臺灣博碩士論文加值系統

在雲端資料中心，由於資源需求量的變動非常大，有效地分配與管理資源，並同時滿足每一個應用程式的服務水準協議是一個非常重要的研究議題。在本論文中，我們提出了一個應用程式感知資源分配的機制（App-RA），預估在軟體定義網路下雲端資料中心每個應用程式所需的資源，從而分配適當數量的虛擬機器（VMs）給每個應用程式。就我們所知，我們所提的應用程式感知資源分配機制（App-RA）是第一個可以適用在各種不同的應用程式之感知資源分配機制，可以讓各個不同的應用程式中滿足不同的服務水準協議、且達到有效的分配資源及省電。本應用程式感知資源分配機制（App-RA）是基於類神經網路去預估未來所需的資源（CPU、記憶體、GPU、硬碟I/O、網路頻寬），並且利用目前時間戳記當作輸入的參數之一，使資源預估變得更為準確。我們為不同類型的應用程式提出兩個分配虛擬機器的演算法，並利用動態調整虛擬機器之分配閾值（VM allocation threshold）來避免違反服務水準協議。除此之外，我們採用基於軟體定義網路OpenFlow網路的CICQ交換器，在網路層針對不同類型的應用程式封包進行妥適排程，最後，模擬結果表示，我們所提的應用程式感知資源分配機制僅比最佳解多了9.21％的耗電，即比起適用於非圖形應用的代表性感知資源分配方法省下了104.58％的耗電。除此之外，我們的機制對不同應用程式的SLA違反率皆低於4％。

In cloud datacenters, since resource requirements change frequently, how to assign and manage resources efficiently while meeting service level agreements (SLAs) of different types of applications is an important research issue. In this paper, we propose an Application-aware Resource Allocation (App-RA) scheme to predict resource requirements and allocate the appropriate number of virtual machines (VMs) for each application in SDN-based cloud datacenters. To the best of our knowledge, the proposed App-RA is the first application-aware resource allocation scheme that adapts to all types of applications. The App-RA can meet SLAs, allocate resources efficiently, and reduce power consumption for each application in cloud datacenters. The proposed App-RA adopts the neural network based predictor to forecast the requirements of resources (CPU, Memory, GPU, Disk I/O and bandwidth) for an application. In the proposed App-RA, we have designed two algorithms which allocate appropriate numbers of virtual machines and use the VM allocation threshold to avoid SLA violations for five different types of applications.

In addition, we adopt an SDN-based OpenFlow network with CICQ switches to appropriately schedule packets for different types of application in the network layer. Finally, simulation results show that the power consumption of the proposed App-RA is only 9.21% higher than that of the best case (oracle) and the power consumption of App-RA is 104.58% better than that of EAACVA, which is a representative resource allocation method for non-graphic applications. Furthermore, the SLA violation rate of the proposed App-RA is less than 4% for all applications.

Abstract (in Chinese).............i
Abstract..........................iii
Contents..........................vi
List of Figures...................viii
List of Tables....................ix
Chapter 1 Introduction............1
Chapter 2 Related Work............3
2.1 Resource allocation architecture....3
2.1.1 Server-aware resource allocation schemes....4
2.1.2 Application-aware resource allocation schemes....4
2.2 SDN-based datacenter network....7
Chapter 3 Application-aware Resource Allocation for SDN-based Cloud Datacenters.........8
3.1 Application-aware resource prediction....8
3.2 Application-aware resource allocation....10
3.3 Proposed SDN-based datacenter network design....14
Chapter 4 Evaluation and Discussion....17
4.1 Simulation environment.....17
4.2 Comparison of different resource allocation schemes....19
4.3 Comparison of power consumption....25
4.4 Comparison of the proposed App-RA with different mechanisms.....................27
Chapter 5 Conclusion...........28
5.1 Concluding remarks.........28
5.2 Future work................29
References.....................29

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