to integrate geographically scattered resources. The resources have diverse types, including computation, networks, storage, and sensing. They are gathered from the cloud data centers to the end devices, which is referred to as cloud-to-things continuum. In this thesis, we propose an intelligent framework for the cloud-to-things continuum platform. The platform consists of two actors: a provider and users. The provider is responsible for building the platform and preparing applications, which are requested by the users. According to different actors in the platform, we design two intelligent components: global optimizer and application specific optimizer. They are designed for the platform's provider and users, respectively. The global optimizer is used to optimize the platform while considering different factors, such as users' QoS requirements, platform's available resources, and applications' sensor requirements. The application specific optimizer is designed for ongoing applications running on our platform to adapt to system dynamics. In order to realize the optimizers, we solve three resource allocation problems, including application deployment problem, delay-sensitive application optimization problem, and delay-insensitive optimization problem on top of the cloud-to-things continuum platforms. For the application deployment problem, our solution runs in polynomial time with a mathematically proved approximation factor. For the delay-sensitive optimization problem, our solutions run in polynomial time and result in optimal solutions. For the delay-insensitive problem, our dynamic programming based solution results in optimal results under delay-tolerable environment and our efficient solution outperforms state-of-the-art algorithms by at least 20%. Providing such complicated and large-scale cloud-to-things continuum platform is a clear trend, but it is expensive and challenging. With our optimization algorithms, the provider can readily estimate costs and decide prices with the given approximation factor, and the users can expect an optimal user experience with our application specific optimizers. In addition, we are building a real testbed by installing various resources on novel smart poles. The testbed allows researchers from academia and industries to test, develop, and evaluate latest technologies and algorithms of cloud-to-things continuum platforms.

Acknowledgments i 致謝 ii 中文摘要 iii Abstract v
1 Introduction 1
1.1 Contributions ................................ 4
1.2 Organization................................. 4
2 Background 5
2.1 CloudComputing.............................. 5
2.2 WirelessSensorNetworksandInternetofThings . . . . . . . . . . . . . 8
2.3 FogComputing ............................... 10
2.4 Timeline................................... 14
3 Cloud-to-Things Continuum Framework 15
3.1 PotentialandLimitationsofExistingFramework . . . . . . . . . . . . . 15
3.2 ProposedIntelligentFramework ...................... 16
3.3 KeyResourceAllocationProblems..................... 17
4 Global Optimization 19
4.1 RelatedWork ................................ 20
4.1.1 DistributedDeploymentProblems................. 20
4.1.2 Application (Analytics) Deployment Problems . . . . . . . . . . 21
4.2 SystemOverview .............................. 23
4.3 Application(Analytics)DeploymentProblem . . . . . . . . . . . . . . . 25
4.3.1 Problem-1: Comprehensive Formulation . . . . . . . . . . . . . . 25
4.3.2 Problem-2:TractableFormulation................. 30
4.4 TestbedandExperiments .......................... 35
4.4.1 TestbedImplementations...................... 35
4.4.2 SystemModelDerivation ..................... 38
4.4.3 Validation.............................. 39
4.5 Simulations ................................. 41
4.5.1 Setup ................................ 41
4.5.2 Results ............................... 44
4.6 Discussion.................................. 48

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